Welcome to City Energy Analyst¶

City Energy Analyst (CEA) is an open-source software for the analysis of energy systems in cities. CEA helps you to analyse the effects of building retrofits, land-use planning, district heating and cooling and renewable energy on the future costs, emissions and energy consumption of neighbourhoods and districts. In Addition CEA helps you to find the optimal location, size and operation of energy generation and distribution technologies for a neighbourhood or a district of your choice.

For the moment, CEA supports neighbourhoods and districts in Temperate (e.g., Switzerland) and Tropical climates (e.g., Singapore).

Learn more..

Visit www.cityenergyanalyst.com for more information on the CEA.

Installation¶

CEA can be installed in Windows, Mac OS, Ubuntu and on the computer cluster of the ETH Zurich Euler. The latter is only available for students and faculty of the ETH Zurich.

Installation guide for Windows¶

Follow these instructions to install the CityEnergyAnalyst (CEA) on a Windows system (tested with Windows 10).

Download the latest version of CEA in here.
Open the installer and follow the instructions

Note

For installing the development version of CEA, tick the box “development version” during the installation.

Note

For previous releases check here.

Note

To install from the source check Installation guide for Windows (from the source)

Interfaces¶

There are different ways in which you can interact with the code of CEA.

The command line interface: This is the command line to all the commands of CEA from your computer terminal
The dashboard: This a web-based interface to CEA, open source and developed by the CEA team.
The pycharm interface: this interface provides access to all the source code of CEA.

The command line interface and dashboard interface are included during the installation of CEA. Other interfaces require a few additional steps to get them up and running.

Pycharm¶

In order to access and work on the source code of CEA from pycharm do:

Make sure to have installed the development version of CEA (see step 2 of the installation guide).
Download and install Github Desktop (64-bit).
Download and install Pycharm Community edition (64-bit)
Open PyCharm from the start menu and open project CityEnergyAnalyst (default location is C:\Users\<you>\Documents\CityEnergyAnalyst\CityEnergyAnalyst).
Open File>Settings>Project:CityEnergyAnalyst>Project Interpreter>Project Interpreter.
Click on the settings button (it looks like a wheel) next to the current interpreter path, and click Add.
Click System Interpreter from the left hand list and select existing environment.
Point to C:\Users\<you>\Documents\CityEnergyAnalyst\Dependencies\Python\python.exe
Click apply changes.

Attention

We ended Support of Grashopper on 20.03.20. The legacy code can be found in our github repositry/legacy

Attention

We ended Support of ArcGIS on 15.04.19. The legacy code can be found in our github repository/legacy

Installation guide for the Euler cluster¶

Disclaimer: for this to work, you must be an ETH Zurich affiliated person and own a nethz-account.

EULER stands for Erweiterbarer, Umweltfreundlicher, Leistungsfähiger ETH-Rechner. It is a high performance cluster available to users affiliated to the ETH Zurich. See more information about the computing cluster on the clusterwiki.

This section describes the steps necessary to get the CEA running on the Euler cluster.

Logging on to the Euler cluster¶

Estimated time: 1 hr

You can login to the Euler cluster via the SSH protocol. If you use Linux or Mac OS X, then you can directly use SSH from within a shell as it is part of the operating system. If you are on Windows, you will need an ssh client. The CEA Console includes the ssh command, otherwise, install a third-party application in order to use SSH (Putty, Cygwin, Git for Windows).

You can only log in to Euler from within the ETH network or when connected via VPN.

Once in the terminal in Linux or Mac OS X or in a terminal of thrid-party application of your choicse, do:

ssh <your nethz-name>@euler.ethz.ch

After entering the above command in the shell, you will be asked for a password. Enter your nethz password. You are then greeted with the Euler welcome message.

Detailed steps are described in the Euler wiki of the Scientific Computing Service in ETHZ. For Windows users, it is recomendded to download WinSCP and MobaXterm. Please follow the steps in the wiki carefully, and consult the cluster support when Troubleshooting section (2.9) is not enough to solve your problwm.

Build a CEA Singularity container¶

Estimated time: 20 mins

You need to build a Singularity container via a cea docker image. The latest docker image of cea is published here. Please login to Euler and conduct the following steps.

Request a compute node with Singularity

$ bsub -n 1 -R singularity -R light -Is bash

Load eth_proxy to connect to the internet from compute nodes

$ module load eth_proxy

Go to the scratch folder

$ cd $SCRATCH

Build a Singularity container based on the cea docker image

$ singularity pull docker://cityenergyanalyst/cea

Check if Singularity has been built

$ ls

You should find the CEA Singularity container, cea_latest.sif, in the list of files. Congratulations! You can start running CEA on Euler!

If wish to run cea test to test the CEA Singularity container:

$ SINGULARITY_HOME=/projects singularity shell -B $SCRATCH cea_latest.sif
Singularity> source /venv/bin/activate
(venv) Singularity> cea test

Running the CEA¶

You need to run the CEA scripts with their command line interface (CLI). Be sure to learn how to use the job system on Euler, as the login nodes are not intended for running simulations. See clusterwiki.

Upload your CEA projects to /cluster/scratch/nethz-username.
Upload a workflow.yml to /cluster/scratch/nethz-username.
Open workflow.yml, point the project path to /cluster/scratch/ethz-username/PATH_TO_PROJECT (be aware of the linux path format).
In the same workflow.yml, specify the steps you wish to simulate. Please refer to this blog post on how to edit workflow.yml.
Submit a batch job following this example command:

$ bsub -n 1 -R "span[host=1]" -R singularity -R "rusage[mem=2048,scratch=2048]" -W 1:00 "SINGULARITY_HOME=/projects singularity run -B \$TMPDIR:/tmp -B $SCRATCH cea_latest.sif cea workflow --workflow /cluster/scratch/nethz-username/workflow.yml"

Other Commands¶

Before building a new singularity container, it is suggested to clean up the folders first.

To remove a singularity container (e.g., a container named cea_latest.sif that is in $SCRATCH)

$ cd $SCRATCH
$ rm cea_latest.sif

To clean up cache files

$ singularity cache clean

Installation guide for Mac OS¶

Working with the CityEnergyAnalyst (CEA) on a Mac OS system is a little bit messier than on Windows and requires using the Terminal to launch CEA. But don’t worry - it works!

There are two installation methods, you can either: (1) use the CEA source code from GitHub, or (2) use the CEA Docker image. The former option gives you full access to CEA but is significantly more cumbersome to install as it involves running a lot of commands on Terminal. The latter option is much easier to install but working in a virtual container on Docker takes some getting used to. Both options are perfectly suited for users, but only the full installation is suitable for developers.

Choose the one that suits your needs!

(1) Use the CEA source code from GitHub¶

If you would like to develop CEA, this will be your method. Follow these instructions to install the CityEnergyAnalyst (CEA) on a Mac system (tested with macOS Mojave 10.14.6) from the source

Attention

We advise to follow the guide precisely:

Be sure to USE conda env create NOT conda create familiar to experienced conda users. This command not only creates an environment, but also reads the environment.yml file, containing a list of packages (and versions) to install, as well as a definition of the channels to check.
If you need to create a conda environment for the CEA that has a specific name (the default is cea) then use the name parameter: conda env create --name your-env-name-here
CONDA_SUBDIR=osx-64 will be used when creating the CEA conda environment to support both Intel and Apple Silicon Macs (this will be removed once all dependencies have been ported to ARM). Instructions below will indicate where it is being used.
(Only applicable for Apple Silicon Macs) If at any point during installation or running CEA and it prompts you to install Rosetta, click install.
This guide SHOULD work for both Intel and Apple Silicon Macs. If you encounter any issues when installing on either, help us report it as an issue on our GitHub page.

Prerequisites¶

Download and install Homebrew.
(Optional) Download and install Github Desktop (64-bit). Only required if you are unfamiliar with using Terminal and/or git commands.
(Optional) Install Mamba. This will help to speed up the creation of the CEA conda environment.

Installation of the code base¶

Excluding the above software, CEA installation requires approximately 13 GB of storage (depending on your existing Python library) and 1 hour of your time.

Note

(Experimental) We have a script that can automate the process below. Just open a Terminal console and enter /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/architecture-building-systems/CityEnergyAnalyst/update-mac-installation/install/mac_installation.sh)" Continue on the next section to find out how to interact with CEA.

Open GitHub Desktop from the start menu.
Clone the CEA repository:
1. Press Cmd+Shift+O (clone repository) and select the URL tab.
2. Paste the CEA GitHub address: https://github.com/architecture-building-systems/CityEnergyAnalyst
3. Click Clone, this will take ~ 5-10 minutes (Size 1.65 GB).
Clone the CEA GUI repository:
1. Press Cmd+Shift+O (clone repository) and select the URL tab.
2. Paste the CEA GUI GitHub address: https://github.com/architecture-building-systems/CityEnergyAnalyst-GUI
3. Click Clone, this will take ~ 5 minutes (Size 600MB).
Install CEA:
1. Open a Terminal console (you can find it in your Mac’s Applications folder).
2. Type cd Documents/GitHub/CityEnergyAnalyst and press ENTER.
3. Type CONDA_SUBDIR=osx-64 conda env create --name cea and press ENTER. (If mamba is installed, replace conda in command to mamba. i.e. CONDA_SUBDIR=osx-64 mamba env create --name cea.)
4. Grab a cup of tea and some toast, this will take ~45 minutes.
5. Type conda activate cea and press ENTER.
6. Type pip install -e . and press ENTER (mind the dot ‘.’ included in this command!).
Build the CEA dashboard GUI:
1. Type cd .. and press ENTER, then type cd CityEnergyAnalyst-GUI and press ENTER.
2. Install Yarn by typing brew install yarn and press ENTER.
3. Type yarn and press ENTER.
4. Type yarn package and press ENTER.
5. You will find the CEA application in the folder /Users/your_name/Documents/GitHub/CityEnergyAnalyst-GUI/out/CityEnergyAnalyst-GUI-darwin-*
Running CEA:
- You can run CEA a few different ways (see Mac Interfaces below).
- If you are familiar with running CEA on a Windows computer, please note that there are a few additional steps when running the dashboard on a Mac!

Attention

In order to run CEA on Mac, you will need to select the correct Daysim binaries:

If you are running the Building Solar radiation tool using the dashboard, make sure the parameter daysim-bin-directory (under Advanced) points to the correct Daysim binary folder (by default, this should be /Users/your_name/Documents/GitHub/CityEnergyAnalyst/setup/Dependencies/Daysim/mac).
If you are using the command line interface or Pycharm, you will need to modify the same parameter (i.e., config.radiation.daysim_bin_directory) in the config file (usually located in /Users/your_name/cea.config, where your_name represents your user name on your Mac).

Mac Interfaces¶

There are different ways in which you can interact with the code of CEA.

The command line interface: This is the command line to all the commands of CEA from your computer terminal
The dashboard: This a web-based interface to CEA, open source and developed by the CEA team.
The Pycharm interface: this interface provides access to all the source code of CEA.

Command line interface¶

You can run the command line interface by on the Terminal by running the command conda activate cea.

That’s it! You can run the CEA command interface normally.

Dashboard (GUI)¶

In order to run the dashboard, you will need to do the following each time you want to start the dashboard:

Open the Terminal (you can find it in your Mac’s Applications folder) and run the following command depending on your installation type:
Type conda activate cea and press ENTER, then type cea dashboard and press ENTER.
Wait for start socketio.run to appear in the Terminal before proceeding to the next step. (This might take a while if you are running this for the first time, around 3-5 min)
Run the CEA dashboard application you created in the last step of the installation above (/Users/your_name/Documents/GitHub/CityEnergyAnalyst-GUI/out/CityEnergyAnalyst-GUI-darwin-*).

You can now run the CEA dashboard interface normally!

Here you can find a series of blog posts to help you get started!

Pycharm¶

The Pycharm interface can be helpful if you would like to contribute to CEA, but it requires a few steps to get it up and running. In order to access and work on the source code of CEA from pycharm do:

Download and install Pycharm Community edition (64-bit) OR your own favorite editor.
Open PyCharm from the start menu and open project CityEnergyAnalyst (stored where you downloaded CityEnergyAnalyst).
Open File>Settings>Project:CityEnergyAnalyst>Project Interpreter>Project Interpreter.
Click on the settings button (it looks like a wheel) next to the current interpreter path, and click Add.
Click Conda Environment from the left hand list and select existing environment.
Point to the location of your conda environment. It should look something like /Users/your_name/Miniconda2/envs/cea/python.exe or /Users/your_name/AppData/Local/conda/conda/envs/cea/python.exe where your_name represents your user name on your Mac.
Click apply changes.

(2) Use the CEA docker image¶

If you would like using docker containers, follow these instructions to run CEA on a Mac OS system (tested with Mac OS Catalina). This method is suitable for users, but not developers. For developers, please refer to the second method below.

Install Docker and run CEA:
1. You can find instructions on how to do that here.
2. If you only plan to run CEA from the command line interface, you’re done!
If you would like to use the CEA dashboard, you will need to download and build it manually:
1. Download and install Github Desktop (64-bit).
2. Download and install Miniconda(64-bit) for Python 3.8.
3. Download and install Homebrew.
4. Clone the CEA GUI repository:
  
  Press Cmd+Shift+O (clone repository) and select the URL tab.
  
  Paste the CEA GUI GitHub address: https://github.com/architecture-building-systems/CityEnergyAnalyst-GUI
  
  Click Clone, this will take ~ 5 minutes (Size 600MB).
5. Build the CEA dashboard GUI:
  
  Open a Terminal console (you can find it in your Mac’s Applications folder).
  
  Type cd Documents/GitHub/CityEnergyAnalyst-GUI and press ENTER.
6. Install Yarn by typing brew install yarn and press ENTER.
7. Type yarn and press ENTER.
8. Type yarn package and press ENTER.
9. You will find the CEA application in the folder /Users/your_name/Documents/GitHub/CityEnergyAnalyst-GUI/out/CityEnergyAnalyst-GUI-darwin-*
Running CEA:
- You can run CEA a couple of different ways (see Docker Interfaces below).
- If you are familiar with running CEA on a Windows computer, please note that there are a few additional steps when running the dashboard on a Mac!

Docker Interfaces¶

There are different ways in which you can interact with the code of CEA.

The command line interface: This is the command line to all the commands of CEA from your computer terminal
The dashboard: This a web-based interface to CEA, open source and developed by the CEA team.
The Pycharm interface: this interface provides access to all the source code of CEA.

Command line interface¶

In order to run the command line interface in Docker, you will need to run the following command instead: docker run --name cea_container -v path_to_your_cea_projects:/projects dockeruser/cea cea workflow --workflow /projects/workflow.yml

That’s it! You can run the CEA command interface normally.

Dashboard¶

In order to run the dashboard, you will need to do the following each time you want to start the dashboard:

Open the Terminal (you can find it in your Mac’s Applications folder) and run the following command depending on your installation type:
Type docker run -t -p 5050:5050 -v path_to_your_cea_projects:/projects dockeruser/cea.
Run the CEA dashboard application you created in the last step of the installation above.

You can now run the CEA dashboard normally… well, mostly. You will need to pay attention to a few details, described below.

Since you will not be running CEA directly on your computer, you will need to select a project on your Docker container. So if your project is located, for example, in the directory /Users/username/Documents/CEA_projects/my_project you will need to select /projects/my_project as your project in the CEA Dashboard.

Also, note that your jobs in the dashboard might be listed as “pending” even when they have finished. If you would like to check if your job has finished, you can check the Terminal - it’s still running in the background.

Cite¶

Please cite us like this in your publications:

The CEA team. The City Energy Analyst (Version 3.31.0). Zenodo. https://doi.org/10.5281/zenodo.598221

You can see a list of our own publications here: https://cityenergyanalyst.com/publications

Tutorials¶

Users¶

General tutorials are constantly updated through blogs or videos.

Developers¶

These are a collection of tutorials we consider essential for any new developer of CEA. We suggest to take them in order for a better learning experience.

Legacy¶

Collection of outdated information about past functionality of CEA (use at your own risk)

Known issues¶

CEA uses Github Issues to document new ideas as well as issues and bugs.

The table below contains a number of the most common issues:

Issue #	Regarding	Description
1577	ArcGIS < 10.6	Internet Explorer Script Error: An error has occurred in the script of this page. Do you want to continue running scripts on this page?
1704	Installation	Error during installation via command line: Import Error: no module named arcpy
1708	Fiona/GDAL	Error after installation via command line ImportError: DLL load failed: The specified module could not be found.
1709	daysim_main.py	Error when running daysim_main.py: .wea weather file cannot be found

If you cannot find your issue here, please check the complete list of known issues.

Report a new issue¶

For any problems please post a new issue here.

Please read the How to report a Bug? guide and review the open issues before posting.

We have a turn-over time of a couple of days.

We appreciate your contribution!

Glossary¶

This glossary contains all the written input and output variables used by CEA. These variables are stored in databases, themed by the type of information they contain. There are two main types of databases in CEA: input and output. This glossary is organised through the cea’s inputlocator method, which is used to retrieve the information within each file.

Input¶

Basic input provided by users:

get_site_polygon¶

path: inputs/building-geometry/site.shp

The following file is used by these scripts: zone_helper

Variable	Description
`FID`	Shapefile ID
`geometry`	Shapefile POLYGON

get_street_network¶

path: inputs/networks/streets.shp

The following file is used by these scripts: network_layout, optimization

Variable	Description
`geometry`	Geometry
`Id`	Unique building ID. It must start with a letter.

get_surroundings_geometry¶

path: inputs/building-geometry/surroundings.shp

The following file is used by these scripts: radiation, schedule_maker

Variable	Description
`floors_ag`	Number of floors above ground (incl. ground floor, Minimum one floor is needed)
`geometry`	Shapefile POLYGON
`height_ag`	Height above ground (incl. ground floor, Minimum one floor is needed)
`Name`	Unique building ID. It must start with a letter.
`REFERENCE`	Reference to data (if any)

get_terrain¶

path: inputs/topography/terrain.tif

The following file is used by these scripts: radiation, schedule_maker

Variable	Description
`raster_value`	TODO

get_weather¶

path: databases/weather/Zug-inducity_1990_2010_TMY.epw

The following file is used by these scripts: weather_helper

Variable	Description
`aerosol_opt_thousandths (index = 29)`	TODO
`Albedo (index = 32)`	Albedo
`atmos_Pa (index = 9)`	Atmospheric pressure
`ceiling_hgt_m (index = 25)`	TODO
`datasource (index = 5)`	Source of data
`day (index = 2)`	TODO
`days_last_snow (index = 31)`	Days since last snow
`dewpoint_C (index = 7)`	TODO
`difhorillum_lux (index = 18)`	TODO
`difhorrad_Whm2 (index = 15)`	TODO
`dirnorillum_lux (index = 17)`	TODO
`dirnorrad_Whm2 (index = 14)`	TODO
`drybulb_C (index = 6)`	TODO
`extdirrad_Whm2 (index = 11)`	TODO
`exthorrad_Whm2 (index = 10)`	TODO
`glohorillum_lux (index = 16)`	TODO
`glohorrad_Whm2 (index = 13)`	TODO
`horirsky_Whm2 (index = 12)`	TODO
`hour (index = 3)`	TODO
`liq_precip_depth_mm (index = 33)`	TODO
`liq_precip_rate_Hour (index = 34)`	TODO
`minute (index = 4)`	TODO
`month (index = 1)`	TODO
`opaqskycvr_tenths (index = 23)`	TODO
`precip_wtr_mm (index = 28)`	TODO
`presweathcodes (index = 27)`	TODO
`presweathobs (index = 26)`	TODO
`relhum_percent (index = 8)`	TODO
`snowdepth_cm (index = 30)`	TODO
`totskycvr_tenths (index = 22)`	TODO
`visibility_km (index = 24)`	TODO
`winddir_deg (index = 20)`	TODO
`windspd_ms (index = 21)`	TODO
`year (index = 0)`	TODO
`zenlum_lux (index = 19)`	TODO

get_zone_geometry¶

path: inputs/building-geometry/zone.shp

The following file is used by these scripts: archetypes_mapper, decentralized, demand, emissions, network_layout, optimization, photovoltaic, photovoltaic_thermal, radiation, schedule_maker, sewage_potential, shallow_geothermal_potential, solar_collector, thermal_network

Variable	Description
`floors_ag`	Number of floors above ground (incl. ground floor, minimum one floor is needed)
`floors_bg`	Number of floors below ground (basement)
`geometry`	Shapefile POLYGON
`height_ag`	Height above ground (incl. ground floor, minimum one floor is needed)
`height_bg`	Height below ground (basement)
`Name`	Unique building ID. It must start with a letter.
`REFERENCE`	Reference to data (if any)

Intermediate Input¶

Intermediate input generated by archetypes-mapper and data-initializer:

get_building_air_conditioning¶

path: inputs/building-properties/air_conditioning_systems.dbf

The following file is used by these scripts: demand

Variable	Description
`cool_ends`	End of the cooling season - use 00\|00 when there is none
`cool_starts`	Start of the cooling season - use 00\|00 when there is none
`heat_ends`	End of the heating season - use 00\|00 when there is none
`heat_starts`	Start of the heating season - use 00\|00 when there is none
`Name`	Unique building ID. It must start with a letter.
`type_cs`	Type of cooling HVAC assembly (relates to “code” in HVAC assemblies)
`type_ctrl`	Type of heating and cooling control HVAC assembly (relates to “code” in HVAC assemblies)
`type_dhw`	Type of hot water HVAC assembly (relates to “code” in HVAC assemblies)
`type_hs`	Type of heating HVAC assembly (relates to “code” in HVAC assemblies)
`type_vent`	Type of ventilation HVAC assembly (relates to “code” in HVAC assemblies)

get_building_architecture¶

path: inputs/building-properties/architecture.dbf

The following file is used by these scripts: demand, emissions, radiation, schedule_maker

Variable	Description
`Es`	Fraction of gross floor area with electrical demands.
`Hs_ag`	Fraction of above ground gross floor area air-conditioned.
`Hs_bg`	Fraction of below ground gross floor area air-conditioned.
`Name`	Unique building ID. It must start with a letter.
`Ns`	Fraction of net gross floor area.
`type_base`	Basement floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_cons`	Type of construction assembly (relates to “code” in ENVELOPE assemblies)
`type_floor`	Internal floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_leak`	Tightness level assembly (relates to “code” in ENVELOPE assemblies)
`type_part`	Internal partitions construction assembly (relates to “code” in ENVELOPE assemblies)
`type_roof`	Roof construction assembly (relates to “code” in ENVELOPE assemblies)
`type_shade`	Shading system assembly (relates to “code” in ENVELOPE assemblies)
`type_wall`	External wall construction assembly (relates to “code” in ENVELOPE assemblies)
`type_win`	Window assembly (relates to “code” in ENVELOPE assemblies)
`void_deck`	Number of floors (from the ground up) with an open envelope (default = 0, should be lower than floors_ag.)
`wwr_east`	Window to wall ratio in in facades facing east
`wwr_north`	Window to wall ratio in in facades facing north
`wwr_south`	Window to wall ratio in in facades facing south
`wwr_west`	Window to wall ratio in in facades facing west

get_building_comfort¶

path: inputs/building-properties/indoor_comfort.dbf

The following file is used by these scripts: demand, schedule_maker

Variable	Description
`Name`	Unique building ID. It must start with a letter.
`RH_max_pc`	Upper bound of relative humidity
`RH_min_pc`	Lower_bound of relative humidity
`Tcs_set_C`	Setpoint temperature for cooling system
`Tcs_setb_C`	Setback point of temperature for cooling system
`Ths_set_C`	Setpoint temperature for heating system
`Ths_setb_C`	Setback point of temperature for heating system
`Ve_lsp`	Minimum outdoor air ventilation rate per person for Air Quality

get_building_internal¶

path: inputs/building-properties/internal_loads.dbf

The following file is used by these scripts: demand, schedule_maker

Variable	Description
`Ea_Wm2`	Peak specific electrical load due to computers and devices
`Ed_Wm2`	Peak specific electrical load due to servers/data centres
`El_Wm2`	Peak specific electrical load due to artificial lighting
`Epro_Wm2`	Peak specific electrical load due to industrial processes
`Ev_kWveh`	Peak capacity of electric battery per vehicle
`Name`	Unique building ID. It must start with a letter.
`Occ_m2p`	Occupancy density
`Qcpro_Wm2`	Peak specific process cooling load
`Qcre_Wm2`	Peak specific cooling load due to refrigeration (cooling rooms)
`Qhpro_Wm2`	Peak specific process heating load
`Qs_Wp`	Peak sensible heat load of people
`Vw_ldp`	Peak specific fresh water consumption (includes cold and hot water)
`Vww_ldp`	Peak specific daily hot water consumption
`X_ghp`	Moisture released by occupancy at peak conditions

get_building_supply¶

path: inputs/building-properties/supply_systems.dbf

The following file is used by these scripts: decentralized, demand, emissions, system_costs

Variable	Description
`Name`	Unique building ID. It must start with a letter.
`type_cs`	Type of cooling supply assembly (refers to “code” in SUPPLY assemblies)
`type_dhw`	Type of hot water supply assembly (refers to “code” in SUPPLY assemblies)
`type_el`	Type of electrical supply assembly (refers to “code” in SUPPLY assemblies)
`type_hs`	Type of heating supply assembly (refers to “code” in SUPPLY assemblies)

get_building_weekly_schedules¶

path: inputs/building-properties/schedules/B001.csv

The following file is used by these scripts: demand, schedule_maker

Variable	Description
`METADATA`	TODO
`MONTHLY_MULTIPLIER`	Monthly probabilities of occupancy throughout the year

get_database_air_conditioning_systems¶

path: inputs/technology/assemblies/HVAC.xlsx

The following file is used by these scripts: demand

Worksheet: `CONTROLLER`¶
Variable	Description
`code`	Unique ID of the controller
`Description`	Describes the type of controller
`dT_Qcs`	correction temperature of emission losses due to control system of cooling
`dT_Qhs`	correction temperature of emission losses due to control system of heating

Worksheet: `COOLING`¶
Variable	Description
`class_cs`	Type or class of the cooling system
`code`	Unique ID of the heating system
`convection_cs`	Convective part of the power of the heating system in relation to the total power
`Description`	Describes the type of cooling system
`dTcs0_ahu_C`	Nominal temperature increase on the water side of the air-handling units
`dTcs0_aru_C`	Nominal temperature increase on the water side of the air-recirculation units
`dTcs0_scu_C`	Nominal temperature increase on the water side of the sensible cooling units
`dTcs_C`	Set-point correction for space emission systems
`Qcsmax_Wm2`	Maximum heat flow permitted by cooling system per m2 gross floor area
`Tc_sup_air_ahu_C`	Supply air temperature of the air-handling units
`Tc_sup_air_aru_C`	Supply air temperature of the air-recirculation units
`Tscs0_ahu_C`	Nominal supply temperature of the water side of the air-handling units
`Tscs0_aru_C`	Nominal supply temperature of the water side of the air-recirculation units
`Tscs0_scu_C`	Nominal supply temperature of the water side of the sensible cooling units

Worksheet: `HEATING`¶
Variable	Description
`class_hs`	Type or class of the heating system
`code`	Unique ID of the heating system
`convection_hs`	Convective part of the power of the heating system in relation to the total power
`Description`	Description
`dThs0_ahu_C`	Nominal temperature increase on the water side of the air-handling units
`dThs0_aru_C`	Nominal temperature increase on the water side of the air-recirculation units
`dThs0_shu_C`	Nominal temperature increase on the water side of the sensible heating units
`dThs_C`	correction temperature of emission losses due to type of heating system
`Qhsmax_Wm2`	Maximum heat flow permitted by heating system per m2 gross floor area
`Th_sup_air_ahu_C`	Supply air temperature of the air-recirculation units
`Th_sup_air_aru_C`	Supply air temperature of the air-handling units
`Tshs0_ahu_C`	Nominal supply temperature of the water side of the air-handling units
`Tshs0_aru_C`	Nominal supply temperature of the water side of the air-recirculation units
`Tshs0_shu_C`	Nominal supply temperature of the water side of the sensible heating units

Worksheet: `HOT_WATER`¶
Variable	Description
`code`	Unique ID of the hot water supply system
`Description`	Describes the Type of hot water supply system
`Qwwmax_Wm2`	Maximum heat flow permitted by hot water system per m2 gross floor area
`Tsww0_C`	Typical supply water temperature.

Worksheet: `VENTILATION`¶
Variable	Description
`code`	Unique ID of the type of ventilation
`Description`	Describes the Type of ventilation
`ECONOMIZER`	Boolean	economizer on
`HEAT_REC`	Boolean	heat recovery on
`MECH_VENT`	Boolean	mechanical ventilation on
`NIGHT_FLSH`	Boolean	night flush on
`WIN_VENT`	Boolean	window ventilation on

get_database_construction_standards¶

path: inputs/technology/archetypes/CONSTRUCTION_STANDARDS.xlsx

The following file is used by these scripts: archetypes_mapper

Worksheet: `ENVELOPE_ASSEMBLIES`¶
Variable	Description
`Es`	Fraction of gross floor area with electrical demands.
`Hs_ag`	Fraction of above ground gross floor area air-conditioned.
`Hs_bg`	Fraction of below ground gross floor area air-conditioned
`Ns`	Fraction of net gross floor area.
`STANDARD`	Unique ID of Construction Standard
`type_base`	Basement floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_cons`	Type of construction assembly (relates to “code” in ENVELOPE assemblies)
`type_floor`	Internal floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_leak`	Tightness level assembly (relates to “code” in ENVELOPE assemblies)
`type_part`	Internal partitions construction assembly (relates to “code” in ENVELOPE assemblies)
`type_roof`	Roof construction assembly (relates to “code” in ENVELOPE assemblies)
`type_shade`	Shading system assembly (relates to “code” in ENVELOPE assemblies)
`type_wall`	External wall construction assembly (relates to “code” in ENVELOPE assemblies)
`type_win`	Window assembly (relates to “code” in ENVELOPE assemblies)
`void_deck`	Number of floors (from the ground up) with an open envelope (default = 0)
`wwr_east`	Window to wall ratio in in facades facing east
`wwr_north`	Window to wall ratio in in facades facing north
`wwr_south`	Window to wall ratio in in facades facing south
`wwr_west`	Window to wall ratio in in facades facing west

Worksheet: `HVAC_ASSEMBLIES`¶
Variable	Description
`cool_ends`	End of the cooling season - use 00\|00 when there is none
`cool_starts`	Start of the cooling season - use 00\|00 when there is none
`heat_ends`	End of the heating season - use 00\|00 when there is none
`heat_starts`	Start of the heating season - use 00\|00 when there is none
`STANDARD`	Unique ID of Construction Standard
`type_cs`	Type of cooling HVAC assembly (relates to “code” in HVAC assemblies)
`type_ctrl`	Type of heating and cooling control HVAC assembly (relates to “code” in HVAC assemblies)
`type_dhw`	Type of hot water HVAC assembly (relates to “code” in HVAC assemblies)
`type_hs`	Type of heating HVAC assembly (relates to “code” in HVAC assemblies)
`type_vent`	Type of ventilation HVAC assembly (relates to “code” in HVAC assemblies)

Worksheet: `STANDARD_DEFINITION`¶
Variable	Description
`Description`	Description of the construction standard
`STANDARD`	Unique ID of Construction Standard
`YEAR_END`	Upper limit of year interval where the building properties apply
`YEAR_START`	Lower limit of year interval where the building properties apply

Worksheet: `SUPPLY_ASSEMBLIES`¶
Variable	Description
`STANDARD`	Unique ID of Construction Standard
`type_cs`	Type of cooling supply assembly (refers to “code” in SUPPLY assemblies)
`type_dhw`	Type of hot water supply assembly (refers to “code” in SUPPLY assemblies)
`type_el`	Type of electrical supply assembly (refers to “code” in SUPPLY assemblies)
`type_hs`	Type of heating supply assembly (refers to “code” in SUPPLY assemblies)

get_database_conversion_systems¶

path: inputs/technology/components/CONVERSION.xlsx

The following file is used by these scripts: decentralized, optimization, photovoltaic, photovoltaic_thermal, solar_collector

Worksheet: `Absorption_chiller`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`a_e`	parameter in the characteristic equations to calculate the evaporator side
`a_g`	parameter in the characteristic equations to calculate the generator side
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the Type of Absorption Chiller
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`e_e`	parameter in the characteristic equations to calculate the evaporator side
`e_g`	parameter in the characteristic equations to calculate the generator side
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`m_cw`	external flow rate of cooling water at the condenser and absorber
`m_hw`	external flow rate of hot water at the generator
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`r_e`	parameter in the characteristic equations to calculate the evaporator side
`r_g`	parameter in the characteristic equations to calculate the generator side
`s_e`	parameter in the characteristic equations to calculate the evaporator side
`s_g`	parameter in the characteristic equations to calculate the generator side
`type`	type of absorption chiller
`unit`	unit of the min/max capacity

Worksheet: `BH`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of borehole heat exchanger
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `Boiler`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of boiler
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `CCGT`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of combined-cycle gas turbine
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `Chiller`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the source of the benchmark standards.
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `CT`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of cooling tower
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `FC`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of fuel cell
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `Furnace`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`Description`	Describes the type of furnace
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `HEX`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`a_p`	parameter in the pressure loss function	f(x) = a_p + b_px^c_p + d_pln(x) + e_pxln*(x)	where x is the capacity mass flow rate [W/K]
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`b_p`	parameter in the pressure loss function	f(x) = a_p + b_px^c_p + d_pln(x) + e_pxln*(x)	where x is the capacity mass flow rate [W/K]
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`c_p`	parameter in the pressure loss function	f(x) = a_p + b_px^c_p + d_pln(x) + e_pxln*(x)	where x is the capacity mass flow rate [W/K]
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`d_p`	parameter in the pressure loss function	f(x) = a_p + b_px^c_p + d_pln(x) + e_pxln*(x)	where x is the capacity mass flow rate [W/K]
`Description`	Describes the type of heat exchanger
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)
`e_p`	parameter in the pressure loss function	f(x) = a_p + b_px^c_p + d_pln(x) + e_pxln*(x)	where x is the capacity mass flow rate [W/K]
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `HP`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the source of the heat pump
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `Pump`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the source of the benchmark standards.
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `PV`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the source of the benchmark standards.
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`misc_losses`	losses from cabling	resistances etc…
`module_length_m`	lengh of the PV module
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`PV_a0`	parameters for air mass modifier	f(x) = a0 + a1x + a2x*2 + a3x*3 + a4x**4	where x is the relative air mass
`PV_a1`	parameters for air mass modifier	f(x) = a0 + a1x + a2x*2 + a3x*3 + a4x**4	where x is the relative air mass
`PV_a2`	parameters for air mass modifier	f(x) = a0 + a1x + a2x*2 + a3x*3 + a4x**4	where x is the relative air mass
`PV_a3`	parameters for air mass modifier	f(x) = a0 + a1x + a2x*2 + a3x*3 + a4x**4	where x is the relative air mass
`PV_a4`	parameters for air mass modifier	f(x) = a0 + a1x + a2x*2 + a3x*3 + a4x**4	where x is the relative air mass
`PV_Bref`	cell maximum power temperature coefficient
`PV_n`	nominal efficiency
`PV_noct`	nominal operating cell temperature
`PV_th`	glazing thickness
`type`	redundant
`unit`	unit of the min/max capacity

Worksheet: `PVT`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the type of photovoltaic thermal technology
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`unit`	unit of the min/max capacity

Worksheet: `SC`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`aperture_area_ratio`	ratio of aperture area to panel area
`assumption`	items made by assumptions in this technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c1`	collector heat loss coefficient at zero temperature difference and wind speed
`c2`	ctemperature difference dependency of the heat loss coefficient
`C_eff`	thermal capacity of module
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	identifier of each unique equipment
`Cp_fluid`	heat capacity of the heat transfer fluid
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the type of solar collector
`dP1`	pressure drop at zero flow rate
`dP2`	pressure drop at nominal flow rate (mB0)
`dP3`	pressure drop at maximum flow rate (mB_max)
`dP4`	pressure drop at minimum flow rate (mB_min)
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`IAM_d`	incident angle modifier for diffuse radiation
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of this technology
`mB0_r`	nominal flow rate per aperture area
`mB_max_r`	maximum flow rate per aperture area
`mB_min_r`	minimum flow rate per aperture area
`module_area_m2`	module area of a solar collector
`module_length_m`	lengh of a solar collector module
`n0`	zero loss efficiency at normal incidence
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`t_max`	maximum operating temperature
`type`	type of the solar collector (FP: flate-plate or ET: evacuated-tube)
`unit`	unit of the min/max capacity

Worksheet: `TES`¶
Variable	Description
`a`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`assumption`	items made by assumptions in this storage technology
`b`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`c`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`C_mat_%`	Working fluid replacement cost factor (fraction of the investment cost)
`cap_max`	maximum capacity
`cap_min`	minimum capacity
`code`	Unique code that identifies the thermal energy storage technology
`Cp_kJkgK`	heat capacity of working fluid
`currency`	currency-year information of the investment cost function	should be unified to USD
`d`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`Description`	Describes the thermal energy storage technology
`e`	parameter in the investment cost function	f(x) = a + bx^c + dln(x) + exln*(x)	where x is the capacity
`HL_kJkg`	Lantent heat of working fluid at phase change temperature
`IR_%`	interest rate charged on the loan for the capital cost
`LT_mat_yr`	lifetime of the working fluid of this storage technology
`LT_yr`	lifetime of this storage technology
`O&M_%`	operation and maintnance cost factor (fraction of the investment cost)
`Rho_T_PHCH_kgm3`	Density of working fluid at phase change temperature
`T_max_C`	Maximum temperature of working fluid at full discharge
`T_min_C`	Minimum temperature of working fluid at full charge
`T_PHCH_C`	Phase change temperature of working fluid
`type`	code that identifies whether the storage is used for heating or cooling (different properties of the transport media)
`unit`	unit which describes the minimum and maximum capacity

get_database_distribution_systems¶

path: inputs/technology/components/DISTRIBUTION.xlsx

The following file is used by these scripts: optimization, thermal_network

Worksheet: `THERMAL_GRID`¶
Variable	Description
`Code`	pipe ID from the manufacterer
`D_ext_m`	external pipe diameter tolerance for the nominal diameter (DN)
`D_ins_m`	maximum pipe diameter tolerance for the nominal diameter (DN)
`D_int_m`	internal pipe diameter tolerance for the nominal diameter (DN)
`Inv_USD2015perm`	Typical cost of investment for a given pipe diameter.
`Pipe_DN`	Nominal pipe diameter
`Vdot_max_m3s`	maximum volumetric flow rate for the nominal diameter (DN)
`Vdot_min_m3s`	minimum volumetric flow rate for the nominal diameter (DN)

get_database_envelope_systems¶

path: inputs/technology/assemblies/ENVELOPE.xlsx

The following file is used by these scripts: demand, radiation, schedule_maker

Worksheet: `CONSTRUCTION`¶
Variable	Description
`Cm_Af`	Internal heat capacity per unit of air conditioned area. Defined according to ISO 13790.
`code`	Type of construction
`Description`	Describes the Type of construction

Worksheet: `FLOOR`¶
Variable	Description
`code`	Type of roof
`Description`	Describes the Type of roof
`GHG_FLOOR_kgCO2m2`	Embodied emissions per m2 of roof.(entire building life cycle)
`U_base`	Thermal transmittance of floor including linear losses (+10%). Defined according to ISO 13790.

Worksheet: `ROOF`¶
Variable	Description
`a_roof`	Solar absorption coefficient. Defined according to ISO 13790.
`code`	Type of roof
`Description`	Describes the Type of roof
`e_roof`	Emissivity of external surface. Defined according to ISO 13790.
`GHG_ROOF_kgCO2m2`	Embodied emissions per m2 of roof.(entire building life cycle)
`r_roof`	Reflectance in the Red spectrum. Defined according Radiance. (long-wave)
`U_roof`	Thermal transmittance of windows including linear losses (+10%). Defined according to ISO 13790.

Worksheet: `SHADING`¶
Variable	Description
`code`	Type of shading
`Description`	Describes the source of the benchmark standards.
`rf_sh`	Shading coefficient when shading device is active. Defined according to ISO 13790.

Worksheet: `TIGHTNESS`¶
Variable	Description
`code`	Type of tightness
`Description`	Describes the Type of tightness
`n50`	Air exchanges per hour at a pressure of 50 Pa.

Worksheet: `WALL`¶
Variable	Description
`a_wall`	Solar absorption coefficient. Defined according to ISO 13790.
`code`	Type of wall
`Description`	Describes the Type of wall
`e_wall`	Emissivity of external surface. Defined according to ISO 13790.
`GHG_WALL_kgCO2m2`	Embodied emissions per m2 of walls (entire building life cycle)
`r_wall`	Reflectance in the Red spectrum. Defined according Radiance. (long-wave)
`U_wall`	Thermal transmittance of windows including linear losses (+10%). Defined according to ISO 13790.

Worksheet: `WINDOW`¶
Variable	Description
`code`	Window type code to relate to other databases
`Description`	Describes the source of the benchmark standards.
`e_win`	Emissivity of external surface. Defined according to ISO 13790.
`F_F`	Window frame fraction coefficient. Defined according to ISO 13790.
`G_win`	Solar heat gain coefficient. Defined according to ISO 13790.
`GHG_WIN_kgCO2m2`	Embodied emissions per m2 of windows.(entire building life cycle)
`U_win`	Thermal transmittance of windows including linear losses (+10%). Defined according to ISO 13790.

get_database_feedstocks¶

path: inputs/technology/components/FEEDSTOCKS.xlsx

The following file is used by these scripts: decentralized, emissions, system_costs, optimization

Worksheet: `BIOGAS`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `COAL`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `DRYBIOMASS`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `GRID`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `NATURALGAS`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `OIL`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `SOLAR`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `WETBIOMASS`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

Worksheet: `WOOD`¶
Variable	Description
`GHG_kgCO2MJ`	Non-renewable Green House Gas Emissions factor
`hour`	hour of a 24 hour day
`Opex_var_buy_USD2015kWh`	buying price
`Opex_var_sell_USD2015kWh`	selling price
`reference`	reference

get_database_standard_schedules_use¶

path: inputs/technology/archetypes/schedules/RESTAURANT.csv

The following file is used by these scripts: archetypes_mapper

Worksheet: `APPLIANCES`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `COOLING`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `ELECTROMOBILITY`¶
Variable	Description
`1`	Average number of electric vehicles in this hour
`2`	Average number of electric vehicles in this hour
`3`	Average number of electric vehicles in this hour
`4`	Average number of electric vehicles in this hour
`5`	Average number of electric vehicles in this hour
`6`	Average number of electric vehicles in this hour
`7`	Average number of electric vehicles in this hour
`8`	Average number of electric vehicles in this hour
`9`	Average number of electric vehicles in this hour
`10`	Average number of electric vehicles in this hour
`11`	Average number of electric vehicles in this hour
`12`	Average number of electric vehicles in this hour
`13`	Average number of electric vehicles in this hour
`14`	Average number of electric vehicles in this hour
`15`	Average number of electric vehicles in this hour
`16`	Average number of electric vehicles in this hour
`17`	Average number of electric vehicles in this hour
`18`	Average number of electric vehicles in this hour
`19`	Average number of electric vehicles in this hour
`20`	Average number of electric vehicles in this hour
`21`	Average number of electric vehicles in this hour
`22`	Average number of electric vehicles in this hour
`23`	Average number of electric vehicles in this hour
`24`	Average number of electric vehicles in this hour
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `HEATING`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `LIGHTING`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `METADATA`¶
Variable	Description
`metadata`

Worksheet: `MONTHLY_MULTIPLIER`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`

Worksheet: `OCCUPANCY`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `PROCESSES`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `SERVERS`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

Worksheet: `WATER`¶
Variable	Description
`1`
`2`
`3`
`4`
`5`
`6`
`7`
`8`
`9`
`10`
`11`
`12`
`13`
`14`
`15`
`16`
`17`
`18`
`19`
`20`
`21`
`22`
`23`
`24`
`DAY`	Day of the week (weekday	saturday	or sunday)

get_database_supply_assemblies¶

path: inputs/technology/assemblies/SUPPLY.xlsx

The following file is used by these scripts: demand, emissions, system_costs

Worksheet: `COOLING`¶
Variable	Description
`CAPEX_USD2015kW`	Capital costs per kW
`code`	Code of cooling supply assembly
`Description`	description
`efficiency`	efficiency of the all in one system
`feedstock`	feedstock used by the the all in one system (refers to the FEEDSTOCK database)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of assembly
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`reference`	reference
`scale`	whether the all in one system is used at the building or the district scale

Worksheet: `ELECTRICITY`¶
Variable	Description
`CAPEX_USD2015kW`	Capital costs per kW
`code`	Type of all in one system
`Description`	Description of Type of all in one system
`efficiency`	efficiency of the all in one system
`feedstock`	feedstock used by the the all in one system (refers to the FEEDSTOCK database)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of assembly
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`reference`	Reference of the data
`scale`	whether the all in one system is used at the building or the district scale

Worksheet: `HEATING`¶
Variable	Description
`CAPEX_USD2015kW`	Capital costs per kW
`code`	Type of all in one system
`Description`	Description of Type of all in one system
`efficiency`	efficiency of the all in one system
`feedstock`	feedstock used by the the all in one system (refers to the FEEDSTOCK database)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of assembly
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`reference`	Reference of the data
`scale`	whether the all in one system is used at the building or the district scale

Worksheet: `HOT_WATER`¶
Variable	Description
`CAPEX_USD2015kW`	Capital costs per kW
`code`	Type of all in one system
`Description`	Description of Type of all in one system
`efficiency`	efficiency of the all in one system
`feedstock`	feedstock used by the the all in one system (refers to the FEEDSTOCK database)
`IR_%`	interest rate charged on the loan for the capital cost
`LT_yr`	lifetime of assembly
`O&M_%`	operation and maintenance cost factor (fraction of the investment cost)
`reference`	Reference of the data
`scale`	whether the all in one system is used at the building or the district scale

get_database_use_types_properties¶

path: inputs/technology/archetypes/use_types/USE_TYPE_PROPERTIES.xlsx

The following file is used by these scripts: archetypes_mapper

Worksheet: `INDOOR_COMFORT`¶
Variable	Description
`code`	use type code (refers to building use type)
`RH_max_pc`	Upper bound of relative humidity
`RH_min_pc`	Lower_bound of relative humidity
`Tcs_set_C`	Setpoint temperature for cooling system
`Tcs_setb_C`	Setback point of temperature for cooling system
`Ths_set_C`	Setpoint temperature for heating system
`Ths_setb_C`	Setback point of temperature for heating system
`Ve_lsp`	Indoor quality requirements of indoor ventilation per person

Worksheet: `INTERNAL_LOADS`¶
Variable	Description
`code`	use type code (refers to building use type)
`Ea_Wm2`	Peak specific electrical load due to computers and devices
`Ed_Wm2`	Peak specific electrical load due to servers/data centres
`El_Wm2`	Peak specific electrical load due to artificial lighting
`Epro_Wm2`	Peak specific electrical load due to industrial processes
`Ev_kWveh`	Peak capacity of electrical battery per vehicle
`Occ_m2p`	Occupancy density
`Qcpro_Wm2`	Peak specific process cooling load
`Qcre_Wm2`	Peak specific cooling load due to refrigeration (cooling rooms)
`Qhpro_Wm2`	Peak specific process heating load
`Qs_Wp`	Peak sensible heat load of people
`Vw_ldp`	Peak specific fresh water consumption (includes cold and hot water)
`Vww_ldp`	Peak specific daily hot water consumption
`X_ghp`	Moisture released by occupancy at peak conditions

Output¶

Output generated from simulations:

get_building_typology¶

path: inputs/building-properties/typology.dbf

The following file is used by these scripts: archetypes_mapper, demand, emissions

Variable	Description
`1ST_USE`	First (Main) Use type of the building
`1ST_USE_R`	Fraction of gross floor area for first Use Type
`2ND_USE`	Second Use type of the building
`2ND_USE_R`	Fraction of gross floor area for second Use Type
`3RD_USE`	Third Use type of the building
`3RD_USE_R`	Fraction of gross floor area for third Use Type
`Name`	Unique building ID. It must start with a letter.
`REFERENCE`	Reference to data (if any)
`STANDARD`	Construction Standard (relates to “code” in Supply Assemblies)
`YEAR`	Construction year

get_costs_operation_file¶

path: outputs/data/costs/operation_costs.csv

The following file is used by these scripts:

Variable	Description
`Aocc_m2`	Occupied floor area (heated/cooled)
`Capex_a_sys_building_scale_USD`	Annualized capital expenditures of building-scale systems
`Capex_a_sys_district_scale_USD`	Annualized capital expenditures of district-scale systems
`COAL_hs_cost_m2yr`	Operation costs of coal due to space heating per unit conditioned floor area
`COAL_hs_cost_yr`	Operation costs of coal due to space heating
`COAL_ww_cost_m2yr`	Operation costs of coal due to hotwater per unit conditioned floor area
`COAL_ww_cost_yr`	Operation costs of coal due to hotwater
`DC_cdata_cost_m2yr`	Operation costs of district cooling due to data center cooling per unit conditioned floor area
`DC_cdata_cost_yr`	Operation costs of district cooling due to data center cooling
`DC_cre_cost_m2yr`	Operation costs of district cooling due to cool room refrigeration per unit conditioned floor area
`DC_cre_cost_yr`	Operation costs of district cooling due to cool room refrigeration
`DC_cs_cost_m2yr`	Operation costs of district cooling due to space cooling per unit conditioned floor area
`DC_cs_cost_yr`	Operation costs of district cooling due to space cooling
`DH_hs_cost_m2yr`	Operation costs of district heating due to space heating per unit conditioned floor area
`DH_hs_cost_yr`	Operation costs of district heating due to space heating
`DH_ww_cost_m2yr`	Operation costs of district heating due to domestic hot water per unit conditioned floor area
`DH_ww_cost_yr`	Operation costs of district heating due to domestic hot water
`GRID_cost_m2yr`	Operation costs due to electricity supply from the grid per unit conditioned floor area
`GRID_cost_yr`	Operation costs due to electricity supply from the grid
`Name`	Unique building ID. It must start with a letter.
`NG_hs_cost_m2yr`	Operation costs of natural gas due to space heating per unit conditioned floor area
`NG_hs_cost_yr`	Operation costs of natural gas due to space heating
`NG_ww_cost_m2yr`	Operation costs of natural gas due to domestic hot water per unit conditioned floor area
`NG_ww_cost_yr`	Operation costs of natural gas due to domestic hot water
`OIL_hs_cost_m2yr`	Operation costs of oil due to space heating per unit conditioned floor area
`OIL_hs_cost_yr`	Operation costs of oil due to space heating
`OIL_ww_cost_m2yr`	Operation costs of oil due to domestic hot water per unit conditioned floor area
`OIL_ww_cost_yr`	Operation costs of oil due to domestic hot water
`Opex_a_sys_building_scale_USD`	Annual operational expenditures of building-scale systems
`Opex_a_sys_district_scale_USD`	Annual operational expenditures of district-scale systems
`PV_cost_m2yr`	Operation costs due to electricity supply from PV per unit conditioned floor area
`PV_cost_yr`	Operation costs due to electricity supply from PV
`SOLAR_hs_cost_m2yr`	Operation costs due to solar collectors for space heating per unit conditioned floor area
`SOLAR_hs_cost_yr`	Operation costs due to solar collectors for space heating
`SOLAR_ww_cost_m2yr`	Operation costs due to solar collectors for domestic hot water per unit conditioned floor area
`SOLAR_ww_cost_yr`	Operation costs due to solar collectors for domestic hot water
`WOOD_hs_cost_m2yr`	Operation costs of wood due to space heating per unit conditioned floor area
`WOOD_hs_cost_yr`	Operation costs of wood due to space heating
`WOOD_ww_cost_m2yr`	Operation costs of wood due to domestic hot water per unit conditioned floor area
`WOOD_ww_cost_yr`	Operation costs of wood due to domestic hot water

get_demand_results_file¶

path: outputs/data/demand/B001.csv

The following file is used by these scripts: decentralized, optimization, sewage_potential, thermal_network

Variable	Description
`COAL_hs_kWh`	Coal requirement for space heating supply
`COAL_ww_kWh`	Coal requirement for hotwater supply
`DATE`	Time stamp for each day of the year ascending in hour intervals.
`DC_cdata_kWh`	District cooling for data center cooling demand
`DC_cre_kWh`	District cooling for refrigeration demand
`DC_cs_kWh`	Energy consumption of space cooling system (if supplied by District Cooling), DC_cs = Qcs_sys / eff_cs
`DH_hs_kWh`	Energy requirement by district heating (space heating supply)
`DH_ww_kWh`	Energy requirement by district heating (hotwater supply)
`E_cdata_kWh`	Data centre cooling specific electricity consumption.
`E_cre_kWh`	Refrigeration system electricity consumption.
`E_cs_kWh`	Energy consumption of cooling system (if supplied by electricity grid), E_cs = Qcs_sys / eff_cs
`E_hs_kWh`	Heating system electricity consumption.
`E_sys_kWh`	End-use total electricity consumption = Ea + El + Edata + Epro + Eaux + Ev + Eve
`E_ww_kWh`	Hot water system electricity consumption.
`Ea_kWh`	End-use electricity for appliances
`Eal_kWh`	End-use electricity consumption of appliances and lighting, Eal = El_W + Ea_W
`Eaux_kWh`	End-use auxiliary electricity consumption, Eaux = Eaux_fw + Eaux_ww + Eaux_cs + Eaux_hs + Ehs_lat_aux
`Edata_kWh`	End-use data centre electricity consumption.
`El_kWh`	End-use electricity for lights
`Epro_kWh`	End-use electricity consumption for industrial processes.
`Ev_kWh`	End-use electricity for electric vehicles
`Eve_kWh`	End-use electricity for ventilation
`GRID_a_kWh`	Grid electricity consumption for appliances
`GRID_aux_kWh`	Grid electricity consumption for auxiliary loads
`GRID_cdata_kWh`	Grid electricity consumption for servers cooling
`GRID_cre_kWh`	Grid electricity consumption for refrigeration
`GRID_cs_kWh`	Grid electricity consumption for space cooling
`GRID_data_kWh`	Grid electricity consumption for servers
`GRID_hs_kWh`	Grid electricity consumption for space heating
`GRID_kWh`	Grid total electricity consumption, GRID_a + GRID_l + GRID_v + GRID_ve + GRID_data + GRID_pro + GRID_aux + GRID_ww + GRID_cs + GRID_hs + GRID_cdata + GRID_cre
`GRID_l_kWh`	Grid electricity consumption for lighting
`GRID_pro_kWh`	Grid electricity consumption for industrial processes
`GRID_ve_kWh`	Grid electricity consumption for ventilation
`GRID_ww_kWh`	Grid electricity consumption for hot water supply
`I_rad_kWh`	Radiative heat loss
`I_sol_and_I_rad_kWh`	Net radiative heat gain
`I_sol_kWh`	Solar heat gain
`mcpcdata_sys_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the chilled water delivered to data centre.
`mcpcre_sys_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the chilled water delivered to refrigeration.
`mcpcs_sys_ahu_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the chilled water delivered to air handling units (space cooling).
`mcpcs_sys_aru_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the chilled water delivered to air recirculation units (space cooling).
`mcpcs_sys_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the chilled water delivered to space cooling.
`mcpcs_sys_scu_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the chilled water delivered to sensible cooling units (space cooling).
`mcphs_sys_ahu_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the warm water delivered to air handling units (space heating).
`mcphs_sys_aru_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the warm water delivered to air recirculation units (space heating).
`mcphs_sys_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the warm water delivered to space heating.
`mcphs_sys_shu_kWperC`	Capacity flow rate (mass flow* specific heat Capacity) of the warm water delivered to sensible heating units (space heating).
`mcptw_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the fresh water
`mcpww_sys_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of domestic hot water
`Name`	Unique building ID. It must start with a letter.
`NG_hs_kWh`	NG requirement for space heating supply
`NG_ww_kWh`	NG requirement for hotwater supply
`OIL_hs_kWh`	OIL requirement for space heating supply
`OIL_ww_kWh`	OIL requirement for hotwater supply
`people`	Predicted occupancy: number of people in building
`PV_kWh`	PV electricity consumption
`Q_gain_lat_peop_kWh`	Latent heat gain from people
`Q_gain_sen_app_kWh`	Sensible heat gain from appliances
`Q_gain_sen_base_kWh`	Sensible heat gain from transmission through the base
`Q_gain_sen_data_kWh`	Sensible heat gain from data centres
`Q_gain_sen_light_kWh`	Sensible heat gain from lighting
`Q_gain_sen_peop_kWh`	Sensible heat gain from people
`Q_gain_sen_pro_kWh`	Sensible heat gain from industrial processes.
`Q_gain_sen_roof_kWh`	Sensible heat gain from transmission through the roof
`Q_gain_sen_vent_kWh`	Sensible heat gain from ventilation and infiltration
`Q_gain_sen_wall_kWh`	Sensible heat gain from transmission through the walls
`Q_gain_sen_wind_kWh`	Sensible heat gain from transmission through the windows
`Q_loss_sen_ref_kWh`	Sensible heat loss from refrigeration systems
`QC_sys_kWh`	Total energy demand for cooling, QC_sys = Qcs_sys + Qcdata_sys + Qcre_sys + Qcpro_sys
`Qcdata_kWh`	Data centre space cooling demand
`Qcdata_sys_kWh`	End-use data center cooling demand
`Qcpro_sys_kWh`	Process cooling demand
`Qcre_kWh`	Refrigeration space cooling demand
`Qcre_sys_kWh`	End-use refrigeration demand
`Qcs_dis_ls_kWh`	Cooling system distribution losses
`Qcs_em_ls_kWh`	Cooling system emission losses
`Qcs_kWh`	Specific cooling demand
`Qcs_lat_ahu_kWh`	AHU latent cooling demand
`Qcs_lat_aru_kWh`	ARU latent cooling demand
`Qcs_lat_sys_kWh`	Total latent cooling demand for all systems
`Qcs_sen_ahu_kWh`	AHU sensible cooling demand
`Qcs_sen_aru_kWh`	ARU sensible cooling demand
`Qcs_sen_scu_kWh`	SHU sensible cooling demand
`Qcs_sen_sys_kWh`	Total sensible cooling demand for all systems
`Qcs_sys_ahu_kWh`	AHU system cooling demand
`Qcs_sys_aru_kWh`	ARU system cooling demand
`Qcs_sys_kWh`	End-use space cooling demand, Qcs_sys = Qcs_sen_sys + Qcs_lat_sys + Qcs_em_ls + Qcs_dis_ls
`Qcs_sys_scu_kWh`	SCU system cooling demand
`QH_sys_kWh`	Total energy demand for heating, QH_sys = Qww_sys + Qhs_sys + Qhpro_sys
`Qhpro_sys_kWh`	Process heating demand
`Qhs_dis_ls_kWh`	Heating system distribution losses
`Qhs_em_ls_kWh`	Heating system emission losses
`Qhs_kWh`	Sensible heating system demand
`Qhs_lat_ahu_kWh`	AHU latent heating demand
`Qhs_lat_aru_kWh`	ARU latent heating demand
`Qhs_lat_sys_kWh`	Total latent heating demand for all systems
`Qhs_sen_ahu_kWh`	AHU sensible heating demand
`Qhs_sen_aru_kWh`	ARU sensible heating demand
`Qhs_sen_shu_kWh`	SHU sensible heating demand
`Qhs_sen_sys_kWh`	Total sensible heating demand
`Qhs_sys_ahu_kWh`	Space heating demand in AHU
`Qhs_sys_aru_kWh`	Space heating demand in ARU
`Qhs_sys_kWh`	End-use space heating demand, Qhs_sys = Qhs_sen_sys + Qhs_em_ls + Qhs_dis_ls
`Qhs_sys_shu_kWh`	SHU system heating demand
`Qww_kWh`	DHW specific heating demand
`Qww_sys_kWh`	End-use hotwater demand
`SOLAR_hs_kWh`	Solar thermal energy requirement for space heating supply
`SOLAR_ww_kWh`	Solar thermal energy requirement for hotwater supply
`T_ext_C`	Outdoor temperature
`T_int_C`	Indoor temperature
`Tcdata_sys_re_C`	Cooling supply temperature of the data centre
`Tcdata_sys_sup_C`	Cooling return temperature of the data centre
`Tcre_sys_re_C`	Cooling return temperature of the refrigeration system.
`Tcre_sys_sup_C`	Cooling supply temperature of the refrigeration system.
`Tcs_sys_re_ahu_C`	Return temperature cooling system
`Tcs_sys_re_aru_C`	Return temperature cooling system
`Tcs_sys_re_C`	System cooling return temperature.
`Tcs_sys_re_scu_C`	Return temperature cooling system
`Tcs_sys_sup_ahu_C`	Supply temperature cooling system
`Tcs_sys_sup_aru_C`	Supply temperature cooling system
`Tcs_sys_sup_C`	System cooling supply temperature.
`Tcs_sys_sup_scu_C`	Supply temperature cooling system
`theta_o_C`	Operative temperature in building (RC-model) used for comfort plotting
`Ths_sys_re_ahu_C`	Return temperature heating system
`Ths_sys_re_aru_C`	Return temperature heating system
`Ths_sys_re_C`	Heating system return temperature.
`Ths_sys_re_shu_C`	Return temperature heating system
`Ths_sys_sup_ahu_C`	Supply temperature heating system
`Ths_sys_sup_aru_C`	Supply temperature heating system
`Ths_sys_sup_C`	Heating system supply temperature.
`Ths_sys_sup_shu_C`	Supply temperature heating system
`Tww_sys_re_C`	Return temperature hotwater system
`Tww_sys_sup_C`	Supply temperature hotwater system
`WOOD_hs_kWh`	WOOD requirement for space heating supply
`WOOD_ww_kWh`	WOOD requirement for hotwater supply
`x_int`	Internal mass fraction of humidity (water/dry air)

get_geothermal_potential¶

path: outputs/data/potentials/Shallow_geothermal_potential.csv

The following file is used by these scripts: optimization

Variable	Description
`Area_avail_m2`	areas available to install ground source heat pumps
`QGHP_kW`	geothermal heat potential
`Ts_C`	ground temperature

get_lca_embodied¶

path: outputs/data/emissions/Total_LCA_embodied.csv

The following file is used by these scripts:

Variable	Description
`GFA_m2`	Gross floor area
`GHG_sys_embodied_kgCO2m2`	Embodied emissions per conditioned floor area due to building construction and decomissioning
`GHG_sys_embodied_tonCO2`	Embodied emissions due to building construction and decomissioning
`Name`	Unique building ID. It must start with a letter.

get_lca_mobility¶

path: outputs/data/emissions/Total_LCA_mobility.csv

The following file is used by these scripts:

Variable	Description
`GFA_m2`	Gross floor area
`GHG_sys_mobility_kgCO2m2`	Operational emissions per unit of conditioned floor area due to mobility
`GHG_sys_mobility_tonCO2`	Operational emissions due to mobility
`Name`	Unique building ID. It must start with a letter.

get_lca_operation¶

path: outputs/data/emissions/Total_LCA_operation.csv

The following file is used by these scripts:

Variable	Description
`COAL_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the coal powererd heating system
`COAL_hs_ghg_ton`	Operational emissions of the coal powered heating system
`COAL_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for coal powered heating system
`COAL_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the coal powered heating system
`COAL_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the coal powered domestic hot water system
`COAL_ww_ghg_ton`	Operational emissions of the coal powered domestic hot water system
`COAL_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for coal powered domestic hot water system
`COAL_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the coal powered domestic hot water system
`DC_cdata_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the district cooling for the data center
`DC_cdata_ghg_ton`	Operational emissions of the district cooling for the data center
`DC_cdata_nre_pen_GJ`	Operational primary energy demand (non-renewable) for district cooling system for cool room refrigeration
`DC_cdata_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) for district cooling for cool room refrigeration
`DC_cre_ghg_kgm2`	Operational emissions per unit of conditioned floor area for district cooling system for cool room refrigeration
`DC_cre_ghg_ton`	Operational emissions for district cooling system for cool room refrigeration
`DC_cre_nre_pen_GJ`	Operational primary energy demand (non-renewable) for district cooling system for cool room refrigeration
`DC_cre_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) for cool room refrigeration
`DC_cs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the district cooling
`DC_cs_ghg_ton`	Operational emissions of the district cooling
`DC_cs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for district cooling system
`DC_cs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the district cooling
`DH_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the district heating system
`DH_hs_ghg_ton`	Operational emissions of the district heating system
`DH_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for district heating system
`DH_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the district heating system
`DH_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the district heating domestic hot water system
`DH_ww_ghg_ton`	Operational emissions of the district heating powered domestic hot water system
`DH_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for district heating powered domestic hot water system
`DH_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the district heating domestic hot water system
`GFA_m2`	Gross floor area
`GHG_sys_kgCO2m2`	Total operational emissions per unit of conditioned floor area
`GHG_sys_tonCO2`	Total operational emissions
`GRID_ghg_kgm2`	Operational emissions per unit of conditioned floor area from grid electricity
`GRID_ghg_ton`	Operational emissions of the electrictiy from the grid
`GRID_nre_pen_GJ`	Operational primary energy demand (non-renewable) from the grid
`GRID_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) from grid electricity
`Name`	Unique building ID. It must start with a letter.
`NG_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the natural gas powered heating system
`NG_hs_ghg_ton`	Operational emissions of the natural gas powered heating system
`NG_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for natural gas powered heating system
`NG_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the natural gas powered heating system
`NG_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the gas powered domestic hot water system
`NG_ww_ghg_ton`	Operational emissions of the solar powered domestic hot water system
`NG_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for natural gas powered domestic hot water system
`NG_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the natural gas powered domestic hot water system
`OIL_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the oil powered heating system
`OIL_hs_ghg_ton`	Operational emissions of the oil powered heating system
`OIL_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for oil powered heating system
`OIL_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the oil powered heating system
`OIL_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the oil powered domestic hot water system
`OIL_ww_ghg_ton`	Operational emissions of the oil powered domestic hot water system
`OIL_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for oil powered domestic hot water system
`OIL_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the oil powered domestic hot water system
`PV_ghg_kgm2`	Operational emissions per unit of conditioned floor area for PV-System
`PV_ghg_ton`	Operational emissions of the PV-System
`PV_nre_pen_GJ`	Operational primary energy demand (non-renewable) for PV-System
`PV_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) for PV System
`SOLAR_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the solar powered heating system
`SOLAR_hs_ghg_ton`	Operational emissions of the solar powered heating system
`SOLAR_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) of the solar powered heating system
`SOLAR_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the solar powered heating system
`SOLAR_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the solar powered domestic hot water system
`SOLAR_ww_ghg_ton`	Operational emissions of the solar powered domestic hot water system
`SOLAR_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for solar powered domestic hot water system
`SOLAR_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the solar poweed domestic hot water system
`WOOD_hs_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the wood powered heating system
`WOOD_hs_ghg_ton`	Operational emissions of the wood powered heating system
`WOOD_hs_nre_pen_GJ`	Operational primary energy demand (non-renewable) for wood powered heating system
`WOOD_hs_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the wood powered heating system
`WOOD_ww_ghg_kgm2`	Operational emissions per unit of conditioned floor area of the wood powered domestic hot water system
`WOOD_ww_ghg_ton`	Operational emissions of the wood powered domestic hot water system
`WOOD_ww_nre_pen_GJ`	Operational primary energy demand (non-renewable) for wood powered domestic hot water system
`WOOD_ww_nre_pen_MJm2`	Operational primary energy demand per unit of conditioned floor area (non-renewable) of the wood powered domestic hot water system

get_multi_criteria_analysis¶

path: outputs/data/multicriteria/gen_2_multi_criteria_analysis.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_sys_building_scale_USD`	Annualized Capital costs of building-scale systems
`Capex_a_sys_district_scale_USD`	Capital costs of district-scale systems
`Capex_a_sys_USD`	Capital costs of all systems
`Capex_total_sys_building_scale_USD`	Capital costs of building-scale systems
`Capex_total_sys_district_scale_USD`	Capital costs of district-scale systems
`Capex_total_sys_USD`	Capital costs of all systems
`generation`	Generation or iteration
`GHG_rank`	Rank for emissions
`GHG_sys_building_scale_tonCO2`	Green house gas emissions of building-scale systems
`GHG_sys_district_scale_tonCO2`	Green house gas emissions of building-scale systems
`GHG_sys_tonCO2`	Green house gas emissions of all systems
`individual`	system number
`individual_name`	Name of system
`normalized_Capex_total`	normalization of CAPEX
`normalized_emissions`	normalization of GHG
`normalized_Opex`	Normalization of OPEX
`normalized_TAC`	normalization of TAC
`Opex_a_sys_building_scale_USD`	Operational costs of building-scale systems
`Opex_a_sys_district_scale_USD`	Operational costs of district-scale systems
`Opex_a_sys_USD`	Operational costs of all systems
`TAC_rank`	Rank of TAC
`TAC_sys_building_scale_USD`	Equivalent annual costs of building-scale systems
`TAC_sys_district_scale_USD`	Equivalent annual of district-scale systems
`TAC_sys_USD`	Equivalent annual costs of all systems
`user_MCDA`	Best system accoridng to user mult-criteria weights
`user_MCDA_rank`	Rank of Best system according to user mult-criteria weights

get_network_energy_pumping_requirements_file¶

path: outputs/data/thermal-network/DH__plant_pumping_load_kW.csv

The following file is used by these scripts:

Variable	Description
`pressure_loss_return_kW`	pumping electricity required to overcome pressure losses in the return network
`pressure_loss_substations_kW`	pumping electricity required to overcome pressure losses in the substations
`pressure_loss_supply_kW`	pumping electricity required to overcome pressure losses in the supply network
`pressure_loss_total_kW`	pumping electricity required to overcome pressure losses in the entire network

get_network_layout_edges_shapefile¶

path: outputs/data/thermal-network/DH/edges.shp

The following file is used by these scripts: thermal_network

Variable	Description
`geometry`	Geometry
`length_m`	lenth of this edge
`Name`	Unique network pipe ID.
`Pipe_DN`	Classifies nominal pipe diameters (DN) into typical bins.
`Type_mat`	Material type

get_network_layout_nodes_shapefile¶

path: outputs/data/thermal-network/DH/nodes.shp

The following file is used by these scripts: thermal_network

Variable	Description
`Building`	Unique building ID. e.g. “B01”
`geometry`	Geometry
`Name`	Unique node ID. e.g. “NODE1”
`Type`	Type of node.

get_network_linear_pressure_drop_edges¶

path: outputs/data/thermal-network/DH__linear_pressure_drop_edges_Paperm.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	linear pressure drop in this pipe section

get_network_linear_thermal_loss_edges_file¶

path: outputs/data/thermal-network/DH__linear_thermal_loss_edges_Wperm.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	linear thermal losses in thie pipe section

get_network_pressure_at_nodes¶

path: outputs/data/thermal-network/DH__pressure_at_nodes_Pa.csv

The following file is used by these scripts:

Variable	Description
`NODE0`	pressure at this node

get_network_temperature_plant¶

path: outputs/data/thermal-network/DH__temperature_plant_K.csv

The following file is used by these scripts:

Variable	Description
`temperature_return_K`	Plant return temperature at each time step
`temperature_supply_K`	Plant supply temperature at each time step

get_network_temperature_return_nodes_file¶

path: outputs/data/thermal-network/DH__temperature_return_nodes_K.csv

The following file is used by these scripts:

Variable	Description
`NODE0`	Return temperature at node NODE0 (outlet temperature of NODE0) at each time step

get_network_temperature_supply_nodes_file¶

path: outputs/data/thermal-network/DH__temperature_supply_nodes_K.csv

The following file is used by these scripts:

Variable	Description
`NODE0`	Supply temperature at node NODE0 (inlet temperature of NODE0) at each time step

get_network_thermal_loss_edges_file¶

path: outputs/data/thermal-network/DH__thermal_loss_edges_kW.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	Thermal losses along pipe PIPE0 at each time step

get_network_total_pressure_drop_file¶

path: outputs/data/thermal-network/DH__plant_pumping_pressure_loss_Pa.csv

The following file is used by these scripts: optimization

Variable	Description
`pressure_loss_return_Pa`	Pressure losses in the return network at each time step
`pressure_loss_substations_Pa`	Pressure losses in all substations at each time step
`pressure_loss_supply_Pa`	Pressure losses in the supply network at each time step
`pressure_loss_total_Pa`	Total pressure losses in the entire thermal network at each time step

get_network_total_thermal_loss_file¶

path: outputs/data/thermal-network/DH__total_thermal_loss_edges_kW.csv

The following file is used by these scripts: optimization

Variable	Description
`thermal_loss_return_kW`	Thermal losses in the supply network at each time step
`thermal_loss_supply_kW`	Thermal losses in the return network at each time step
`thermal_loss_total_kW`	Thermal losses in the entire thermal network at each time step

get_nominal_edge_mass_flow_csv_file¶

path: outputs/data/thermal-network/Nominal_EdgeMassFlow_at_design_{network_type}__kgpers.csv

The following file is used by these scripts: thermal_network

Variable	Description
`PIPE0`	Mass flow rate in pipe PIPE0 at design operating conditions

get_nominal_node_mass_flow_csv_file¶

path: outputs/data/thermal-network/Nominal_NodeMassFlow_at_design_{network_type}__kgpers.csv

The following file is used by these scripts: thermal_network

Variable	Description
`NODE0`	Mass flow rate in node NODE0 at design operating conditions

get_optimization_building_scale_cooling_capacity¶

path: outputs/data/optimization/slave/gen_1/ind_0_building_scale_cooling_capacity.csv

The following file is used by these scripts:

Variable	Description
`Capacity_ACH_SC_FP_cool_building_scale_W`	Thermal Capacity of Absorption Chiller and Solar Collector (Flat Plate) for Decentralized Building
`Capacity_ACHHT_FP_cool_building_scale_W`	Thermal Capacity of High-Temperature Absorption Chiller and Solar Collector (Flat Plate) for Decentralized Building
`Capacity_BaseVCC_AS_cool_building_scale_W`	Thermal Capacity of Base load Vapor Compression Chiller for Decentralized Building
`Capacity_DX_AS_cool_building_scale_W`	Thermal Capacity of Direct Expansion Air-Source for Decentralized Building
`Capacity_VCCHT_AS_cool_building_scale_W`	Thermal Capacity of High-Temperature Vapor Compression Chiller (Air-Source) for Decentralized Building
`Capaticy_ACH_SC_ET_cool_building_scale_W`	Thermal Capacity of Absorption Chiller and Solar Collector (Evacuated Tube)for Decentralized Building
`Name`	Unique building ID. It must start with a letter

get_optimization_building_scale_heating_capacity¶

path: outputs/data/optimization/slave/gen_0/ind_1_building_scale_heating_capacity.csv

The following file is used by these scripts:

Variable	Description
`Capacity_BaseBoiler_NG_heat_building_scale_W`	Thermal capacity of Base load boiler for Decentralized building
`Capacity_FC_NG_heat_building_scale_W`	Thermal capacity of Fuel Cell for Decentralized building
`Capacity_GS_HP_heat_building_scale_W`	Thermal capacity of ground-source heat pump for Decentralized building
`Name`	Unique building ID. It must start with a letter

get_optimization_checkpoint¶

path: outputs/data/optimization/master/CheckPoint_1

The following file is used by these scripts:

Variable	Description
`difference_generational_distances`	TODO
`generation`	TODO
`generational_distances`	TODO
`selected_population`	TODO
`tested_population`	TODO

get_optimization_decentralized_folder_building_result_cooling¶

path: outputs/data/optimization/decentralized/{building}_{configuration}_cooling.csv

The following file is used by these scripts: optimization

Variable	Description
`Best configuration`	Index of best configuration simulated
`Capacity_ACH_SC_FP_W`	Thermal Capacity of Absorption Chiller connected to Flat-plate Solar Collector
`Capacity_ACHHT_FP_W`	Thermal Capacity of High Temperature Absorption Chiller connected to Solar Collector (flat Plate)
`Capacity_BaseVCC_AS_W`	Thermal Capacity of Base Vapor compression chiller (air-source)
`Capacity_DX_AS_W`	Thermal Capacity of Direct-Expansion Unit Air-source
`Capacity_VCCHT_AS_W`	Thermal Capacity of High Temperature Vapor compression chiller (air-source)
`Capaticy_ACH_SC_ET_W`	Thermal Capacity of Absorption Chiller connected to Evacuated-Tube Solar Collector
`Capex_a_USD`	Annualized Capital Costs
`Capex_total_USD`	Total Capital Costs
`GHG_tonCO2`	Annual Green House Gas Emissions
`Nominal heating load`	Nominal heat load
`Opex_fixed_USD`	Fixed Annual Operational Costs
`Opex_var_USD`	Variable Annual Operational Costs
`TAC_USD`	Total Annualized Costs

get_optimization_decentralized_folder_building_result_cooling_activation¶

path: outputs/data/optimization/decentralized/{building}_{configuration}_cooling_activation.csv

The following file is used by these scripts: optimization

Variable	Description
`E_ACH_req_W`	Electricity requirements of Absorption Chillers
`E_BaseVCC_AS_req_W`	Electricity requirements of Vapor Compression Chillers and refrigeration
`E_cs_cre_cdata_req_W`	Electricity requirements due to space cooling, servers cooling and refrigeration
`E_CT_req_W`	Electricity requirements of Cooling Towers
`E_DX_AS_req_W`	Electricity requirements of Air-Source direct expansion chillers
`E_SC_ET_req_W`	Electricity requirements of Solar Collectors (evacuated-tubes)
`E_SC_FP_req_W`	Electricity requirements of Solar Collectors (flat-plate)
`NG_Boiler_req`	Requirements of Natural Gas for Boilers
`NG_Burner_req`	Requirements of Natural Gas for Burners
`Q_ACH_gen_directload_W`	Thermal energy generated by Absorption chillers
`Q_BaseVCC_AS_gen_directload_W`	Thermal energy generated by Air-Source Vapor-compression chillers
`Q_Boiler_NG_ACH_W`	Thermal energy generated by Natural gas Boilers to Absorption chillers
`Q_Burner_NG_ACH_W`	Thermal energy generated by Natural gas Burners to Absorption chillers
`Q_DX_AS_gen_directload_W`	Thermal energy generated by Air-Source direct expansion chillers
`Q_SC_ET_ACH_W`	Thermal energy generated by Solar Collectors (evacuated-tubes) to Absorption chillers
`Q_SC_FP_ACH_W`	Thermal energy generated by Solar Collectors (flat-plate) to Absorption chillers

get_optimization_decentralized_folder_building_result_heating¶

path: outputs/data/optimization/decentralized/DiscOp_B001_result_heating.csv

The following file is used by these scripts: optimization

Variable	Description
`Best configuration`	Index of best configuration simulated
`Capacity_BaseBoiler_NG_W`	Thermal capacity of Baseload Boiler NG
`Capacity_FC_NG_W`	Thermal Capacity of Fuel Cell NG
`Capacity_GS_HP_W`	Thermal Capacity of Ground Source Heat Pump
`Capex_a_USD`	Annualized Capital Costs
`Capex_total_USD`	Total Capital Costs
`GHG_tonCO2`	Annual Green House Gas Emissions
`Nominal heating load`	Nominal heat load
`Opex_fixed_USD`	Fixed Annual Operational Costs
`Opex_var_USD`	Variable Annual Operational Costs
`TAC_USD`	Total Annualized Costs

get_optimization_decentralized_folder_building_result_heating_activation¶

path: outputs/data/optimization/decentralized/DiscOp_B001_result_heating_activation.csv

The following file is used by these scripts: optimization

Variable	Description
`BackupBoiler_Status`	Status of the BackupBoiler (1=on, 0 =off)
`BG_Boiler_req_W`	Requirements of Bio-gas for Base load Boiler
`Boiler_Status`	Status of the Base load Boiler (1=on, 0 =off)
`E_Fuelcell_gen_export_W`	Electricity generation of fuel cell exported to the grid
`E_hs_ww_req_W`	Electricity Requirements for heat pump compressor and auxiliary uses (if required)
`Fuelcell_Status`	Status of the fuel cell (1=on, 0 =off)
`GHP_Status`	Status of the ground-source heat pump (1=on, 0 =off)
`NG_BackupBoiler_req_W`	Requirements of Natural Gas for Back-up Boiler
`NG_Boiler_req_W`	Requirements of Natural Gas for Base load Boiler
`NG_FuelCell_req_W`	Requirements of Natural Gas for fuel cell
`Q_BackupBoiler_gen_directload_W`	Thermal generation of Back-up Boiler to direct load
`Q_Boiler_gen_directload_W`	Thermal generation of Base load Boiler to direct load
`Q_Fuelcell_gen_directload_W`	Thermal generation of fuel cell to direct load
`Q_GHP_gen_directload_W`	Thermal generation of ground-source heat pump to direct load

get_optimization_district_scale_cooling_capacity¶

path: outputs/data/optimization/slave/gen_1/ind_1_district_scale_cooling_capacity.csv

The following file is used by these scripts:

Variable	Description
`Capacity_ACH_SC_FP_cool_building_scale_W`	Thermal Capacity of Absorption Chiller and Solar Collector (Flat Plate) for Decentralized Building
`Capacity_ACHHT_FP_cool_building_scale_W`	Thermal Capacity of High-Temperature Absorption Chiller and Solar Collector (Flat Plate) for Decentralized Building
`Capacity_BaseVCC_AS_cool_building_scale_W`	Thermal Capacity of Base load Vapor Compression Chiller for Decentralized Building
`Capacity_DX_AS_cool_building_scale_W`	Thermal Capacity of Direct Expansion Air-Source for Decentralized Building
`Capacity_VCCHT_AS_cool_building_scale_W`	Thermal Capacity of High-Temperature Vapor Compression Chiller (Air-Source) for Decentralized Building
`Capaticy_ACH_SC_ET_cool_building_scale_W`	Thermal Capacity of Absorption Chiller and Solar Collector (Evacuated Tube)for Decentralized Building
`Name`	Unique building ID. It must start with a letter

get_optimization_district_scale_electricity_capacity¶

path: outputs/data/optimization/slave/gen_2/ind_0_district_scale_electrical_capacity.csv

The following file is used by these scripts:

Variable	Description
`Capacity_GRID_el_district_scale_W`	Electrical Capacity Required from the local Grid
`Capacity_PV_el_district_scale_m2`	Area Coverage of PV in central Plant
`Capacity_PV_el_district_scale_W`	Electrical Capacity of PV in central Plant

get_optimization_district_scale_heating_capacity¶

path: outputs/data/optimization/slave/gen_0/ind_2_district_scale_heating_capacity.csv

The following file is used by these scripts:

Variable	Description
`Capacity_BackupBoiler_NG_heat_district_scale_W`	Thermal Capacity of Back-up Boiler - Natural Gas in central plant
`Capacity_BaseBoiler_NG_heat_district_scale_W`	Thermal Capacity of Base Load Boiler - Natural Gas in central plant
`Capacity_CHP_DB_el_district_scale_W`	Electrical Capacity of CHP Dry-Biomass in central plant
`Capacity_CHP_DB_heat_district_scale_W`	ThermalCapacity of CHP Dry-Biomass in central plant
`Capacity_CHP_NG_el_district_scale_W`	Electrical Capacity of CHP Natural-Gas in central plant
`Capacity_CHP_NG_heat_district_scale_W`	Thermal Capacity of CHP Natural-Gas in central plant
`Capacity_CHP_WB_el_district_scale_W`	Electrical Capacity of CHP Wet-Biomass in central plant
`Capacity_CHP_WB_heat_district_scale_W`	Thermal Capacity of CHP Wet-Biomass in central plant
`Capacity_HP_DS_heat_district_scale_W`	Thermal Capacity of Heat Pump Server-Source in central plant
`Capacity_HP_GS_heat_district_scale_W`	Thermal Capacity of Heat Pump Ground-Source in central plant
`Capacity_HP_SS_heat_district_scale_W`	Thermal Capacity of Heat Pump Sewage-Source in central plant
`Capacity_HP_WS_heat_district_scale_W`	Thermal Capacity of Heat Pump Water-Source in central plant
`Capacity_PeakBoiler_NG_heat_district_scale_W`	Thermal Capacity of Peak Boiler - Natural Gas in central plant
`Capacity_PVT_el_district_scale_W`	Electrical Capacity of PVT Field in central plant
`Capacity_PVT_heat_district_scale_W`	Thermal Capacity of PVT panels in central plant
`Capacity_SC_ET_heat_district_scale_W`	Thermal Capacity of Solar Collectors (Evacuated-tube) in central plant
`Capacity_SC_FP_heat_district_scale_W`	Thermal Capacity of Solar Collectors (Flat-plate) in central plant
`Capacity_SeasonalStorage_WS_heat_district_scale_W`	Thermal Capacity of Seasonal Thermal Storage in central plant

get_optimization_generation_building_scale_performance¶

path: outputs/data/optimization/slave/gen_2/gen_2_building_scale_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_cooling_building_scale_USD`	Annualized Capital Costs of building-scale systems due to cooling
`Capex_a_heating_building_scale_USD`	Annualized Capital Costs of building-scale systems due to heating
`Capex_total_cooling_building_scale_USD`	Total Capital Costs of building-scale systems due to cooling
`Capex_total_heating_building_scale_USD`	Total Capital Costs of building-scale systems due to heating
`generation`	No. Generation or Iteration (genetic Algorithm)
`GHG_cooling_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems due to Cooling
`GHG_heating_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems due to Heating
`individual`	No. Individual unique ID
`individual_name`	Name of Individual unique ID
`Opex_fixed_cooling_building_scale_USD`	Fixed Operational Costs of building-scale systems due to cooling
`Opex_fixed_heating_building_scale_USD`	Fixed Operational Costs of building-scale systems due to heating
`Opex_var_cooling_building_scale_USD`	Variable Operational Costs of building-scale systems due to cooling
`Opex_var_heating_building_scale_USD`	Variable Operational Costs of building-scale systems due to heating

get_optimization_generation_district_scale_performance¶

path: outputs/data/optimization/slave/gen_1/gen_1_district_scale_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_BackupBoiler_NG_district_scale_USD`	Annualized Capital Costs of Back-up Boiler Natural Gas in Central Plant
`Capex_a_BaseBoiler_NG_district_scale_USD`	Annualized Capital Costs of Base Load Boiler Boiler Natural Gas in Central Plant
`Capex_a_CHP_NG_district_scale_USD`	Annualized Capital Costs of CHP Natural Gas in Central Plant
`Capex_a_DHN_district_scale_USD`	Annualized Capital Costs of District Heating Network
`Capex_a_Furnace_dry_district_scale_USD`	Annualized Capital Costs of CHP Dry-Biomass in Central Plant
`Capex_a_Furnace_wet_district_scale_USD`	Annualized Capital Costs of CHP Wet-Biomass in Central Plant
`Capex_a_GHP_district_scale_USD`	Annualized Capital Costs of Ground-Source Heat Pump in Central Plant
`Capex_a_GRID_district_scale_USD`	Annualized Capital Costs of connection to local electrical grid
`Capex_a_HP_Lake_district_scale_USD`	Annualized Capital Costs of Lake-Source Heat Pump in Central Plant
`Capex_a_HP_Server_district_scale_USD`	Annualized Capital Costs of Server-Source Heat Pump in Central Plant
`Capex_a_HP_Sewage_district_scale_USD`	Annualized Capital Costs of Sewage-Source Heat Pump in Central Plant
`Capex_a_PeakBoiler_NG_district_scale_USD`	Annualized Capital Costs of Peak Load Boiler Boiler Natural Gas in Central Plant
`Capex_a_PV_district_scale_USD`	Annualized Capital Costs of Photovoltaic Panels in Central Plant
`Capex_a_PVT_district_scale_USD`	Annualized Capital Costs of PVT Panels in Central Plant
`Capex_a_SC_ET_district_scale_USD`	Annualized Capital Costs of Solar Collectors (evacuated-Tube) in Central Plant
`Capex_a_SC_FP_district_scale_USD`	Annualized Capital Costs of Solar Collectors (Flat-Plate) in Central Plant
`Capex_a_SeasonalStorage_WS_district_scale_USD`	Annualized Capital Costs of Seasonal Thermal Storage in Central Plant
`Capex_a_SubstationsHeating_district_scale_USD`	Annualized Capital Costs of Thermal Substations
`Capex_total_BackupBoiler_NG_district_scale_USD`	Total Capital Costs of Back-up Boiler Natural Gas in Central Plant
`Capex_total_BaseBoiler_NG_district_scale_USD`	Total Capital Costs of Base Load Boiler Boiler Natural Gas in Central Plant
`Capex_total_CHP_NG_district_scale_USD`	Total Capital Costs of CHP Natural Gas in Central Plant
`Capex_total_DHN_district_scale_USD`	Total Capital Costs of District Heating Network
`Capex_total_Furnace_dry_district_scale_USD`	Total Capital Costs of CHP Dry-Biomass in Central Plant
`Capex_total_Furnace_wet_district_scale_USD`	Total Capital Costs of CHP Wet-Biomass in Central Plant
`Capex_total_GHP_district_scale_USD`	Total Capital Costs of Ground-Source Heat Pump in Central Plant
`Capex_total_GRID_district_scale_USD`	Total Capital Costs of connection to local electrical grid
`Capex_total_HP_Lake_district_scale_USD`	Total Capital Costs of Lake-Source Heat Pump in Central Plant
`Capex_total_HP_Server_district_scale_USD`	Total Capital Costs of Server-Source Heat Pump in Central Plant
`Capex_total_HP_Sewage_district_scale_USD`	Total Capital Costs of Sewage-Source Heat Pump in Central Plant
`Capex_total_PeakBoiler_NG_district_scale_USD`	Total Capital Costs of Peak Load Boiler Boiler Natural Gas in Central Plant
`Capex_total_PV_district_scale_USD`	Total Capital Costs of Photovoltaic Panels in Central Plant
`Capex_total_PVT_district_scale_USD`	Total Capital Costs of PVT Panels in Central Plant
`Capex_total_SC_ET_district_scale_USD`	Total Capital Costs of Solar Collectors (evacuated-Tube) in Central Plant
`Capex_total_SC_FP_district_scale_USD`	Total Capital Costs of Solar Collectors (Flat-Plate) in Central Plant
`Capex_total_SeasonalStorage_WS_district_scale_USD`	Total Capital Costs of Seasonal Thermal Storage in Central Plant
`Capex_total_SubstationsHeating_district_scale_USD`	Total Capital Costs of Thermal Substations
`generation`	Number of the Generation or Iteration (Genetic algorithm)
`GHG_DB_district_scale_tonCO2yr`	Green House Gas Emissions of Dry-Biomass of district-scale systems
`GHG_GRID_exports_district_scale_tonCO2yr`	Green House Gas Emissions of Exports of Electricity
`GHG_GRID_imports_district_scale_tonCO2yr`	Green House Gas Emissions of Import of Electricity
`GHG_NG_district_scale_tonCO2yr`	Green House Gas Emissions of Natural Gas of district-scale systems
`GHG_WB_district_scale_tonCO2yr`	Green House Gas Emissions of Wet-Biomass of district-scale systems
`individual`	Unique numerical ID of individual
`individual_name`	Unique alphanumerical ID of individual
`Opex_fixed_BackupBoiler_NG_district_scale_USD`	Fixed Operation Costs of Back-up Boiler Natural Gas in Central Plant
`Opex_fixed_BaseBoiler_NG_district_scale_USD`	Fixed Operation Costs of Base Load Boiler Boiler Natural Gas in Central Plant
`Opex_fixed_CHP_NG_district_scale_USD`	Fixed Operation Costs of CHP Natural Gas in Central Plant
`Opex_fixed_DHN_district_scale_USD`	Fixed Operation Costs of District Heating Network
`Opex_fixed_Furnace_dry_district_scale_USD`	Fixed Operation Costs of CHP Dry-Biomass in Central Plant
`Opex_fixed_Furnace_wet_district_scale_USD`	Fixed Operation Costs of CHP Wet-Biomass in Central Plant
`Opex_fixed_GHP_district_scale_USD`	Fixed Operation Costs of Ground-Source Heat Pump in Central Plant
`Opex_fixed_GRID_district_scale_USD`	Fixed Operation Costs of Electricity in Buildings Connected to Central Plant
`Opex_fixed_HP_Lake_district_scale_USD`	Fixed Operation Costs of Lake-Source Heat Pump in Central Plant
`Opex_fixed_HP_Server_district_scale_USD`	Fixed Operation Costs of Server-Source Heat Pump in Central Plant
`Opex_fixed_HP_Sewage_district_scale_USD`	Fixed Operation Costs of Sewage-Source Heat Pump in Central Plant
`Opex_fixed_PeakBoiler_NG_district_scale_USD`	Fixed Operation Costs of Peak Load Boiler Boiler Natural Gas in Central Plant
`Opex_fixed_PV_district_scale_USD`	Fixed Operation Costs of Photovoltaic Panels in Central Plant
`Opex_fixed_PVT_district_scale_USD`	Fixed Operation Costs of PVT Panels in Central Plant
`Opex_fixed_SC_ET_district_scale_USD`	Fixed Operation Costs of Solar Collectors (evacuated-Tube) in Central Plant
`Opex_fixed_SC_FP_district_scale_USD`	Fixed Operation Costs of Solar Collectors (Flat-Plate) in Central Plant
`Opex_fixed_SeasonalStorage_WS_district_scale_USD`	Fixed Operation Costs of Seasonal Thermal Storage in Central Plant
`Opex_fixed_SubstationsHeating_district_scale_USD`	Fixed Operation Costs of Thermal Substations
`Opex_var_DB_district_scale_USD`	Variable Operation Costs due to consumption of Dry-Biomass in Central Plant
`Opex_var_GRID_exports_district_scale_USD`	Variable Operation Costs due to electricity exported
`Opex_var_GRID_imports_district_scale_USD`	Variable Operation Costs due to electricity imported
`Opex_var_NG_district_scale_USD`	Variable Operation Costs due to consumption of Natural Gas in Central Plant
`Opex_var_WB_district_scale_USD`	Variable Operation Costs due to consumption of Wet-Biomass in Central Plant

get_optimization_generation_total_performance¶

path: outputs/data/optimization/slave/gen_2/gen_2_total_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_sys_building_scale_USD`	Annualized Capital Costs of building-scale systems
`Capex_a_sys_district_scale_USD`	Annualized Capital Costs of district-scale systems
`Capex_a_sys_USD`	Annualized Capital Costs of all systems
`Capex_total_sys_building_scale_USD`	Total Capital Costs of building-scale systems
`Capex_total_sys_district_scale_USD`	Total Capital Costs of district-scale systems
`Capex_total_sys_USD`	Total Capital Costs of district-scale systems and Decentralized Buildings
`generation`	No. Generation or Iteration (genetic Algorithm)
`GHG_sys_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems
`GHG_sys_district_scale_tonCO2`	Green House Gas Emissions Central Plant
`GHG_sys_tonCO2`	Green House Gas Emissions of all systems
`individual`	No. Individual unique ID
`individual_name`	Name of Individual unique ID
`Opex_a_sys_building_scale_USD`	Operation Costs of building-scale systems
`Opex_a_sys_district_scale_USD`	Operation Costs of district-scale systems
`Opex_a_sys_USD`	Operation Costs of all systems
`TAC_sys_building_scale_USD`	Total Anualized Costs of building-scale systems
`TAC_sys_district_scale_USD`	Total Anualized Costs of district-scale systems
`TAC_sys_USD`	Total Anualized Costs of all systems

get_optimization_generation_total_performance_pareto¶

path: outputs/data/optimization/slave/gen_2/gen_2_total_performance_pareto.csv

The following file is used by these scripts: multi_criteria_analysis

Variable	Description
`Capex_a_sys_building_scale_USD`	Annualized Capital Costs of building-scale systems
`Capex_a_sys_district_scale_USD`	Annualized Capital Costs of district-scale systems
`Capex_a_sys_USD`	Annualized Capital Costs of all systems
`Capex_total_sys_building_scale_USD`	Total Capital Costs of building-scale systems
`Capex_total_sys_district_scale_USD`	Total Capital Costs of district-scale systems
`Capex_total_sys_USD`	Total Capital Costs of district-scale systems and Decentralized Buildings
`generation`	No. Generation or Iteration (genetic Algorithm)
`GHG_sys_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems
`GHG_sys_district_scale_tonCO2`	Green House Gas Emissions Central Plant
`GHG_sys_tonCO2`	Green House Gas Emissions of all systems
`individual`	No. Individual unique ID
`individual_name`	Name of Individual unique ID
`Opex_a_sys_building_scale_USD`	Operation Costs of building-scale systems
`Opex_a_sys_district_scale_USD`	Operation Costs of district-scale systems
`Opex_a_sys_USD`	Operation Costs of all systems
`TAC_sys_building_scale_USD`	Total Anualized Costs of building-scale systems
`TAC_sys_district_scale_USD`	Total Anualized Costs of district-scale systems
`TAC_sys_USD`	Total Anualized Costs of all systems

get_optimization_individuals_in_generation¶

path: outputs/data/optimization/slave/gen_2/generation_2_individuals.csv

The following file is used by these scripts:

Variable	Description
`B01_DH`	TODO
`B02_DH`	TODO
`B03_DH`	TODO
`B04_DH`	TODO
`B05_DH`	TODO
`B06_DH`	TODO
`B07_DH`	TODO
`B08_DH`	TODO
`B09_DH`	TODO
`DB_Cogen`	TODO
`DS_HP`	TODO
`generation`	TODO
`GS_HP`	TODO
`individual`	TODO
`NG_BaseBoiler`	TODO
`NG_Cogen`	TODO
`NG_PeakBoiler`	TODO
`PV`	TODO
`PVT`	TODO
`SC_ET`	TODO
`SC_FP`	TODO
`SS_HP`	TODO
`WB_Cogen`	TODO
`WS_HP`	TODO

get_optimization_network_results_summary¶

path: outputs/data/optimization/network/DH_Network_summary_result_0x1be.csv

The following file is used by these scripts: optimization

Variable	Description
`DATE`	Time stamp (hourly) for one year
`mcpdata_netw_total_kWperC`	Capacity mass flow reate for server cooling of this network
`mdot_DH_netw_total_kgpers`	Total mass flow rate in this district heating network
`Q_DH_losses_W`	Thermal losses of this district heating network
`Q_DHNf_W`	Total thermal demand of district heating network
`Qcdata_netw_total_kWh`	Thermal Demand for server cooling in this network
`T_DHNf_re_K`	Average Temperature of return of this district heating network
`T_DHNf_sup_K`	Average Temperature of supply of this district heating network

get_optimization_slave_building_connectivity¶

path: outputs/data/optimization/slave/gen_2/ind_1_building_connectivity.csv

The following file is used by these scripts:

Variable	Description
`DC_connectivity`	Flag to know if building is connected to District Heating or not
`DH_connectivity`	Flag to know if building is connected to District Cooling or not
`Name`	Unique building ID. It must start with a letter.)

get_optimization_slave_building_scale_performance¶

path: outputs/data/optimization/slave/gen_2/ind_0_buildings_building_scale_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_cooling_building_scale_USD`	Annualized Capital Costs of building-scale systems due to cooling
`Capex_a_heating_building_scale_USD`	Annualized Capital Costs of building-scale systems due to heating
`Capex_total_cooling_building_scale_USD`	Total Capital Costs of building-scale systems due to cooling
`Capex_total_heating_building_scale_USD`	Total Capital Costs of building-scale systems due to heating
`GHG_cooling_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems due to Cooling
`GHG_heating_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems due to Heating
`Opex_fixed_cooling_building_scale_USD`	Fixed Operational Costs of building-scale systems due to cooling
`Opex_fixed_heating_building_scale_USD`	Fixed Operational Costs of building-scale systems due to heating
`Opex_var_cooling_building_scale_USD`	Variable Operational Costs of building-scale systems due to cooling
`Opex_var_heating_building_scale_USD`	Variable Operational Costs of building-scale systems due to heating

get_optimization_slave_cooling_activation_pattern¶

path: outputs/data/optimization/slave/gen_1/ind_2_Cooling_Activation_Pattern.csv

The following file is used by these scripts: optimization

Variable	Description
`Capacity_DailyStorage_WS_cool_district_scale_W`	Installed capacity of the short-term thermal storage
`Capex_a_DailyStorage_WS_cool_district_scale_USD`	Annualized capital costs of the short-term thermal storage
`Capex_total_DailyStorage_WS_cool_district_scale_USD`	Total capital costs of the short-term thermal storage
`Opex_fixed_DailyStorage_WS_cool_district_scale_USD`	Fixed operational costs of the short-term thermal storage
`Q_DailyStorage_content_W`	Thermal energy content of the short-term thermal storage
`Q_DailyStorage_gen_directload_W`	Thermal energy supplied from the short-term thermal storage

get_optimization_slave_district_scale_performance¶

path: outputs/data/optimization/slave/gen_1/ind_2_buildings_district_scale_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_BackupBoiler_NG_district_scale_USD`	Annualized Capital Costs of Back-up Boiler Natural Gas in Central Plant
`Capex_a_BaseBoiler_NG_district_scale_USD`	Annualized Capital Costs of Base load Boiler Natural Gas in Central Plant
`Capex_a_CHP_NG_district_scale_USD`	Annualized Capital Costs of CHP Natural Gas in Central Plant
`Capex_a_DHN_district_scale_USD`	Annualized Capital Costs of District Heating Network
`Capex_a_Furnace_dry_district_scale_USD`	Annualized Capital Costs of CHP Dry-Biomass in Central Plant
`Capex_a_Furnace_wet_district_scale_USD`	Annualized Capital Costs of CHP Wet-Biomass in Central Plant
`Capex_a_GHP_district_scale_USD`	Annualized Capital Costs of Ground-Source Heat-Pump in Central Plant
`Capex_a_GRID_district_scale_USD`	Annualized Capital Costs of connection to local grid
`Capex_a_HP_Lake_district_scale_USD`	Annualized Capital Costs of Lake-Source Heat Pump in Central Plant
`Capex_a_HP_Server_district_scale_USD`	Annualized Capital Costs of Server-Source Heat Pump in Central Plant
`Capex_a_HP_Sewage_district_scale_USD`	Annualized Capital Costs of Sewage-Source Heat Pump in Central Plant
`Capex_a_PeakBoiler_NG_district_scale_USD`	Annualized Capital Costs of Peak Boiler in Central Plant
`Capex_a_PV_district_scale_USD`	Annualized Capital Costs of PV panels
`Capex_a_PVT_district_scale_USD`	Annualized Capital Costs of PVT panels
`Capex_a_SC_ET_district_scale_USD`	Annualized Capital Costs of Solar collectors (evacuated Tubes)
`Capex_a_SC_FP_district_scale_USD`	Annualized Capital Costs of Solar collectors (Flat-Plate)
`Capex_a_SeasonalStorage_WS_district_scale_USD`	Annualized Capital Costs of Seasonal Thermal Storage in Central Plant
`Capex_a_SubstationsHeating_district_scale_USD`	Annualized Capital Costs of Heating Substations
`Capex_total_BackupBoiler_NG_district_scale_USD`	Total Capital Costs of Back-up Boiler Natural Gas in Central Plant
`Capex_total_BaseBoiler_NG_district_scale_USD`	Total Capital Costs of Base load Boiler Natural Gas in Central Plant
`Capex_total_CHP_NG_district_scale_USD`	Total Capital Costs of CHP Natural Gas in Central Plant
`Capex_total_DHN_district_scale_USD`	Total Capital Costs of District Heating Network
`Capex_total_Furnace_dry_district_scale_USD`	Total Capital Costs of CHP Dry-Biomass in Central Plant
`Capex_total_Furnace_wet_district_scale_USD`	Total Capital Costs of CHP Wet-Biomass in Central Plant
`Capex_total_GHP_district_scale_USD`	Total Capital Costs of Ground-Source Heat-Pump in Central Plant
`Capex_total_GRID_district_scale_USD`	Total Capital Costs of connection to local grid
`Capex_total_HP_Lake_district_scale_USD`	Total Capital Costs of Lake-Source Heat Pump in Central Plant
`Capex_total_HP_Server_district_scale_USD`	Total Capital Costs of Server-Source Heat Pump in Central Plant
`Capex_total_HP_Sewage_district_scale_USD`	Total Capital Costs of Sewage-Source Heat Pump in Central Plant
`Capex_total_PeakBoiler_NG_district_scale_USD`	Total Capital Costs of Peak Boiler in Central Plant
`Capex_total_PV_district_scale_USD`	Total Capital Costs of PV panels
`Capex_total_PVT_district_scale_USD`	Total Capital Costs of PVT panels
`Capex_total_SC_ET_district_scale_USD`	Total Capital Costs of Solar collectors (evacuated Tubes)
`Capex_total_SC_FP_district_scale_USD`	Total Capital Costs of Solar collectors (Flat-Plate)
`Capex_total_SeasonalStorage_WS_district_scale_USD`	Total Capital Costs of Seasonal Thermal Storage
`Capex_total_SubstationsHeating_district_scale_USD`	Total Capital Costs of Heating Substations
`GHG_DB_district_scale_tonCO2yr`	Green House Gas Emissions of Dry-Biomass in Central plant
`GHG_GRID_exports_district_scale_tonCO2yr`	Green House Gas Emissions of Electricity Exports in Central Plant
`GHG_GRID_imports_district_scale_tonCO2yr`	Green House Gas Emissions of Electricity Import in Central Plant
`GHG_NG_district_scale_tonCO2yr`	Green House Gas Emissions of Natural Gas in Central plant
`GHG_WB_district_scale_tonCO2yr`	Green House Gas Emissions of Wet-Biomass in Central plant
`Opex_fixed_BackupBoiler_NG_district_scale_USD`	Fixed Operation Costs of Back-up Boiler Natural Gas in Central Plant
`Opex_fixed_BaseBoiler_NG_district_scale_USD`	Fixed Operation Costs of Base load Boiler Natural Gas in Central Plant
`Opex_fixed_CHP_NG_district_scale_USD`	Fixed Operation Costs of CHP Natural Gas in Central Plant
`Opex_fixed_DHN_district_scale_USD`	Fixed Operation Costs of District Heating Network
`Opex_fixed_Furnace_dry_district_scale_USD`	Fixed Operation Costs of CHP Dry-Biomass in Central Plant
`Opex_fixed_Furnace_wet_district_scale_USD`	Fixed Operation Costs of CHP Wet-Biomass in Central Plant
`Opex_fixed_GHP_district_scale_USD`	Fixed Operation Costs of Ground-Source Heat-Pump in Central Plant
`Opex_fixed_GRID_district_scale_USD`	Fixed Operation Costs of connection to local grid
`Opex_fixed_HP_Lake_district_scale_USD`	Fixed Operation Costs of Lake-Source Heat Pump in Central Plant
`Opex_fixed_HP_Server_district_scale_USD`	Fixed Operation Costs of Server-Source Heat Pump in Central Plant
`Opex_fixed_HP_Sewage_district_scale_USD`	Fixed Operation Costs of Sewage-Source Heat Pump in Central Plant
`Opex_fixed_PeakBoiler_NG_district_scale_USD`	Fixed Operation Costs of Peak Boiler in Central Plant
`Opex_fixed_PV_district_scale_USD`	Fixed Operation Costs of PV panels
`Opex_fixed_PVT_district_scale_USD`	Fixed Operation Costs of PVT panels
`Opex_fixed_SC_ET_district_scale_USD`	Fixed Operation Costs of Solar collectors (evacuated Tubes)
`Opex_fixed_SC_FP_district_scale_USD`	Fixed Operation Costs of Solar collectors (Flat-Plate)
`Opex_fixed_SeasonalStorage_WS_district_scale_USD`	Fixed Operation Costs of Seasonal Thermal Storage
`Opex_fixed_SubstationsHeating_district_scale_USD`	Fixed Operation Costs of Heating Substations
`Opex_var_DB_district_scale_USD`	Variable Operation Costs
`Opex_var_GRID_exports_district_scale_USD`	Variable Operation Costs
`Opex_var_GRID_imports_district_scale_USD`	Variable Operation Costs
`Opex_var_NG_district_scale_USD`	Variable Operation Costs
`Opex_var_WB_district_scale_USD`	Variable Operation Costs

get_optimization_slave_electricity_activation_pattern¶

path: outputs/data/optimization/slave/gen_1/ind_1_Electricity_Activation_Pattern.csv

The following file is used by these scripts:

Variable	Description
`DATE`	Time stamp (hourly) for one year
`E_CHP_gen_directload_W`	Electricity Generated to direct load by CHP Natural Gas
`E_CHP_gen_export_W`	Electricity Exported by CHP Natural Gas
`E_Furnace_dry_gen_directload_W`	Electricity Generated to direct load by CHP Dry Biomass
`E_Furnace_dry_gen_export_W`	Electricity Exported by CHP Dry Biomass
`E_Furnace_wet_gen_directload_W`	Electricity Generated to direct load by CHP Wet Biomass
`E_Furnace_wet_gen_export_W`	Electricity Exported by CHP Wet Biomass
`E_GRID_directload_W`	Electricity Imported from the local grid
`E_PV_gen_directload_W`	Electricity Generated to direct load by PV panels
`E_PV_gen_export_W`	Electricity Exported by PV panels
`E_PVT_gen_directload_W`	Electricity Generated to direct load by PVT panels
`E_PVT_gen_export_W`	Electricity Exported by PVT panels
`E_Trigen_gen_directload_W`	Electricity Generated to direct load by Trigen CHP Natural Gas
`E_Trigen_gen_export_W`	Electricity Exported by Trigen CHP Natural Gas

get_optimization_slave_electricity_requirements_data¶

path: outputs/data/optimization/slave/gen_1/ind_1_Electricity_Requirements_Pattern.csv

The following file is used by these scripts:

Variable	Description
`DATE`	Time stamp (hourly) for one year
`E_BackupBoiler_req_W`	Electricity (auxiliary) Required by Back-up Boiler
`E_BackupVCC_AS_req_W`	Electricity Required by Back-up Vapor Compression Chiller (Air-Source)
`E_BaseBoiler_req_W`	Electricity (auxiliary) Required by Base Load Boiler
`E_BaseVCC_AS_req_W`	Electricity Required by Base Load Vapor Compression Chiller (Air-Source)
`E_BaseVCC_WS_req_W`	Electricity Required by Base Load Vapor Compression Chiller (Water-Source)
`E_cs_cre_cdata_req_building_scale_W`	Electricity Required for space cooling, server cooling and refrigeration of building-scale systems
`E_cs_cre_cdata_req_district_scale_W`	Electricity Required for space cooling, server cooling and refrigeration of Buildings Connected to Network
`E_DCN_req_W`	Electricity Required for Chilled water Pumping in District Cooling Network
`E_DHN_req_W`	Electricity Required for Chilled water Pumping in District Heating Network
`E_electricalnetwork_sys_req_W`	Total Electricity Requirements
`E_GHP_req_W`	Electricity Required by Ground-Source Heat Pumps
`E_HP_Lake_req_W`	Electricity Required by Lake-Source Heat Pumps
`E_HP_PVT_req_W`	Electricity Required by Auxiliary Heat Pumps of PVT panels
`E_HP_SC_ET_req_W`	Electricity Required by Auxiliary Heat Pumps of Solar collectors (Evacuated tubes)
`E_HP_SC_FP_req_W`	Electricity Required by Auxiliary Heat Pumps of Solar collectors (Evacuated Flat Plate)
`E_HP_Server_req_W`	Electricity Required by Server-Source Heat Pumps
`E_HP_Sew_req_W`	Electricity Required by Sewage-Source Heat Pumps
`E_hs_ww_req_building_scale_W`	Electricity Required for space heating and hotwater of building-scale systems
`E_hs_ww_req_district_scale_W`	Electricity Required for space heating and hotwater of Buildings Connected to Network
`E_PeakBoiler_req_W`	Electricity (auxiliary) Required by Peak-Boiler
`E_PeakVCC_AS_req_W`	Electricity Required by Peak Vapor Compression Chiller (Air-Source)
`E_PeakVCC_WS_req_W`	Electricity Required by Peak Vapor Compression Chiller (Water-Source)
`E_Storage_charging_req_W`	Electricity Required by Auxiliary Heatpumps for charging Seasonal Thermal Storage
`E_Storage_discharging_req_W`	Electricity Required by Auxiliary Heatpumps for discharging Seasonal Thermal Storage
`Eal_req_W`	Electricity Required for Appliances and Lighting in all Buildings
`Eaux_req_W`	Electricity Required for Fans and others in all Buildings
`Edata_req_W`	Electricity Required for Servers in all Buildings
`Epro_req_W`	Electricity Required for Industrial Processes in all Buildings

get_optimization_slave_heating_activation_pattern¶

path: outputs/data/optimization/slave/gen_2/ind_0_Heating_Activation_Pattern.csv

The following file is used by these scripts:

Variable	Description
`DATE`	Time stamp (hourly) for one year
`E_CHP_gen_W`	Electricity Generation by CHP Natural Gas
`E_Furnace_dry_gen_W`	Electricity Generation by CHP Dry-Biomass
`E_Furnace_wet_gen_W`	Electricity Generation by CHP Wet-Biomass
`E_PVT_gen_W`	Electricity Generation by PVT
`Q_BackupBoiler_gen_directload_W`	Thermal generation of Back-up Boiler to direct load
`Q_BaseBoiler_gen_directload_W`	Thermal generation of Base load Boiler to direct load
`Q_CHP_gen_directload_W`	Thermal generation of CHP Natural Gas to direct load
`Q_districtheating_sys_req_W`	Thermal requirements of District Heating Network
`Q_Furnace_dry_gen_directload_W`	Thermal generation of CHP Dry-Biomass to direct load
`Q_Furnace_wet_gen_directload_W`	Thermal generation of CHP Wet-Biomass to direct load
`Q_GHP_gen_directload_W`	Thermal generation of ground-source heat pump to direct load
`Q_HP_Lake_gen_directload_W`	Thermal generation of Lake-Source Heatpump to direct load
`Q_HP_Server_gen_directload_W`	Thermal generation of Server-Source Heatpump to direct load
`Q_HP_Server_storage_W`	Thermal generation of Server-Source Heatpump to Seasonal Thermal Storage
`Q_HP_Sew_gen_directload_W`	Thermal generation of Sewage-Source Heatpump to direct load
`Q_PeakBoiler_gen_directload_W`	Thermal generation of Peak Boiler to direct load
`Q_PVT_gen_directload_W`	Thermal generation of PVT to direct load
`Q_PVT_gen_storage_W`	Thermal generation of PVT to Seasonal Thermal Storage
`Q_SC_ET_gen_directload_W`	Thermal generation of Solar Collectors (Evacuated Tubes) to direct load
`Q_SC_ET_gen_storage_W`	Thermal generation of Solar Collectors (Evacuated Tubes) to Seasonal Thermal Storage
`Q_SC_FP_gen_directload_W`	Thermal generation of Solar Collectors (Flat Plate) to direct load
`Q_SC_FP_gen_storage_W`	Thermal generation of Solar Collectors (Flat Plate) to Seasonal Thermal Storage
`Q_Storage_gen_directload_W`	Discharge from Storage to Direct Load

get_optimization_slave_total_performance¶

path: outputs/data/optimization/slave/gen_0/ind_2_total_performance.csv

The following file is used by these scripts:

Variable	Description
`Capex_a_sys_building_scale_USD`	Annualized Capital Costs of building-scale systems
`Capex_a_sys_district_scale_USD`	Annualized Capital Costs of district-scale systems
`Capex_a_sys_USD`	Annualized Capital Costs of all systems
`Capex_total_sys_building_scale_USD`	Total Capital Costs of building-scale systems
`Capex_total_sys_district_scale_USD`	Total Capital Costs of district-scale systems
`Capex_total_sys_USD`	Total Capital Costs of all systems
`GHG_sys_building_scale_tonCO2`	Green House Gas Emissions of building-scale systems
`GHG_sys_district_scale_tonCO2`	Green House Gas Emissions Central Plant
`GHG_sys_tonCO2`	Green House Gas Emissions of all systems
`Opex_a_sys_building_scale_USD`	Operation Costs of building-scale systems
`Opex_a_sys_district_scale_USD`	Operation Costs of district-scale systems
`Opex_a_sys_USD`	Operation Costs of all systems
`TAC_sys_building_scale_USD`	Total Anualized Costs of building-scale systems
`TAC_sys_district_scale_USD`	Total Anualized Costs of district-scale systems
`TAC_sys_USD`	Total Anualized Costs of all systems

get_optimization_substations_results_file¶

path: outputs/data/optimization/substations/110011011DH_B001_result.csv

The following file is used by these scripts: optimization

Variable	Description
`A_hex_dhw_design_m2`	Substation heat exchanger area to supply domestic hot water
`A_hex_heating_design_m2`	Substation heat exchanger area to supply space heating
`mdot_DH_result_kgpers`	Substation flow rate on the DH side.
`Q_dhw_W`	Substation heat requirement to supply domestic hot water
`Q_heating_W`	Substation heat requirement to supply space heating
`T_return_DH_result_K`	Substation return temperature of the district heating network
`T_supply_DH_result_K`	Substation supply temperature of the district heating network.

get_optimization_substations_total_file¶

path: outputs/data/optimization/substations/Total_DH_111111111.csv

The following file is used by these scripts:

Variable	Description
`Af_m2`	Conditioned floor area (heated/cooled)
`Aocc_m2`	Occupied floor area (heated/cooled)
`Aroof_m2`	Roof area
`COAL_hs0_kW`	Nominal Coal requirement for space heating supply
`COAL_hs_MWhyr`	Coal requirement for space heating supply
`COAL_ww0_kW`	Nominal Coal requirement for hotwater supply
`COAL_ww_MWhyr`	Coal requirement for hotwater supply
`DC_cdata0_kW`	Nominal district cooling for final data center cooling demand
`DC_cdata_MWhyr`	District cooling for data center cooling demand
`DC_cre0_kW`	Nominal district cooling for refrigeration demand
`DC_cre_MWhyr`	District cooling for refrigeration demand
`DC_cs0_kW`	Nominal district cooling for space cooling demand
`DC_cs_MWhyr`	Energy consumption of space cooling system (if supplied by District Cooling), DC_cs = Qcs_sys / eff_cs
`DH_hs0_kW`	Nominal energy requirement by district heating (space heating supply)
`DH_hs_MWhyr`	Energy requirement by district heating (space heating supply)
`DH_ww0_kW`	Nominal Energy requirement by district heating (hotwater supply)
`DH_ww_MWhyr`	Energy requirement by district heating (hotwater supply)
`E_cdata0_kW`	Nominal Data centre cooling specific electricity consumption.
`E_cdata_MWhyr`	Electricity consumption due to data center cooling
`E_cre0_kW`	Nominal Refrigeration system electricity consumption.
`E_cre_MWhyr`	Electricity consumption due to refrigeration
`E_cs0_kW`	Nominal Cooling system electricity consumption.
`E_cs_MWhyr`	Energy consumption of cooling system (if supplied by electricity grid), E_cs = Qcs_sys / eff_cs
`E_hs0_kW`	Nominal Heating system electricity consumption.
`E_hs_MWhyr`	Electricity consumption due to space heating
`E_sys0_kW`	Nominal end-use electricity demand
`E_sys_MWhyr`	End-use total electricity consumption, E_sys = Eve + Ea + El + Edata + Epro + Eaux + Ev
`E_ww0_kW`	Nominal Domestic hot water electricity consumption.
`E_ww_MWhyr`	Electricity consumption due to hot water
`Ea0_kW`	Nominal end-use electricity for appliances
`Ea_MWhyr`	End-use electricity for appliances
`Eal0_kW`	Nominal Total net electricity for all sources and sinks.
`Eal_MWhyr`	End-use electricity consumption of appliances and lighting, Eal = El_W + Ea_W
`Eaux0_kW`	Nominal Auxiliary electricity consumption.
`Eaux_MWhyr`	End-use auxiliary electricity consumption, Eaux = Eaux_fw + Eaux_ww + Eaux_cs + Eaux_hs + Ehs_lat_aux
`Edata0_kW`	Nominal Data centre electricity consumption.
`Edata_MWhyr`	Electricity consumption for data centers
`El0_kW`	Nominal end-use electricity for lights
`El_MWhyr`	End-use electricity for lights
`Epro0_kW`	Nominal Industrial processes electricity consumption.
`Epro_MWhyr`	Electricity supplied to industrial processes
`Ev0_kW`	Nominal end-use electricity for electric vehicles
`Ev_MWhyr`	End-use electricity for electric vehicles
`Eve0_kW`	Nominal end-use electricity for ventilation
`Eve_MWhyr`	End-use electricity for ventilation
`GFA_m2`	Gross floor area
`GRID0_kW`	Nominal Grid electricity consumption
`GRID_a0_kW`	Nominal grid electricity requirements for appliances
`GRID_a_MWhyr`	Grid electricity requirements for appliances
`GRID_aux0_kW`	Nominal grid electricity requirements for auxiliary loads
`GRID_aux_MWhyr`	Grid electricity requirements for auxiliary loads
`GRID_cdata0_kW`	Nominal grid electricity requirements for servers cooling
`GRID_cdata_MWhyr`	Grid electricity requirements for servers cooling
`GRID_cre0_kW`	Nominal grid electricity requirements for refrigeration
`GRID_cre_MWhyr`	Grid electricity requirements for refrigeration
`GRID_cs0_kW`	Nominal grid electricity requirements for space cooling
`GRID_cs_MWhyr`	Grid electricity requirements for space cooling
`GRID_data0_kW`	Nominal grid electricity requirements for servers
`GRID_data_MWhyr`	Grid electricity requirements for servers
`GRID_hs0_kW`	Nominal grid electricity requirements for space heating
`GRID_hs_MWhyr`	Grid electricity requirements for space heating
`GRID_l0_kW`	Nominal grid electricity consumption for lights
`GRID_l_MWhyr`	Grid electricity requirements for lights
`GRID_MWhyr`	Grid electricity consumption, GRID = GRID_a + GRID_l + GRID_v + GRID_ve + GRID_data + GRID_pro + GRID_aux + GRID_ww + GRID_cs + GRID_hs + GRID_cdata + GRID_cre
`GRID_pro0_kW`	Nominal grid electricity requirements for industrial processes
`GRID_pro_MWhyr`	Grid electricity requirements for industrial processes
`GRID_v0_kW`	Nominal grid electricity consumption for electric vehicles
`GRID_v_MWhyr`	Grid electricity requirements for electric vehicles
`GRID_ve0_kW`	Nominal grid electricity consumption for ventilatioon
`GRID_ve_MWhyr`	Grid electricity requirements for ventilatioon
`GRID_ww0_kW`	Nominal grid electricity requirements for hot water supply
`GRID_ww_MWhyr`	Grid electricity requirements for hot water supply
`Name`	Unique building ID. It must start with a letter.
`NG_hs0_kW`	Nominal NG requirement for space heating supply
`NG_hs_MWhyr`	NG requirement for space heating supply
`NG_ww0_kW`	Nominal NG requirement for hotwater supply
`NG_ww_MWhyr`	NG requirement for hotwater supply
`OIL_hs0_kW`	Nominal OIL requirement for space heating supply
`OIL_hs_MWhyr`	OIL requirement for space heating supply
`OIL_ww0_kW`	Nominal OIL requirement for hotwater supply
`OIL_ww_MWhyr`	OIL requirement for hotwater supply
`people0`	Nominal occupancy
`PV0_kW`	Nominal PV electricity consumption
`PV_MWhyr`	PV electricity consumption
`QC_sys0_kW`	Nominal Total system cooling demand.
`QC_sys_MWhyr`	Total system cooling demand, QC_sys = Qcs_sys + Qcdata_sys + Qcre_sys + Qcpro_sys
`Qcdata0_kW`	Nominal Data centre cooling demand.
`Qcdata_MWhyr`	Data centre cooling demand
`Qcdata_sys0_kW`	Nominal end-use data center cooling demand
`Qcdata_sys_MWhyr`	End-use data center cooling demand
`Qcpro_sys0_kW`	Nominal process cooling demand.
`Qcpro_sys_MWhyr`	Yearly processes cooling demand.
`Qcre0_kW`	Nominal Refrigeration cooling demand.
`Qcre_MWhyr`	Refrigeration cooling demand for the system
`Qcre_sys0_kW`	Nominal refrigeration cooling demand
`Qcre_sys_MWhyr`	End-use refrigeration demand
`Qcs0_kW`	Nominal Total cooling demand.
`Qcs_dis_ls0_kW`	Nominal Cooling distribution losses.
`Qcs_dis_ls_MWhyr`	Cooling distribution losses
`Qcs_em_ls0_kW`	Nominal Cooling emission losses.
`Qcs_em_ls_MWhyr`	Cooling emission losses
`Qcs_lat_ahu0_kW`	Nominal AHU latent cooling demand.
`Qcs_lat_ahu_MWhyr`	AHU latent cooling demand
`Qcs_lat_aru0_kW`	Nominal ARU latent cooling demand.
`Qcs_lat_aru_MWhyr`	ARU latent cooling demand
`Qcs_lat_sys0_kW`	Nominal System latent cooling demand.
`Qcs_lat_sys_MWhyr`	System latent cooling demand
`Qcs_MWhyr`	Total cooling demand
`Qcs_sen_ahu0_kW`	Nominal AHU system cooling demand.
`Qcs_sen_ahu_MWhyr`	Sensible cooling demand in AHU
`Qcs_sen_aru0_kW`	Nominal ARU system cooling demand.
`Qcs_sen_aru_MWhyr`	ARU system cooling demand
`Qcs_sen_scu0_kW`	Nominal SCU system cooling demand.
`Qcs_sen_scu_MWhyr`	SCU system cooling demand
`Qcs_sen_sys0_kW`	Nominal Sensible system cooling demand.
`Qcs_sen_sys_MWhyr`	Total sensible cooling demand
`Qcs_sys0_kW`	Nominal end-use space cooling demand
`Qcs_sys_ahu0_kW`	Nominal AHU system cooling demand.
`Qcs_sys_ahu_MWhyr`	AHU system cooling demand
`Qcs_sys_aru0_kW`	Nominal ARU system cooling demand.
`Qcs_sys_aru_MWhyr`	ARU system cooling demand
`Qcs_sys_MWhyr`	End-use space cooling demand, Qcs_sys = Qcs_sen_sys + Qcs_lat_sys + Qcs_em_ls + Qcs_dis_ls
`Qcs_sys_scu0_kW`	Nominal SCU system cooling demand.
`Qcs_sys_scu_MWhyr`	SCU system cooling demand
`QH_sys0_kW`	Nominal total building heating demand.
`QH_sys_MWhyr`	Total building heating demand
`Qhpro_sys0_kW`	Nominal process heating demand.
`Qhpro_sys_MWhyr`	Yearly processes heating demand.
`Qhs0_kW`	Nominal space heating demand.
`Qhs_dis_ls0_kW`	Nominal Heating system distribution losses.
`Qhs_dis_ls_MWhyr`	Heating system distribution losses
`Qhs_em_ls0_kW`	Nominal Heating emission losses.
`Qhs_em_ls_MWhyr`	Heating system emission losses
`Qhs_lat_ahu0_kW`	Nominal AHU latent heating demand.
`Qhs_lat_ahu_MWhyr`	AHU latent heating demand
`Qhs_lat_aru0_kW`	Nominal ARU latent heating demand.
`Qhs_lat_aru_MWhyr`	ARU latent heating demand
`Qhs_lat_sys0_kW`	Nominal System latent heating demand.
`Qhs_lat_sys_MWhyr`	System latent heating demand
`Qhs_MWhyr`	Total space heating demand.
`Qhs_sen_ahu0_kW`	Nominal AHU sensible heating demand.
`Qhs_sen_ahu_MWhyr`	AHU sensible heating demand
`Qhs_sen_aru0_kW`	ARU sensible heating demand
`Qhs_sen_aru_MWhyr`	ARU sensible heating demand
`Qhs_sen_shu0_kW`	Nominal SHU sensible heating demand.
`Qhs_sen_shu_MWhyr`	SHU sensible heating demand
`Qhs_sen_sys0_kW`	Nominal HVAC systems sensible heating demand.
`Qhs_sen_sys_MWhyr`	SHU sensible heating demand
`Qhs_sys0_kW`	Nominal end-use space heating demand
`Qhs_sys_ahu0_kW`	Nominal AHU sensible heating demand.
`Qhs_sys_ahu_MWhyr`	AHU system heating demand
`Qhs_sys_aru0_kW`	Nominal ARU sensible heating demand.
`Qhs_sys_aru_MWhyr`	ARU sensible heating demand
`Qhs_sys_MWhyr`	End-use space heating demand, Qhs_sys = Qhs_sen_sys + Qhs_em_ls + Qhs_dis_ls
`Qhs_sys_shu0_kW`	Nominal SHU sensible heating demand.
`Qhs_sys_shu_MWhyr`	SHU sensible heating demand
`Qww0_kW`	Nominal DHW heating demand.
`Qww_MWhyr`	DHW heating demand
`Qww_sys0_kW`	Nominal end-use hotwater demand
`Qww_sys_MWhyr`	End-use hotwater demand
`SOLAR_hs0_kW`	Nominal solar thermal energy requirement for space heating supply
`SOLAR_hs_MWhyr`	Solar thermal energy requirement for space heating supply
`SOLAR_ww0_kW`	Nominal solar thermal energy requirement for hotwater supply
`SOLAR_ww_MWhyr`	Solar thermal energy requirement for hotwater supply
`WOOD_hs0_kW`	Nominal WOOD requirement for space heating supply
`WOOD_hs_MWhyr`	WOOD requirement for space heating supply
`WOOD_ww0_kW`	Nominal WOOD requirement for hotwater supply
`WOOD_ww_MWhyr`	WOOD requirement for hotwater supply

get_radiation_building¶

path: outputs/data/solar-radiation/{building}_radiation.csv

The following file is used by these scripts: demand, photovoltaic, photovoltaic_thermal, solar_collector

Variable	Description
`Date`	Date and time in hourly steps
`roofs_top_kW`	solar incident on the roof tops
`roofs_top_m2`	roof top area
`walls_east_kW`	solar incident on the east facing facades excluding windows
`walls_east_m2`	area of the east facing facades excluding windows
`walls_north_kW`	solar incident on the north facing facades excluding windows
`walls_north_m2`	area of the north facing facades excluding windows
`walls_south_kW`	solar incident on the south facing facades excluding windows
`walls_south_m2`	area of the south facing facades excluding windows
`walls_west_kW`	solar incident on the west facing facades excluding windows
`walls_west_m2`	area of the south facing facades excluding windows
`windows_east_kW`	solar incident on windows on the south facing facades
`windows_east_m2`	window area on the east facing facades
`windows_north_kW`	solar incident on windows on the south facing facades
`windows_north_m2`	window area on the north facing facades
`windows_south_kW`	solar incident on windows on the south facing facades
`windows_south_m2`	window area on the south facing facades
`windows_west_kW`	solar incident on windows on the west facing facades
`windows_west_m2`	window area on the west facing facades

get_radiation_building_sensors¶

path: outputs/data/solar-radiation/B001_insolation_Whm2.json

The following file is used by these scripts: demand, photovoltaic, photovoltaic_thermal, solar_collector

Variable	Description
`srf0`	TODO

get_radiation_materials¶

path: outputs/data/solar-radiation/buidling_materials.csv

The following file is used by these scripts:

Variable	Description
`G_win`	Solar heat gain coefficient. Defined according to ISO 13790.
`Name`	Unique building ID. It must start with a letter.
`r_roof`	Reflectance in the Red spectrum. Defined according Radiance. (long-wave)
`r_wall`	Reflectance in the Red spectrum. Defined according Radiance. (long-wave)
`type_base`	Basement floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_floor`	Internal floor construction assembly (relates to “code” in ENVELOPE assemblies)
`type_roof`	Roof construction assembly (relates to “code” in ENVELOPE assemblies)
`type_wall`	External wall construction assembly (relates to “code” in ENVELOPE assemblies)
`type_win`	Window assembly (relates to “code” in ENVELOPE assemblies)

get_radiation_metadata¶

path: outputs/data/solar-radiation/B001_geometry.csv

The following file is used by these scripts: demand, photovoltaic, photovoltaic_thermal, solar_collector

Variable	Description
`AREA_m2`	Surface area.
`BUILDING`	Unique building ID. It must start with a letter.
`intersection`	flag to indicate whether this surface is intersecting with another surface (0: no intersection, 1: intersected)
`orientation`	Orientation of the surface (north/east/south/west/top)
`SURFACE`	Unique surface ID for each building exterior surface.
`TYPE`	Surface typology.
`Xcoor`	Describes the position of the x vector.
`Xdir`	Directional scalar of the x vector.
`Ycoor`	Describes the position of the y vector.
`Ydir`	Directional scalar of the y vector.
`Zcoor`	Describes the position of the z vector.
`Zdir`	Directional scalar of the z vector.

get_schedule_model_file¶

path: outputs/data/occupancy/B001.csv

The following file is used by these scripts: demand

Variable	Description
`DATE`	Time stamp for each day of the year ascending in hourly intervals
`Ea_W`	Electrical load due to processes
`Ed_W`	Electrical load due to servers/data centers
`El_W`	Electrical load due to lighting
`Epro_W`	Electrical load due to processes
`people_p`	Number of people in the building
`Qcpro_W`	Process cooling load
`Qcre_W`	Cooling load due to cool room refrigeration
`Qhpro_W`	Process heat load
`Qs_W`	Sensible heat load of people
`Tcs_set_C`	Set point temperature of space cooling system
`Ths_set_C`	Set point temperature of space heating system
`Ve_lps`	Ventilation rate
`Vw_lph`	Fresh water consumption (includes cold and hot water)
`Vww_lph`	Domestic hot water consumption
`X_gh`	Moisture released by occupants

get_sewage_heat_potential¶

path: outputs/data/potentials/Sewage_heat_potential.csv

The following file is used by these scripts: optimization

Variable	Description
`mww_zone_kWperC`	heat capacity of total sewage in a zone
`Qsw_kW`	heat extracted from sewage flows
`T_in_HP_C`	Inlet temperature of the sweage heapump
`T_in_sw_C`	Inlet temperature of sewage flows
`T_out_HP_C`	Outlet temperature of the sewage heatpump
`T_out_sw_C`	Outlet temperature of sewage flows
`Ts_C`	Average temperature of sewage flows

get_thermal_demand_csv_file¶

path: outputs/data/thermal-network/DH__thermal_demand_per_building_W.csv

The following file is used by these scripts:

Variable	Description
`B01`	Thermal demand for building B01 at each simulation time step

get_thermal_network_edge_list_file¶

path: outputs/data/thermal-network/DH__metadata_edges.csv

The following file is used by these scripts: optimization

Variable	Description
`D_int_m`	Internal pipe diameter for the nominal diameter
`length_m`	Length of each pipe in the network
`Name`	Unique network pipe ID.
`Pipe_DN`	Nominal pipe diameter (e.g. DN100 refers to pipes of approx. 100 mm in diameter)
`Type_mat`	Material of the pipes

get_thermal_network_edge_node_matrix_file¶

path: outputs/data/thermal-network/{network_type}__EdgeNode.csv

The following file is used by these scripts: thermal_network

Variable	Description
`NODE`	Names of the nodes in the network
`PIPE0`	Indicates the direction of flow of PIPE0 with respect to each node NODEn: if equal to PIPE0 and NODEn are not connected / if equal to 1 PIPE0 enters NODEn / if equal to -1 PIPE0 leaves NODEn

get_thermal_network_layout_massflow_edges_file¶

path: outputs/data/thermal-network/DH__massflow_edges_kgs.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	Mass flow rate in pipe PIPE0 at each time step

get_thermal_network_layout_massflow_nodes_file¶

path: outputs/data/thermal-network/DH__massflow_nodes_kgs.csv

The following file is used by these scripts:

Variable	Description
`NODE0`	Mass flow rate in node NODE0 at each time step

get_thermal_network_node_types_csv_file¶

path: outputs/data/thermal-network/DH__metadata_nodes.csv

The following file is used by these scripts:

Variable	Description
`Building`	Unique building ID. It must start with a letter.
`Name`	Unique network node ID.
`Type`	Type of node: “PLANT” / “CONSUMER” / “NONE” (if it is neither)

get_thermal_network_plant_heat_requirement_file¶

path: outputs/data/thermal-network/DH__plant_thermal_load_kW.csv

The following file is used by these scripts:

Variable	Description
`thermal_load_kW`	Thermal load supplied by the plant at each time step

get_thermal_network_pressure_losses_edges_file¶

path: outputs/data/thermal-network/DH__pressure_losses_edges_kW.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	Pressure losses at pipe PIPE0 at each time step

get_thermal_network_substation_ploss_file¶

path: outputs/data/thermal-network/DH__pumping_load_due_to_substations_kW.csv

The following file is used by these scripts:

Variable	Description
`B01`	Pumping load at building substation B01 for each timestep

get_thermal_network_velocity_edges_file¶

path: outputs/data/thermal-network/DH__velocity_edges_mpers.csv

The following file is used by these scripts:

Variable	Description
`PIPE0`	Flow velocity of heating/cooling medium in pipe PIPE0

get_total_demand¶

path: outputs/data/demand/Total_demand.csv

The following file is used by these scripts: decentralized, emissions, network_layout, system_costs, optimization, sewage_potential, thermal_network

Variable	Description
`Af_m2`	Conditioned floor area (heated/cooled)
`Aocc_m2`	Occupied floor area (heated/cooled)
`Aroof_m2`	Roof area
`COAL_hs0_kW`	Nominal Coal requirement for space heating supply
`COAL_hs_MWhyr`	Coal requirement for space heating supply
`COAL_ww0_kW`	Nominal Coal requirement for hotwater supply
`COAL_ww_MWhyr`	Coal requirement for hotwater supply
`DC_cdata0_kW`	Nominal district cooling for final data center cooling demand
`DC_cdata_MWhyr`	District cooling for data center cooling demand
`DC_cre0_kW`	Nominal district cooling for refrigeration demand
`DC_cre_MWhyr`	District cooling for refrigeration demand
`DC_cs0_kW`	Nominal district cooling for space cooling demand
`DC_cs_MWhyr`	Energy consumption of space cooling system (if supplied by District Cooling), DC_cs = Qcs_sys / eff_cs
`DH_hs0_kW`	Nominal energy requirement by district heating (space heating supply)
`DH_hs_MWhyr`	Energy requirement by district heating (space heating supply)
`DH_ww0_kW`	Nominal Energy requirement by district heating (hotwater supply)
`DH_ww_MWhyr`	Energy requirement by district heating (hotwater supply)
`E_cdata0_kW`	Nominal Data centre cooling specific electricity consumption.
`E_cdata_MWhyr`	Electricity consumption due to data center cooling
`E_cre0_kW`	Nominal Refrigeration system electricity consumption.
`E_cre_MWhyr`	Electricity consumption due to refrigeration
`E_cs0_kW`	Nominal Cooling system electricity consumption.
`E_cs_MWhyr`	Energy consumption of cooling system (if supplied by electricity grid), E_cs = Qcs_sys / eff_cs
`E_hs0_kW`	Nominal Heating system electricity consumption.
`E_hs_MWhyr`	Electricity consumption due to space heating
`E_sys0_kW`	Nominal end-use electricity demand
`E_sys_MWhyr`	End-use total electricity consumption, E_sys = Eve + Ea + El + Edata + Epro + Eaux + Ev
`E_ww0_kW`	Nominal Domestic hot water electricity consumption.
`E_ww_MWhyr`	Electricity consumption due to hot water
`Ea0_kW`	Nominal end-use electricity for appliances
`Ea_MWhyr`	End-use electricity for appliances
`Eal0_kW`	Nominal Total net electricity for all sources and sinks.
`Eal_MWhyr`	End-use electricity consumption of appliances and lighting, Eal = El_W + Ea_W
`Eaux0_kW`	Nominal Auxiliary electricity consumption.
`Eaux_MWhyr`	End-use auxiliary electricity consumption, Eaux = Eaux_fw + Eaux_ww + Eaux_cs + Eaux_hs + Ehs_lat_aux
`Edata0_kW`	Nominal Data centre electricity consumption.
`Edata_MWhyr`	Electricity consumption for data centers
`El0_kW`	Nominal end-use electricity for lights
`El_MWhyr`	End-use electricity for lights
`Epro0_kW`	Nominal Industrial processes electricity consumption.
`Epro_MWhyr`	Electricity supplied to industrial processes
`Ev0_kW`	Nominal end-use electricity for electric vehicles
`Ev_MWhyr`	End-use electricity for electric vehicles
`Eve0_kW`	Nominal end-use electricity for ventilation
`Eve_MWhyr`	End-use electricity for ventilation
`GFA_m2`	Gross floor area
`GRID0_kW`	Nominal Grid electricity consumption
`GRID_a0_kW`	Nominal grid electricity requirements for appliances
`GRID_a_MWhyr`	Grid electricity requirements for appliances
`GRID_aux0_kW`	Nominal grid electricity requirements for auxiliary loads
`GRID_aux_MWhyr`	Grid electricity requirements for auxiliary loads
`GRID_cdata0_kW`	Nominal grid electricity requirements for servers cooling
`GRID_cdata_MWhyr`	Grid electricity requirements for servers cooling
`GRID_cre0_kW`	Nominal grid electricity requirements for refrigeration
`GRID_cre_MWhyr`	Grid electricity requirements for refrigeration
`GRID_cs0_kW`	Nominal grid electricity requirements for space cooling
`GRID_cs_MWhyr`	Grid electricity requirements for space cooling
`GRID_data0_kW`	Nominal grid electricity requirements for servers
`GRID_data_MWhyr`	Grid electricity requirements for servers
`GRID_hs0_kW`	Nominal grid electricity requirements for space heating
`GRID_hs_MWhyr`	Grid electricity requirements for space heating
`GRID_l0_kW`	Nominal grid electricity consumption for lights
`GRID_l_MWhyr`	Grid electricity requirements for lights
`GRID_MWhyr`	Grid electricity consumption, GRID_a + GRID_l + GRID_v + GRID_ve + GRID_data + GRID_pro + GRID_aux + GRID_ww + GRID_cs + GRID_hs + GRID_cdata + GRID_cre
`GRID_pro0_kW`	Nominal grid electricity requirements for industrial processes
`GRID_pro_MWhyr`	Grid electricity requirements for industrial processes
`GRID_v0_kW`	Nominal grid electricity consumption for electric vehicles
`GRID_v_MWhyr`	Grid electricity requirements for electric vehicles
`GRID_ve0_kW`	Nominal grid electricity consumption for ventilation
`GRID_ve_MWhyr`	Grid electricity requirements for ventilation
`GRID_ww0_kW`	Nominal grid electricity requirements for hot water supply
`GRID_ww_MWhyr`	Grid electricity requirements for hot water supply
`Name`	Unique building ID. It must start with a letter.
`NG_hs0_kW`	Nominal NG requirement for space heating supply
`NG_hs_MWhyr`	NG requirement for space heating supply
`NG_ww0_kW`	Nominal NG requirement for hotwater supply
`NG_ww_MWhyr`	NG requirement for hotwater supply
`OIL_hs0_kW`	Nominal OIL requirement for space heating supply
`OIL_hs_MWhyr`	OIL requirement for space heating supply
`OIL_ww0_kW`	Nominal OIL requirement for hotwater supply
`OIL_ww_MWhyr`	OIL requirement for hotwater supply
`people0`	Nominal occupancy
`PV0_kW`	Nominal PV electricity consumption
`PV_MWhyr`	PV electricity consumption
`QC_sys0_kW`	Nominal Total system cooling demand.
`QC_sys_MWhyr`	Total energy demand for cooling, QC_sys = Qcs_sys + Qcdata_sys + Qcre_sys + Qcpro_sys
`Qcdata0_kW`	Nominal Data centre cooling demand.
`Qcdata_MWhyr`	Data centre cooling demand
`Qcdata_sys0_kW`	Nominal end-use data center cooling demand
`Qcdata_sys_MWhyr`	End-use data center cooling demand
`Qcpro_sys0_kW`	Nominal process cooling demand.
`Qcpro_sys_MWhyr`	Yearly processes cooling demand.
`Qcre0_kW`	Nominal Refrigeration cooling demand.
`Qcre_MWhyr`	Refrigeration cooling demand for the system
`Qcre_sys0_kW`	Nominal refrigeration cooling demand
`Qcre_sys_MWhyr`	End-use refrigeration demand
`Qcs0_kW`	Nominal Total cooling demand.
`Qcs_dis_ls0_kW`	Nominal Cooling distribution losses.
`Qcs_dis_ls_MWhyr`	Cooling distribution losses
`Qcs_em_ls0_kW`	Nominal Cooling emission losses.
`Qcs_em_ls_MWhyr`	Cooling emission losses
`Qcs_lat_ahu0_kW`	Nominal AHU latent cooling demand.
`Qcs_lat_ahu_MWhyr`	AHU latent cooling demand
`Qcs_lat_aru0_kW`	Nominal ARU latent cooling demand.
`Qcs_lat_aru_MWhyr`	ARU latent cooling demand
`Qcs_lat_sys0_kW`	Nominal System latent cooling demand.
`Qcs_lat_sys_MWhyr`	Latent cooling demand
`Qcs_MWhyr`	Total cooling demand
`Qcs_sen_ahu0_kW`	Nominal AHU system cooling demand.
`Qcs_sen_ahu_MWhyr`	AHU system cooling demand
`Qcs_sen_aru0_kW`	Nominal ARU system cooling demand.
`Qcs_sen_aru_MWhyr`	ARU system cooling demand
`Qcs_sen_scu0_kW`	Nominal SCU system cooling demand.
`Qcs_sen_scu_MWhyr`	SCU system cooling demand
`Qcs_sen_sys0_kW`	Nominal Sensible system cooling demand.
`Qcs_sen_sys_MWhyr`	Sensible system cooling demand
`Qcs_sys0_kW`	Nominal end-use space cooling demand
`Qcs_sys_ahu0_kW`	Nominal AHU system cooling demand.
`Qcs_sys_ahu_MWhyr`	AHU system cooling demand
`Qcs_sys_aru0_kW`	Nominal ARU system cooling demand.
`Qcs_sys_aru_MWhyr`	ARU system cooling demand
`Qcs_sys_MWhyr`	End-use space cooling demand, Qcs_sys = Qcs_sen_sys + Qcs_lat_sys + Qcs_em_ls + Qcs_dis_ls
`Qcs_sys_scu0_kW`	Nominal SCU system cooling demand.
`Qcs_sys_scu_MWhyr`	SCU system cooling demand
`QH_sys0_kW`	Nominal total building heating demand.
`QH_sys_MWhyr`	Total energy demand for heating
`Qhpro_sys0_kW`	Nominal process heating demand.
`Qhpro_sys_MWhyr`	Yearly processes heating demand.
`Qhs0_kW`	Nominal Total heating demand.
`Qhs_dis_ls0_kW`	Nominal Heating system distribution losses.
`Qhs_dis_ls_MWhyr`	Heating system distribution losses
`Qhs_em_ls0_kW`	Nominal Heating emission losses.
`Qhs_em_ls_MWhyr`	Heating system emission losses
`Qhs_lat_ahu0_kW`	Nominal AHU latent heating demand.
`Qhs_lat_ahu_MWhyr`	AHU latent heating demand
`Qhs_lat_aru0_kW`	Nominal ARU latent heating demand.
`Qhs_lat_aru_MWhyr`	ARU latent heating demand
`Qhs_lat_sys0_kW`	Nominal System latent heating demand.
`Qhs_lat_sys_MWhyr`	System latent heating demand
`Qhs_MWhyr`	Total heating demand
`Qhs_sen_ahu0_kW`	Nominal AHU sensible heating demand.
`Qhs_sen_ahu_MWhyr`	Sensible heating demand in AHU
`Qhs_sen_aru0_kW`	ARU sensible heating demand
`Qhs_sen_aru_MWhyr`	ARU sensible heating demand
`Qhs_sen_shu0_kW`	Nominal SHU sensible heating demand.
`Qhs_sen_shu_MWhyr`	SHU sensible heating demand
`Qhs_sen_sys0_kW`	Nominal HVAC systems sensible heating demand.
`Qhs_sen_sys_MWhyr`	Sensible heating demand
`Qhs_sys0_kW`	Nominal end-use space heating demand
`Qhs_sys_ahu0_kW`	Nominal AHU sensible heating demand.
`Qhs_sys_ahu_MWhyr`	AHU system heating demand
`Qhs_sys_aru0_kW`	Nominal ARU sensible heating demand.
`Qhs_sys_aru_MWhyr`	ARU sensible heating demand
`Qhs_sys_MWhyr`	End-use space heating demand, Qhs_sys = Qhs_sen_sys + Qhs_em_ls + Qhs_dis_ls
`Qhs_sys_shu0_kW`	Nominal SHU sensible heating demand.
`Qhs_sys_shu_MWhyr`	SHU sensible heating demand
`Qww0_kW`	Nominal DHW heating demand.
`Qww_MWhyr`	DHW heating demand
`Qww_sys0_kW`	Nominal end-use hotwater demand
`Qww_sys_MWhyr`	End-use hotwater demand
`SOLAR_hs0_kW`	Nominal solar thermal energy requirement for space heating supply
`SOLAR_hs_MWhyr`	Solar thermal energy requirement for space heating supply
`SOLAR_ww0_kW`	Nominal solar thermal energy requirement for hotwater supply
`SOLAR_ww_MWhyr`	Solar thermal energy requirement for hotwater supply
`WOOD_hs0_kW`	Nominal WOOD requirement for space heating supply
`WOOD_hs_MWhyr`	WOOD requirement for space heating supply
`WOOD_ww0_kW`	Nominal WOOD requirement for hotwater supply
`WOOD_ww_MWhyr`	WOOD requirement for hotwater supply

get_water_body_potential¶

path: outputs/data/potentials/Water_body_potential.csv

The following file is used by these scripts: optimization

Variable	Description
`QLake_kW`	thermal potential from water body
`Ts_C`	average temperature of the water body

get_weather_file¶

path: inputs/weather/weather.epw

The following file is used by these scripts: decentralized, demand, optimization, photovoltaic, photovoltaic_thermal, radiation, schedule_maker, shallow_geothermal_potential, solar_collector, thermal_network

Variable	Description
`aerosol_opt_thousandths (index = 29)`	TODO
`Albedo (index = 32)`	TODO
`atmos_Pa (index = 9)`	TODO
`ceiling_hgt_m (index = 25)`	TODO
`datasource (index = 5)`	TODO
`day (index = 2)`	TODO
`days_last_snow (index = 31)`	TODO
`dewpoint_C (index = 7)`	TODO
`difhorillum_lux (index = 18)`	TODO
`difhorrad_Whm2 (index = 15)`	TODO
`dirnorillum_lux (index = 17)`	TODO
`dirnorrad_Whm2 (index = 14)`	TODO
`drybulb_C (index = 6)`	TODO
`extdirrad_Whm2 (index = 11)`	TODO
`exthorrad_Whm2 (index = 10)`	TODO
`glohorillum_lux (index = 16)`	TODO
`glohorrad_Whm2 (index = 13)`	TODO
`horirsky_Whm2 (index = 12)`	TODO
`hour (index = 3)`	TODO
`liq_precip_depth_mm (index = 33)`	TODO
`liq_precip_rate_Hour (index = 34)`	TODO
`minute (index = 4)`	TODO
`month (index = 1)`	TODO
`opaqskycvr_tenths (index = 23)`	TODO
`precip_wtr_mm (index = 28)`	TODO
`presweathcodes (index = 27)`	TODO
`presweathobs (index = 26)`	TODO
`relhum_percent (index = 8)`	TODO
`snowdepth_cm (index = 30)`	TODO
`totskycvr_tenths (index = 22)`	TODO
`visibility_km (index = 24)`	TODO
`winddir_deg (index = 20)`	TODO
`windspd_ms (index = 21)`	TODO
`year (index = 0)`	TODO
`zenlum_lux (index = 19)`	TODO

PV_metadata_results¶

path: outputs/data/potentials/solar/B001_PV_sensors.csv

The following file is used by these scripts:

Variable	Description
`area_installed_module_m2`	The area of the building suface covered by one solar panel
`AREA_m2`	Surface area.
`array_spacing_m`	Spacing between solar arrays.
`B_deg`	Tilt angle of the installed solar panels
`BUILDING`	Unique building ID. It must start with a letter.
`CATB`	Category according to the tilt angle of the panel
`CATGB`	Category according to the annual radiation on the panel surface
`CATteta_z`	Category according to the surface azimuth of the panel
`intersection`	flag to indicate whether this surface is intersecting with another surface (0: no intersection, 1: intersected)
`orientation`	Orientation of the surface (north/east/south/west/top)
`SURFACE`	Unique surface ID for each building exterior surface.
`surface`	Unique surface ID for each building exterior surface.
`surface_azimuth_deg`	Azimuth angle of the panel surface e.g. south facing = 180 deg
`tilt_deg`	Tilt angle of roof or walls
`total_rad_Whm2`	Total radiatiative potential of a given surfaces area.
`TYPE`	Surface typology.
`type_orientation`	Concatenated surface type and orientation.
`Xcoor`	Describes the position of the x vector.
`Xdir`	Directional scalar of the x vector.
`Ycoor`	Describes the position of the y vector.
`Ydir`	Directional scalar of the y vector.
`Zcoor`	Describes the position of the z vector.
`Zdir`	Directional scalar of the z vector.

PV_results¶

path: outputs/data/potentials/solar/B001_PV.csv

The following file is used by these scripts:

Variable	Description
`Area_PV_m2`	Total area of investigated collector.
`Date`	Date and time in hourly steps.
`E_PV_gen_kWh`	Total electricity generated by the collector.
`PV_roofs_top_E_kWh`	Electricity production from photovoltaic panels on roof tops
`PV_roofs_top_m2`	Collector surface area on roof tops.
`PV_walls_east_E_kWh`	Electricity production from photovoltaic panels on east facades
`PV_walls_east_m2`	Collector surface area on east facades.
`PV_walls_north_E_kWh`	Electricity production from photovoltaic panels on north facades
`PV_walls_north_m2`	Collector surface area on north facades.
`PV_walls_south_E_kWh`	Electricity production from photovoltaic panels on south facades
`PV_walls_south_m2`	Collector surface area on south facades.
`PV_walls_west_E_kWh`	Electricity production from photovoltaic panels on west facades
`PV_walls_west_m2`	West facing wall collector surface area.
`radiation_kWh`	Total radiatiative potential.

PV_total_buildings¶

path: outputs/data/potentials/solar/PV_total_buildings.csv

The following file is used by these scripts:

Variable	Description
`Area_PV_m2`	Total area of investigated collector.
`E_PV_gen_kWh`	Total electricity generated by the collector.
`Name`	Unique building ID. It must start with a letter.
`PV_roofs_top_E_kWh`	Electricity production from photovoltaic panels on roof tops
`PV_roofs_top_m2`	Collector surface area on roof tops.
`PV_walls_east_E_kWh`	Electricity production from photovoltaic panels on east facades
`PV_walls_east_m2`	Collector surface area on east facades.
`PV_walls_north_E_kWh`	Electricity production from photovoltaic panels on north facades
`PV_walls_north_m2`	Collector surface area on north facades.
`PV_walls_south_E_kWh`	Electricity production from photovoltaic panels on south facades
`PV_walls_south_m2`	Collector surface area on south facades.
`PV_walls_west_E_kWh`	Electricity production from photovoltaic panels on west facades
`PV_walls_west_m2`	West facing wall collector surface area.
`radiation_kWh`	Total radiatiative potential.

PV_totals¶

path: outputs/data/potentials/solar/PV_total.csv

The following file is used by these scripts: optimization

Variable	Description
`Area_PV_m2`	Total area of investigated collector.
`Date`	Date and time in hourly steps.
`E_PV_gen_kWh`	Total electricity generated by the collector.
`PV_roofs_top_E_kWh`	Electricity production from photovoltaic panels on roof tops
`PV_roofs_top_m2`	Collector surface area on roof tops.
`PV_walls_east_E_kWh`	Electricity production from photovoltaic panels on east facades
`PV_walls_east_m2`	Collector surface area on east facades.
`PV_walls_north_E_kWh`	Electricity production from photovoltaic panels on north facades
`PV_walls_north_m2`	Collector surface area on north facades.
`PV_walls_south_E_kWh`	Electricity production from photovoltaic panels on south facades
`PV_walls_south_m2`	Collector surface area on south facades.
`PV_walls_west_E_kWh`	Electricity production from photovoltaic panels on west facades
`PV_walls_west_m2`	West facing wall collector surface area.
`radiation_kWh`	Total radiatiative potential.

PVT_metadata_results¶

path: outputs/data/potentials/solar/B001_PVT_sensors.csv

The following file is used by these scripts:

Variable	Description
`area_installed_module_m2`	The area of the building suface covered by one solar panel
`AREA_m2`	Surface area.
`array_spacing_m`	Spacing between solar arrays.
`B_deg`	Tilt angle of the installed solar panels
`BUILDING`	Unique building ID. It must start with a letter.
`CATB`	Category according to the tilt angle of the panel
`CATGB`	Category according to the annual radiation on the panel surface
`CATteta_z`	Category according to the surface azimuth of the panel
`intersection`	flag to indicate whether this surface is intersecting with another surface (0: no intersection, 1: intersected)
`orientation`	Orientation of the surface (north/east/south/west/top)
`SURFACE`	Unique surface ID for each building exterior surface.
`surface`	Unique surface ID for each building exterior surface.
`surface_azimuth_deg`	Azimuth angle of the panel surface e.g. south facing = 180 deg
`tilt_deg`	Tilt angle of roof or walls
`total_rad_Whm2`	Total radiatiative potential of a given surfaces area.
`TYPE`	Surface typology.
`type_orientation`	Concatenated surface type and orientation.
`Xcoor`	Describes the position of the x vector.
`Xdir`	Directional scalar of the x vector.
`Ycoor`	Describes the position of the y vector.
`Ydir`	Directional scalar of the y vector.
`Zcoor`	Describes the position of the z vector.
`Zdir`	Directional scalar of the z vector.

PVT_results¶

path: outputs/data/potentials/solar/B001_PVT.csv

The following file is used by these scripts:

Variable	Description
`Area_PVT_m2`	Total area of investigated collector.
`Date`	Date and time in hourly steps.
`E_PVT_gen_kWh`	Total electricity generated by the collector.
`Eaux_PVT_kWh`	Auxiliary electricity consumed by the collector.
`mcp_PVT_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the hot water delivered by the collector.
`PVT_roofs_top_E_kWh`	Electricity production from photovoltaic-thermal panels on roof tops
`PVT_roofs_top_m2`	Collector surface area on roof tops.
`PVT_roofs_top_Q_kWh`	Heat production from photovoltaic-thermal panels on roof tops
`PVT_walls_east_E_kWh`	Electricity production from photovoltaic-thermal panels on east facades
`PVT_walls_east_m2`	Collector surface area on east facades.
`PVT_walls_east_Q_kWh`	Heat production from photovoltaic-thermal panels on east facades
`PVT_walls_north_E_kWh`	Electricity production from photovoltaic-thermal panels on north facades
`PVT_walls_north_m2`	Collector surface area on north facades.
`PVT_walls_north_Q_kWh`	Heat production from photovoltaic-thermal panels on north facades
`PVT_walls_south_E_kWh`	Electricity production from photovoltaic-thermal panels on south facades
`PVT_walls_south_m2`	Collector surface area on south facades.
`PVT_walls_south_Q_kWh`	Heat production from photovoltaic-thermal panels on south facades
`PVT_walls_west_E_kWh`	Electricity production from photovoltaic-thermal panels on west facades
`PVT_walls_west_m2`	West facing wall collector surface area.
`PVT_walls_west_Q_kWh`	Heat production from photovoltaic-thermal panels on west facades
`Q_PVT_gen_kWh`	Total heat generated by the collector.
`Q_PVT_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.
`T_PVT_re_C`	Collector hot water return temperature.
`T_PVT_sup_C`	Collector heating supply temperature.

PVT_total_buildings¶

path: outputs/data/potentials/solar/PVT_total_buildings.csv

The following file is used by these scripts:

Variable	Description
`Area_PVT_m2`	Total area of investigated collector.
`E_PVT_gen_kWh`	Total electricity generated by the collector.
`Eaux_PVT_kWh`	Auxiliary electricity consumed by the collector.
`Name`	Unique building ID.
`PVT_roofs_top_E_kWh`	Electricity production from photovoltaic-thermal panels on roof tops
`PVT_roofs_top_m2`	Collector surface area on roof tops.
`PVT_roofs_top_Q_kWh`	Heat production from photovoltaic-thermal panels on roof tops
`PVT_walls_east_E_kWh`	Electricity production from photovoltaic-thermal panels on east facades
`PVT_walls_east_m2`	Collector surface area on east facades.
`PVT_walls_east_Q_kWh`	Heat production from photovoltaic-thermal panels on east facades
`PVT_walls_north_E_kWh`	Electricity production from photovoltaic-thermal panels on north facades
`PVT_walls_north_m2`	Collector surface area on north facades.
`PVT_walls_north_Q_kWh`	Heat production from photovoltaic-thermal panels on north facades
`PVT_walls_south_E_kWh`	Electricity production from photovoltaic-thermal panels on south facades
`PVT_walls_south_m2`	Collector surface area on south facades.
`PVT_walls_south_Q_kWh`	Heat production from photovoltaic-thermal panels on south facades
`PVT_walls_west_E_kWh`	Electricity production from photovoltaic-thermal panels on west facades
`PVT_walls_west_m2`	West facing wall collector surface area.
`PVT_walls_west_Q_kWh`	Heat production from photovoltaic-thermal panels on west facades
`Q_PVT_gen_kWh`	Total heat generated by the collector.
`Q_PVT_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.

PVT_totals¶

path: outputs/data/potentials/solar/PVT_total.csv

The following file is used by these scripts: optimization

Variable	Description
`Area_PVT_m2`	Total area of investigated collector.
`Date`	Date and time in hourly steps.
`E_PVT_gen_kWh`	Total electricity generated by the collector.
`Eaux_PVT_kWh`	Auxiliary electricity consumed by the collector.
`mcp_PVT_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the hot water delivered by the collector.
`PVT_roofs_top_E_kWh`	Electricity production from photovoltaic-thermal panels on roof tops
`PVT_roofs_top_m2`	Collector surface area on roof tops.
`PVT_roofs_top_Q_kWh`	Heat production from photovoltaic-thermal panels on roof tops
`PVT_walls_east_E_kWh`	Electricity production from photovoltaic-thermal panels on east facades
`PVT_walls_east_m2`	Collector surface area on east facades.
`PVT_walls_east_Q_kWh`	Heat production from photovoltaic-thermal panels on east facades
`PVT_walls_north_E_kWh`	Electricity production from photovoltaic-thermal panels on north facades
`PVT_walls_north_m2`	Collector surface area on north facades.
`PVT_walls_north_Q_kWh`	Heat production from photovoltaic-thermal panels on north facades
`PVT_walls_south_E_kWh`	Electricity production from photovoltaic-thermal panels on south facades
`PVT_walls_south_m2`	Collector surface area on south facades.
`PVT_walls_south_Q_kWh`	Heat production from photovoltaic-thermal panels on south facades
`PVT_walls_west_E_kWh`	Electricity production from photovoltaic-thermal panels on west facades
`PVT_walls_west_m2`	West facing wall collector surface area.
`PVT_walls_west_Q_kWh`	Heat production from photovoltaic-thermal panels on west facades
`Q_PVT_gen_kWh`	Total heat generated by the collector.
`Q_PVT_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.
`T_PVT_re_C`	Collector heating supply temperature.
`T_PVT_sup_C`	Collector heating supply temperature.

SC_metadata_results¶

path: outputs/data/potentials/solar/B001_SC_ET_sensors.csv

The following file is used by these scripts:

Variable	Description
`area_installed_module_m2`	The area of the building suface covered by one solar panel
`AREA_m2`	Surface area.
`array_spacing_m`	Spacing between solar arrays.
`B_deg`	Tilt angle of the installed solar panels
`BUILDING`	Unique building ID. It must start with a letter.
`CATB`	Category according to the tilt angle of the panel
`CATGB`	Category according to the annual radiation on the panel surface
`CATteta_z`	Category according to the surface azimuth of the panel
`intersection`	flag to indicate whether this surface is intersecting with another surface (0: no intersection, 1: intersected)
`orientation`	Orientation of the surface (north/east/south/west/top)
`SURFACE`	Unique surface ID for each building exterior surface.
`surface`	Unique surface ID for each building exterior surface.
`surface_azimuth_deg`	Azimuth angle of the panel surface e.g. south facing = 180 deg
`tilt_deg`	Tilt angle of roof or walls
`total_rad_Whm2`	Total radiatiative potential of a given surfaces area.
`TYPE`	Surface typology.
`type_orientation`	Concatenated surface type and orientation.
`Xcoor`	Describes the position of the x vector.
`Xdir`	Directional scalar of the x vector.
`Ycoor`	Describes the position of the y vector.
`Ydir`	Directional scalar of the y vector.
`Zcoor`	Describes the position of the z vector.
`Zdir`	Directional scalar of the z vector.

SC_results¶

path: outputs/data/potentials/solar/B001_SC_ET.csv

The following file is used by these scripts: decentralized

Variable	Description
`Area_SC_m2`	Total area of investigated collector.
`Date`	Date and time in hourly steps.
`Eaux_SC_kWh`	Auxiliary electricity consumed by the collector.
`mcp_SC_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the hot water delivered by the collector.
`Q_SC_gen_kWh`	Total heat generated by the collector.
`Q_SC_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.
`SC_ET_roofs_top_m2`	Collector surface area on roof tops.
`SC_ET_roofs_top_Q_kWh`	Heat production from solar collectors on roof tops
`SC_ET_walls_east_m2`	Collector surface area on east facades.
`SC_ET_walls_east_Q_kWh`	Heat production from solar collectors on east facades
`SC_ET_walls_north_m2`	Collector surface area on north facades.
`SC_ET_walls_north_Q_kWh`	Heat production from solar collectors on north facades
`SC_ET_walls_south_m2`	Collector surface area on south facades.
`SC_ET_walls_south_Q_kWh`	Heat production from solar collectors on south facades
`SC_ET_walls_west_m2`	Collector surface area on west facades.
`SC_ET_walls_west_Q_kWh`	Heat production from solar collectors on west facades
`T_SC_re_C`	Collector hot water return temperature.
`T_SC_sup_C`	Collector hot water supply temperature.

SC_total_buildings¶

path: outputs/data/potentials/solar/SC_ET_total_buildings.csv

The following file is used by these scripts:

Variable	Description
`Area_SC_m2`	Total area of investigated collector.
`Eaux_SC_kWh`	Auxiliary electricity consumed by the collector.
`Name`	Unique building ID.
`Q_SC_gen_kWh`	Total heat generated by the collector.
`Q_SC_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.
`SC_ET_roofs_top_m2`	Roof top collector surface area.
`SC_ET_roofs_top_Q_kWh`	Heat production from solar collectors on roof tops
`SC_ET_walls_east_m2`	East facing wall collector surface area.
`SC_ET_walls_east_Q_kWh`	Heat production from solar collectors on east facades
`SC_ET_walls_north_m2`	North facing wall collector surface area.
`SC_ET_walls_north_Q_kWh`	Heat production from solar collectors on west facades
`SC_ET_walls_south_m2`	South facing wall collector surface area.
`SC_ET_walls_south_Q_kWh`	Heat production from solar collectors on south facades
`SC_ET_walls_west_m2`	West facing wall collector surface area.
`SC_ET_walls_west_Q_kWh`	Heat production from solar collectors on west facades

SC_totals¶

path: outputs/data/potentials/solar/SC_FP_total.csv

The following file is used by these scripts: optimization

Variable	Description
`Area_SC_m2`	Collector surface area on south facades.
`Date`	Date and time in hourly steps.
`Eaux_SC_kWh`	Auxiliary electricity consumed by the collector.
`mcp_SC_kWperC`	Capacity flow rate (mass flow* specific heat capacity) of the hot water delivered by the collector.
`Q_SC_gen_kWh`	Total heat generated by the collector.
`Q_SC_l_kWh`	Collector heat loss.
`radiation_kWh`	Total radiatiative potential.
`SC_FP_roofs_top_m2`	Collector surface area on roof tops.
`SC_FP_roofs_top_Q_kWh`	Heat production from solar collectors on roof tops
`SC_FP_walls_east_m2`	Collector surface area on east facades.
`SC_FP_walls_east_Q_kWh`	Heat production from solar collectors on east facades
`SC_FP_walls_north_m2`	Collector surface area on north facades.
`SC_FP_walls_north_Q_kWh`	Heat production from solar collectors on north facades
`SC_FP_walls_south_m2`	Collector surface area on south facades.
`SC_FP_walls_south_Q_kWh`	Heat production from solar collectors on south facades
`SC_FP_walls_west_m2`	Collector surface area on west facades.
`SC_FP_walls_west_Q_kWh`	Heat production from solar collectors on west facades
`T_SC_re_C`	Collector hot water return temperature.
`T_SC_sup_C`	Collector hot water supply temperature.

Legal¶

License¶

The core of the City Energy Analyst is registered under The MIT License (MIT).

for V0.3c¶

Copyright (c) 2016, `Jimeno A. Fonseca <http://www.fcl.ethz.ch/person/dr-jimeno-a-fonseca/>`__, `Daren Thomas <http://www.systems.arch.ethz.ch/about-us/team/team-zurich/daren-thomas.html>`__, `Gabriel Happle <http://www.fcl.ethz.ch/person/gabriel-happle/>`__, `Shanshan Hsieh <http://www.fcl.ethz.ch/person/hsieh-shan-shan/>`__, `Martin Mosteiro <http://www.systems.arch.ethz.ch/about-us/team/team-zurich/martin-mosteiro-romero.html>`__, `Amr Elesawy <http://www.systems.arch.ethz.ch/about-us/team/team-zurich/amr-elesawy.html>`__, `Architecture and Building Systems - ETH Zurich <http://www.systems.arch.ethz.ch>`__

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

for V0.1¶

Copyright (c) 2015, `Jimeno A. Fonseca <http://www.fcl.ethz.ch/person/dr-jimeno-a-fonseca/>`__, `Daren Thomas <http://www.systems.arch.ethz.ch/about-us/team/team-zurich/daren-thomas.html>`__, `Architecture and Building Systems - ETH Zurich <http://www.systems.arch.ethz.ch>`__

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

Disclaimer¶

The City Energy Analyst is provided “as is”, without warranty of any
kind, express or
implied, including but not limited to the warranties of
merchantability,
fitness for a particular purpose and noninfringement. In no event
shall the
authors or copyright holders be liable for any claim, damages or other
liability, whether in an action of contract, tort or otherwise,
arising from,
out of or in connection with the software or the use or other dealings
in the
software.

Being Agile with CEA¶

Roles and Responsibilities¶

by Dr. Jimeno A. Fonseca, 2020, updated by Shanshan Hsieh, March 2020

This is a guide of roles and responsibilities in CEA. We use this to more formally coordinate our work. To check who is currently holding each position, we invite you to check here for more information.

Product Owner (PO)¶

The Product Owner is the voice of the customer and performs all tactical operations.

PO Responsibilities¶

Guarantee our product success by acting as a conduit/facilitator for communication between the team and the customers.

The means to communication to the customers include:

Conducting Usability Tests

Organizing Teaching Activities

Maintaining Documentation

Maintaining CEA website

Maintaining cea@arch.ethz.ch

Distributing Newsletters

Coordinate the Roadmap planning and review event (see Activities).
Break down the overall vision into feasible and tactical pieces of work for the development team to understand and create.
Prioritizing and selecting user stories. This entails grooming the product backlogs with the help of the Srcum Master.

The product backlogs contains issues that is labelled as Must-have and Should-have. Here are the details of the labels used to categorize new issues in the CEA:

Label	Description
Must-have	Features defined in the roadmap. Bugs.
Should-have	Issues that can’t be categorized for the moment.
Won’t have	Features that are not in the roadmap. Features that are hard to maintain. These features can become external modules of the CEA.

PO Engagement¶

Participate in all Activities.
One day per week on communication to the customer team and event coordination.

Scrum Master (SM)¶

The Scrum Master is the servant leader and maintainer of agile practices.

SM Responsibilities¶

Coordinate all Activities that form the agile practices of the CEA.
Coordinate the training of new developers.
1. Introduce the functionalities in the CEA.
2. Introduce the CEA-ecosystem
Coordinate the training of the team
1. Pair-programming sessions
2. Refresher course on programming
Support the Product Owner on setting priorities and grooming the product backlog.
Maintaining the internal communication channel (slack).

Development Team (DT)¶

The Development team is the group of individuals who build CEA. His/her main responsibilities are:

Create the best working software possible.
Design, analyze, develop, test and document new features in CEA.
Be self-organized and self-directed.
Participate in all agile practices led by the Scrum Master (Activities).

Customer Team (CT)¶

The Customer team is the group of individuals who use CEA. They are not part of the CEA team, but their function is extremely important for the succes of the project.

Represent one or more User Personas in CEA.
Validate CEA and give feedback to the Product Owner
Communicate issues and new features to the Product Owner.

User Personas¶

This is a guide of User Personas in CEA. These are potential end-users of CEA. We describe the goals and priorities of these user personas hereafter. We use this information to build User Stories / Report bugs / Develop new features. It is of utmost importance for every developer to know this by heart.

Researcher¶

The priorities of this User Persona are:

To find an answer to a unique research question/hypothesis
To build upon the state-of-the-art, saving time in coding newly established algorithms.
To conduct experiments quickly.
to generate replicable and transparent results.

Student¶

The priorities of this User Persona are:

To understand the trade-offs of planning cities for energy efficiency.
To build intuition about the mechanics of different analysis and modeling techniques.
To generate replicable and transparent results.

Practitioner¶

The priorities of this User Persona are:

To analyze real case studies quickly.
To generate great visualizations which aid communication to stakeholders.
To generate standard, replicable and transparent results.

User Stories¶

User stories define WHAT (need), WHY (reason) and for WHOM (User persona) we aim to solve a new bug or implement a new feature. This helps our Project Owner to better prioritize issues around CEA.

We have a standard yet simple way to define these user stories. This guide walks you through defining your first user story.

Step 1. define a user persona¶

At CEA we differentiate four (4) potential USERs for creating an user story. These are key groups of users we want CEA to serve and are defined as user personas. The first step in creating a new story is to select a User Persona from the following list:

Researcher: A member of the CEA research team or network of contributors.
Student or Practitioner: An active user of CEA.

For more information about the goals and priorities of the different user personas check: User Personas

Step 2. define its needs¶

Think about the NEED this user persona has. Is it about a problem/bug or rather about a new feature you want to be implemented? It is important that you think about the NEED from the point of view of the user persona.

Step 3. define a reason¶

Now think about why the user persona needs that.

Step 4. put the story together¶

Now put it all together using the following template:

As a USER PERSONA I want to NEED so I can REASON

Here is an example:

USER PERSONA:	Researcher
NEED:	know how to define user stories
REASON:	add new bugs and features to the pipeline of CEA

The result will be the title of your user story:

As a Researcher I want to know how to define user stories so I can add new bugs and features to the pipeline of CEA.

One more example:

USER PERSONA:	Student
NEED:	understand how the dynamic tool works
REASON:	use CEA more effectively

The result will be the title of your user story:

As a Student I want to understand how the dynamic tool works so I can use CEA more effectively.

Step 5. submit a new user story¶

Now it is time to submit a new user story in CEA. For this:

Go to the CEA repository in Github.
Click New Issue
in Title, add the name of the user story
Finally, in description, give a more detailed description of the problem.
In the description you can directly connect to other user stories using # or connect to other people using @ in the text.

Activities¶

This is a guide of Activities carried out during the development of CEA. This activities are part of our concept of operation under responsibilities of the Scrum Master.

As the project evolves, so do the activities: We try to keep them as lean as possible and expect to adapt this document as needed.

One important aspect of the team is how it is distributed between (mainly) 2 timezones (Singapore, Switzerland) and a large part of the team has other responsibilities (e.g. writing their PhD thesis) that needs to be accounted for and impacts the amount of “ceremony” we can sustain.

The Kanban board¶

The written record of these activities is reflected in the GitHub issues and are maintained using ZenHub to produce a Kanban view of our process. The columns on our Kanban board are:

New Issues
Project Backlog
Sprint Backlog
In Progress
In Review
Closed

The different events focus on different parts of the process and their respective Kanban columns are mentioned below.

Pre-Planning event¶

Duration: 1 - 2 hours.
Frequency: Before each Planning event (bi-weekly)
Scope: Prepare the Kanban board to streamline the Planning event.
Kanban:
- Move User stories from “New Issues” to either “Product Backlog” (if in scope) or close them with the label “wont-fix” or “known issue”.
- Make sure each User story in Product backlog has a (preliminary) time estimation (measured in story points, each story point is roughly 1 day of work)
- Make sure each User story in Product backlog has a priority label: “Must have” or “Should have”
- Order the Product backlog with most important issues at the top of the column

NOTE: Currently, the Pre-Planning event is held every second Wednesday at 9am Zurich time on Zoom, the day before the Planning event.

Attendees: Product Owner, Scrum Master

Planning event¶

Duration: 1 - 2 hours.
Frequency: After every block of work (sprint, bi-weekly)
Scope: Define what User stories should go in the next block of work (sprint). These stories are attached to a new time-bounded milestone.
Kanban: User stories are moved from the Product Backlog and to the Sprint Backlog and assigned to a member of the Development team. Each User story requires a time estimate and the developer the issue is assigned to is responsible for updating this estimate based on his/her experience.

Currently, the Planning event is held every second Thursday at 9am Zurich time on Zoom. Subject to change.

Attendees: Scrum Master, Product Owner, Product Sponsor, and Development Team.

Daily stand-up¶

Duration: 15 min.
Frequency: daily
Scope: Provide early support and mentoring
Kanban: Main focus is on User stories in the “In Progress” and “In Review” columns. This is also time to point out important “New Issues” and discuss problems blocking “Sprint Backlog” issues from being moved to “In Progress”.

NOTE: Currently this is being held every morning at 9am (Switzerland time) on Zoom with the SM and a reduced set of the DT.

Attendees: Scrum Master and Development Team.

Roadmap planning and review event¶

Duration: 1 - 2 hours.
Frequency: quarterly
Scope:
1. Demonstration of the newest development in the CEA.
2. Bottlenecks that are faced by the development team.
3. Planning for the next quarter.

Attendees: Scrum Master, Product Owner, Product Sponsor, and Development Team.

NOTE: While this event is mainly an update to the product sponsors, it is also open to the entire CEA team and the customer team.

Retrospective¶

Duration: 1 hour.
Frequency: After every review event
Scope: Discuss what was wrong and set next steps to follow.

NOTE: This activity is currently not part of our repertoire. We are trying to figure out the right parameters for this.

Attendees: Scrum Master and Development Team.

Communication channels¶

This is a guide of Communication channels in CEA. These are maintained by our Scrum Master.

CEA website¶

Host: squarespace.com
Credentials: Ask the Product Owner
Administrator: Product Owner
Access granted to: Scrum Master, Product Owner, Development Team

CEA e-mail¶

Host: arch.ethz.ch
Credentials: Ask the Product Owner
Administrator: Product Owner
Access granted to: Scrum Master, Product Owner

CEA messenger¶

Host: ceadev.slack.com
Credential: Ask the Scrum Master
Administrator: Scrum Master
Access granted to: Scrum Master, Product Owner, Development team

CEA documents¶

Host: gmail.com
Credentials: Ask the Scrum Master Daren Thomas
Administrator: Scrum Master
Access granted to: Scrum Master, Product Owner, Development Team

Contributing to City Energy Analyst (CEA)¶

First off, thank you for taking the time to contribute!

The following is a set of guidelines for contributing to CEA, which is hosted on GitHub. These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.

Step 1. Let us know about it¶

Whether you would like to implement a new feature or fix a bug, let the CEA team know. This will help us to coordinate efforts across network of developers. It will also help us to better support your work.

You can let us know by reporting a new issue https://github.com/architecture-building-systems/CityEnergyAnalyst/issues/new/choose

Step 2. Install our development version¶

If you have not done it yet, please install the development version of CEA. Make sure you have the CEA repository (included in the development version) connected via github and your favorite python editor (ours is PyCharm). You can download the development version with the same link as the one used www.cityenergyanalyst.com/tryit.

Here we tell you how to do it https://cityenergyanalyst.com/blog/2019/6/3/how-to-install-the-cea-on-windows-part-2

Step 3. Branch out and code¶

Branch out from out main ‘Master branch” of our github repository and start coding. This can be done with the CEA development version. For this make sure to use one of our template scripts and follow the documentation guide. This could help to maintain an homogenous structure, and help us to acknowledge you.

Check these guides for more details: How to Use Github?, How to add a new script?.

Step 4. Check style¶

If you have not done it yet, take some time to get acquainted with variable names in CEA. This would make easier for you to understand and develop consistent code.

Here we tell some basic hints Variable Naming in CEA.

Step 5. Run some local tests¶

Now test if your creation does not brake CEA’s functionality.

The next guide explains how to run local unittests in CEA How to test the CEA?.

Step 6. Create a Pull request¶

Now it is time to ask other developer of CEA to review your code so we get to make it part of the CEA main core. We do this by creating a Pull Request in github.

Check this guide for more details on how to do it: How to Use Github?.

Step 7. Claim your CEA T-shirt!¶

What happens after that? We will check the code, and if all is correct we will proceed to make it part of CEA’s main source code. If your work has been merged, give yourself an applause. You have just made part of the growing network of developers of CEA.

Your are entitled to claim a CEA T-shirt after this to cea@arch.ethz.ch

Developer walkthrough¶

The Configuration File¶

The City Energy Analyst uses a configuration file for storing user preferences. User preferences are inputs to simulation runs like what weather file to use, what scenario to use and script-specific inputs.

When you first run the cea tool (e.g. with cea install-toolbox during the installation process), the default configuration file is copied to your home folder.

On Windows systems, the home folder is usually something like C:\Users\YourUserName, so the configuration file would be stored in C:\Users\michelle\cea.config, assuming that your username is michelle.

Setting values in the configuration file¶

The most important values to set when working with the CEA are probably those under the [general] section, specifically scenario, and weather

Open the cea.config file with a text editor (notebook.exe will do just fine) and update the values.

Note

We expect to implement an editor for the configuration file soon.

The configuration file and the command line interface¶

When you run the CEA from the command line (with the cea command), then the values to use as inputs to the scripts are taken from the configuration file. You can override each value by adding it as a parameter to the cea command, using the syntax -- + parameter-name + `` `` + value. Example:

$ cea demand --scenario C:\scenario\baseline --weather Brussels

The configuration file and the ArcGIS interface¶

The values in the configuration file are used as the default values when you open up a cea tool in the ArcGIS interface.

Configuration File Details¶

Let’s explore the details of how the configuration file works!

The configuration file edited by the user (~/cea.config) is only the tip of the iceberg, resting on a foundation of the default configuraiton file default.config file and the class cea.config.Configuration, which reads in the default configuration file as well as the user configuration file and makes those parameters available to the scripts. Each script is provided with an instance of cea.config.Configuration called config.

Each parameter is defined in a section. Each parameter has a type, which specifies the range of values allowed for that parameter and also how to read and write them to the configuration file.

Access to parameters through the config variable happens by section. Since all section names and parameter names in the configuration file follow the kebab-case naming convention, and these are not valid python identifiers, a translation is made to the snake_case naming convention: All hyphens (-) are replaced by underscores (_).

The syntax is simple:

"config." + [section] + "." + parameter

The section name is optional for the section general, so config.general.scenario refers to the same parameter as config.scenario. Note that these parameters can also be set:

config.scenario = r'C:\hoenggerberg\baseline'

If you want to persist these changes to disk, you need to explicitly save them with cea.config.Configuration.save().

Note

It is a bad idea to have multiple instances of cea.config.Configuration, as if one part of a script changes a parameter, this will not be reflected in the other instances. Each CEA script accepts a config argument to it’s main function and should only use that.

Overview¶

$digraph config { graph [splines=ortho, nodesep=0.1, rankdir="TD"] node [shape="record", fontname="Arial", fontsize="8"] Configuration -> Section [label=" 0..*"]; Section -> Parameter [label=" 0..*"]; Configuration [label="{Configuration|default_config\luser_config\lsections\l|apply_command_line_args()\lsave()\l}"]; Parameter [label="{Parameter|name\lhelp\lcategory\lsection\lconfig\l|initialize()\lget()\lset()\lencode()\ldecode()\l}"]; Section [label="{Section|name\lconfig\lparameters\l|__getattr__()\l__setattr()__\l}"]; { rank="same"; Configuration; Section; Parameter; } }$

Initialization of the config object¶

An instance of cea.config.Configuration is created with an optional config_file parameter that specifies the configuration file to load as the user configuration file. This defaults to ~/cea.config. This file is parsed as a ConfigParser.SafeConfigParser, using the default configuration as a backup for the values and stored in the attribute user_config. Another ConfigParser.SafeConfigParser is created for the default configuration and stored in the attribute default_config.

Next, the default_config is used to create a dictionary of :py:class`cea.config.Section` objects and each section is populated with a dictionary of cea.config.Parameter instances. The default configuration file lists not only each parameter, but additional keys for each parameter as well. Example:

[general]
scenario = C:\reference-case-open\baseline
scenario.type = PathParameter
scenario.help = Path to the scenario to run

Using this information, the parameter general:scenario is assigned a default value of C:\reference-case-open\baseline, is represented by a subtype of cea.config.Parameter` called cea.config.PathParameter and has a help text “Path to the scenario to run” - which is stored in the help attribute of the parameter object.

Some subclasses of cea.config.Parameter have additional configuration, like the cea.config.ChoiceParameter:

[data-helper]
region = CH
region.type = ChoiceParameter
region.choices = CH SIN custom
region.help = The region to use for the databases (either CH or SIN) - set to "custom" if you want to edit them

When the config instance is creating the parameters, each parameter object is given a chance to initialize itself with a call to cea.config.Parameter.initialize(parser)() with parser set to the default_config. Subclasses of Parameter can override this method to read this additional configuration.

How a value is read from the config file¶

When a script does something like config.general.weather, the config.sections dictionary is checked for the section named general and the parameters dictionary in that section is checked for a parameter named weather. The cea.config.Parameter.get() method is called on that parameter and the result of this call is returned.

Based on the default configuration file, this is defined as:

[general]
weather = Zug-inducity_1990_2010_TMY
weather.type = WeatherPathParameter
weather.help = either a full path to a weather file or the name of one of the weather files shipped with the CEA

So the parameter is of type cea.config.WeatherPathParameter.

Inside the cea.config.Parameter.get() method, a call is made to cea.config.Parameter.decode(), passing in the value read from the user configuration file. Subclasses of Parameter specify how to encode and decode values to the configuration file. The semantics are:

decode takes a string from a configuration file (or from the command line) and returns a typed value (e.g. a bool if the parameter type is cea.config.BooleanParameter).
encode takes a typed value (e.g. a boolean value) and encodes it to a string that can be stored in the configuration file.

In the case of cea.config.WeatherPathParameter, decode will ensure that the path to the weather file exists and, if just the name of a weather file in the CEA weather file database is returned, resolves that to the full path to that file. Hence, on my system, the value of config.weather is C:\Users\darthoma\Documents\GitHub\CityEnergyAnalyst\cea\databases\weather\Zurich.epw.

How a value is saved to the config file¶

The mechanism for saving a value to the config file works similarly: cea.config.Parameter.set() is called, which in turn calls cea.config.Parameter.encode() - subclasses can override this to provide type specific behaviour.

How to create new parameter types¶

Steps:

subclass cea.config.Parameter
optional: override initialize to settings
optional: override encode to format the parameter value as a string
optional: override decode to read the parameter value from a string

Check the existing parameter types for ideas!

User Interfaces¶

The CEA code exposes multiple interfaces as an API:

CLI (Command Line Interface) - each module in the CEA implements a CLI for calling it from the command line.
euler - a set of scripts for running the CEA sensitivity analysis on the ETH Euler cluster is provided in the folder euler and can be used as a starting point for running the analysis on similar clusters and / or linux machines.

The Command Line Interface¶

The most portable way to interact with the CEA is via the CLI. Type the following command in your shell to see the list of commands available:

> cea --help
usage: cea SCRIPT [OPTIONS]
       to run a specific script
usage: cea --help SCRIPT
       to get additional help specific to a script

SCRIPT can be one of: benchmark-graphs, compile, data-helper,
    dbf-to-excel, demand, demand-graphs, embodied-energy, emissions,
    excel-to-dbf, extract-reference-case, install-toolbox,
    latitude, list-demand-graphs-fields, locate, longitude, mobility,
    operation-costs, photovoltaic, photovoltaic-thermal, read-config, read-config-section,
    retrofit-potential, scenario-plots, sensitivity-demand-analyze,
    sensitivity-demand-samples, sensitivity-demand-simulate,
    solar-collector, test, weather-files, weather-path, write-config

All scripts use the configuration file as the default source of parameters. See the Configuration File Details for information on the configuration file.

The parameters in the configuration file relevant to a script can be overridden. To see which parameters are used by a certain script, use the syntax cea --help SCRIPT:

> cea --help data-helper

building properties algorithm

OPTIONS for data-helper:
--scenario: C:/reference-case-open/baseline
    Path to the scenario to run
--archetypes: ['comfort', 'architecture', 'HVAC', 'internal-loads']
    List of archetypes to process

This displays some documentation on the script as well as a list of parameters, their default values and a description of the parameter. Using this information, the data-helper script can be run like this:

> cea data-helper --scenario C:/reference-case-open/scenario1 --archetypes HVAC

Note

All options are optional and have default values as defined in the configuration file!

Architecture¶

The architecture of the CEA is still a bit in flux, but some main components have already been developed and will be explained in this chapter. The following figure shows a high-level view of the main components of the CEA:

Demand calculation¶

At the core of the CEA is the demand calculation. The demand calculation retrieves inputs from the scenario folder and stores outputs back to the scenario folder. A preprocessing step can be used to add archetype data to a scenario as a first guess of certain parameters.

The demand calculation uses a special variable called tsd to store information about the timestep data during the calculation of thermal loads for each building. The data structure used is a simple python dictionary of NumPy arrays. Each of these arrays has the length 8760, to total number of hours of the year. The keys of the tsd dictionary are the names of the state variables of the simulation.

The demand calculation also uses a variable bpr to store building properties of a single building.

InputLocator¶

The cea.inputlocator.InputLocator class encapsulates the code for creating paths for input and output to the archetypes and the contents of the scenario (input and output files). An instance of this class is found in most of the code and is always named locator, unless multiple InputLocator objects are used, e.g. for comparing scenarios.

Each method of the locator starts with get_* and returns a string containing the full path to the resource requested. These get_* methods should be the only way to obtain file- and folder names in the CEA - files and folders should especially not be concatenated with strings and backslashes (\). Instead, new paths should be introduced as methods of the InputLocator class.

One of the main benefits of doing this is that it makes documentation of what files are read / written by what module of the CEA easier. The funcionlogger module can be used to trace these calls for generating documentation.

The private method _ensure_folder(*paths) is used to join path components and at the same time ensure that the folders are present in the scenario folder, creating them with os.makedirs if necessary.

NOTE: The list of get_* methods is getting very long. We might end up creating a namespace hierarchy for grouping related paths together.

Analysis and Visualization¶

Separate modules exist for analyzing different aspects of a scenario. Some of the analysis modules operate only on the input data (LCA for embedded emissions, mobility) and others operate on the output of the demand module (LCA for emissions due to operation). These modules are grouped in the folder cea/analysis.

The folder cea/plots contains modules for plotting outputs of the calculations.

“Higher order” modules¶

Some of the modules in the CEA use the demand calculation to calculate variants of a scenario. This includes the sensitivity analysis, the calibration and the network optimization code. All these modules call the demand calculation as part of their process.

How to review a pull request¶

Code review could be time-consuming, but it is extremely important. All pull requests (PR) to the CEA should be reviewed by at least one contributor with maintenance right. The reviewer needs to ensure the changes in the code are aligned with the authors’ description and do not compromise the existing functionalities in the CEA.

1. Read the PR description and follow the test¶

The author of the PR should provide an instruction on how to test the implementation of the new changes. As the reviewer, you should be able to follow the instruction provided by the PR’s author, or provide feedback if the instruction is unclear. Once the test provided by the author has passed, the reviewer may proceed to the next step.

2. Go through the file changes¶

It is always a good idea to go through all the changes at least once. Please follow this guide to review the file changes on GitHub.

During this process, the reviewer should check for:

Conflicts with master. Make sure the branch is updated with the latest master, and all conflicts are resolved.

Sufficient documentation. Check if the documentation is sufficient for the next person to understand the code.

Hard-coded values. All hard-coded values should be avoided if possible.

Unit tests to implement. The reviewer should decide whether a unit test should be implemented, and request the PR author to implement one accordingly.

Changes that might affect other existing functions. In this case, the reviewer should come up with a test to ensure the existing functions are still working as intended.

Once all the points are checked out, the reviewer may proceed to the next step.

3. Run tests¶

All PRs are automatically sent to test by Jenkins, it executes cea test --workflow quick on a remote computer. The test result is directly shown in the PR page on GitHub.

Additionally, it is always a good idea to run a complete test (cea test --workflow slow) on your local computer. If Jenkins encounters any errors, you can also reproduce those errors by running cea test --workflow quick locally. See here for more information on cea test.

Once cea test is passed, the reviewer may proceed to the last step!

4. Merge the Pull Request¶

Now you have made sure the PR is going to improve the CEA, thank you for your time! You may go ahead and merge the PR. If the new changes would affect many users, you might want to consider publishing it on the #_critical_updates channel on Slack.

How to add a heating/cooling system in CEA¶

Step 0: Make an issue and create a branch¶

As this procedure requires adding scripts in CEA master, please make a branch before performing the changes.

Step 1: Add the new system to the database¶

Open cea/databases/systems/emission_systems.xls
In the tab heating or cooling, add a row for the new system.
Specify the operating conditions of the new system, for cooling systems:

code: add a new code Tx that has not been used.

Qcsmax_Wm2: maximum cooling capacity of the system.

dTcs_C:

For Air Handling Units (ahu), if applicable:

Tscs0_ahu_C: coolant (water) supply temperature at the primary side
dTcs0_ahu_C: temperature change of the coolant at the primary side
Tc_sup_air_ahu_C: air supply temperature from ahu to the room

For Air Recirculation Units (aru), if applicable:

Tscs0_aru_C: coolant (water) supply temperature at the primary side
dTcs0_aru_C: temperature change of the coolant at the primary side
Tc_sup_air_aru_C: air supply temperature from ahu to the room

For Sensible Cooling Units (scu), if applicable:

Tscs0_scu_C: coolant (water) supply temperature at the primary side
dTcs0_scu_C: temperature change of the coolant at the primary side

Step 2: Add the new system to the options¶

Go to script cea/demand/control_heating_cooling_systems.py
Add the code of the new systems (Tx) to function has_cooling_systems or has_heating_systems
Add a new function that check the type of the system, similar to has_3for2_cooling_systems

Step 3: Add a new function to model new technologies¶

Currently, there are models for AHU, ARU, SCU running wiht heating/cooling coil. If the new systems is utilizing different technologies, the models should be added to airconditioning_model.py.

Step 3: Add a new function to calculate cooling/heating loads¶

Go to script cea/demand/hourly_procedure_heating_cooling_system_load.py
Add a new function to set up the calculation procedure for cooling/heating loads, similar to calc_cool_loads_3for2

Step 4: Add distribution losses¶

Go to cea/demand/sensible_loads.py
Update calc_Qhs_Qcs_loss

Step 5: Calculate temperature and mass flow primary supply systems¶

Go to cea/demand/sensible_loads.py
Update calc_temperatures_emission_systems

Step 6: Calculate auxiliary electricity¶

calc_Eauxf_cs_dis calc_Eauxf_hs_dis

How to create a new release?¶

This section describes the steps necessary to create a new release of the City Energy Analyst (CEA).

Versioning¶

Each release of the CEA needs a version number. Version numbers need to increase for PyPI. The relevant documentation for python version numbers is documented in PEP440. The CEA uses the following versioning scheme in compliance with PEP440:

major.minor[.revision][pre-release]

Major and minor version segments in this scheme refer to the milestone (sprint) the release was developed for. The major version segment works on roughly a yearly time scale while the minor version segment tracks sprints inside the major release. Each such pair (major.minor) refers to a “milestone” in the GitHub issues milestones list.

If a release needs to be updated after publishing, an optional revision can be used, starting at 1 and incrementing.

During the sprint, the pre-release section is used to represent the current state of the master branch. At the beginning of the sprint, alpha versions are used. Examples: 2.2a0, 2.2a1, 2.2a2, etc. In this phase the issues belonging to the milestone are being added.

Once the code base settles down, beta versions can be used. Examples: 2.2b0, 2.2b1, 2.2b2, etc. In this phase, new features should not be added anymore and instead testing / bug fixing activities should dominate.

Before releasing a milestone, the release candidates can be used. Examples: 2.2rc0, 2.2rc1, 2.2rc2, etc. In this phase the software is just being tested with show-stopping bugs being fixed if possible.

Where to find the current version number¶

The current version number can be found in the module cea (actually, since cea is a package, you need to look into the file __init__.py) in the variable __version__.

All code requiring knowledge of the current version number should read the version from here.

In python modules this can be achieved by:

import cea
version_number = cea.__version__

The NSIS installer (see section Creating the installer) uses the helper tool setup/get_version.exe to extract the version and write it to the file setup/cea_version.txt - if importing cea is not an option, you could explore this avenue too…

Responsibility for version numbers¶

The repository admin merging a pull request to master is responsible for updating the version number.

Create a Release Branch¶

Create a branch Release-x.x.x from master.

Update the CREDITS.md file¶

For each minor release (2.2, 2.3, …) the CREDITS.md file needs to be updated to include all the authors that worked on that release. Update the “How to Cite” section with the Zenodo link to the correct version and doi.

Update CHANGELOG¶

Run create-changelog.py in CityEnergyAnalyst\bin.
Update CHANGELOG.md with the latest changes from the outputs.

Updating the CEA GUI interface¶

You’ll need yarn and Node.js installed.

For the installer to be able to pick up the newest version of the CEA GUI interface, make sure you

Pull the newest version of the CityEnergyAnalyst-GUI repository
Open CEA Console, navigate to the GitHub repo of the CityEnergyAnalyst-GUI repository
Type yarn, wait for the command to complete (this will update packages if necessary)

Creating the installer¶

First, make sure you have the Nullsoft Scriptable Installation System (NSIS) installed. See :docs:`how-to-set-up-nsis`

Next, make sure the command cea-dev build is configured properly. The configuration should look something like this:

(CEA) λ cea-config build
City Energy Analyst version 3.11.0
Configuring `cea build` with the following parameters:
- development:nsis = C:\Program Files (x86)\NSIS\Bin\makensis.exe
  (default: )
- development:conda = C:\Users\darthoma\miniconda3\condabin\conda.bat
  (default: )
- development:gui = c:\Users\darthoma\Documents\GitHub\CityEnergyAnalyst-GUI
  (default: )
- development:yarn = C:\Users\darthoma\AppData\Roaming\npm\yarn.cmd
  (default: )

You can either edit the cea.config file directly or use cea-config build --nsis C:\...\makensis.exe --conda ....

Note: The paths will be different on your system. Use the conda.bat in condabin of your Anaconda/Miniconda installation. The path to gui should be set to the repository folder of the CityEnergyAnalyst-GUI repository.

Install conda-pack by typing conda install conda-pack.
Creating the installer is then as easy as cea-dev build. This will run quite some time as it will create a new conda environment for the version, conda-pack it, and do a lot of compressing.
Locate the installer in the CityEnergyAnalyst repository under setup/Output.

Create a Release Draft on GitHub¶

Tag the release with the correct version number.

Testing in a virtual machine¶

In order to test the release, it is a good idea to run the installation guide / installer on a clean virtual machine, e.g. with VirtualBox.

This test should go as far as running cea test --workflow slow just to be sure everything is still working. This test goes a bit further than the regular test in that it makes sure the installation instructions still work on a new installation. This is important because it can find missing packages in the dependency lists etc.

It’s a good idea to use a different username on the VM as the one you used to create the installer - some pip bugs can be found that way.

Merge the Release Branch¶

Update the “How to Cite” section inside CREDITS.md with the Zenodo link to the correct version and doi.
Merge the branch Release-x.x.x into master.

Publish the Release on GitHub¶

The release should be published so that it could be found on the CityEnergyAnalyst repository on GitHub. Add the

installer you created in the previous step. - It is recommended to also publish a release on the CityEnergyAnalyst-GUI repository that corresponds to the version on the CityEnergyAnalyst repository.

Building the documentation¶

Well documented code is an essential part of the release, allowing your code’s legacy to only grow in glory and admiration.

The documentation will be rendered via the readthedocs site, allowing future developers, practitioners, researchers and students to understand and build upon your work. CEA uses sphinx to document all module code, and GraphViz to render flow charts (please install Graphviz to view graphs).

First, launch the CEA Console created by the installer and call (please address any errors (red text) which appears during the sphinx build):

cea-doc html

This tool will:

Remove any outdated module rst files
Rebuild all module rst files
Render all rst files to html
Open any documentation html’s for files identified by a Gitdiff.

Finally, any changes to the conda environment need to be reflected in the CityEnergyAnalyst/environment.yml file and if your code writes any new output variables or files, the CityEnergyAnalyst/cea/schemas.yml should be updated accordingly.

For more information, check out the how-to-document-cea.

Updating Link in www.cityenergyanalyst.com/try-cea¶

Go to http://www.cityenergyanalyst.com
Press Esc and try logging into squarespace
Go to Pages/Try CEA (it is the last page in the list)
Go to edit ‘Page content’
Go to edit ‘Form’
Change ‘Form Name’ to the name of the new version of CEA you just released
Go to the tab ‘Advanced’
Change ‘POST-SUBMIT REDIRECT’ to the link where the .exe of CEA can be downloaded from
Change ‘POST-SUBMIT MESSAGE’/here, to the link where the .exe of CEA can be downloaded from
Click ‘Apply’
Click ‘Save’

Uploading to PyPI¶

Note

This step is not necessary anymore for installation.

Check long-description with this commandline:

python setup.py --long-description | for /f %i in ('where rst2html.py') do python %i > %temp%\ld.html && start %temp%\ld.html

make sure the output is valid / no errors, as this will be the text of the CEA on PyPI

Delete any old distributions from dist folder (you can just delete the whole dist folder if you like)
Do python setup.py sdist bdist_wheel
- this will recreate the dist folder with two files that look similar to these:
  - cityenergyanalyst-2.2-py2-none-any.whl
  - cityenergyanalyst-2.2.tar.gz
Use twine to upload to PyPI (twine upload dist/*)
- you can get twine with pip install twine (it should be pre-installed in the CEA Console)
- the command above assumes you have set the TWINE_PASSWORD and TWINE_USERNAME environment variables if not, use the --username and --password positional arguments
- ask the repository admins for username and password

How to set up the Jenkins server on a new PC¶

Note

you only need to do this when the current Jenkins server dies

Note

this guide assumes you are installing on a Windows 10 Professional system. Adjust accordingly for other systems, but keep in mind that some functionality of the CEA is dependant on Windows.

There are a few steps to take to setting up a Jenkins server:

installation of some prerequisites
installation of Jenkins
installation of a tunnel to the Jenkins server
global configuration of Jenkins
configuration of the Jenkins items
- cea test for new pull requests
- cea test for merges to master

Installation of some prerequisites¶

You will need to install these softwares:

CityEnergyAnalyst (install with the Setup_CityEnergyAnalyst_<VERSION>.exe installer)
- we’ll be using the Python environment shipped with the CEA to test the CEA
- we’ll also be using the git.exe shipped with the CEA

Installation of Jenkins¶

Download & install jenkins from https://jenkins.io
- LTS version Jenkins for Windows (last time this document was used, it was version 2.204.4)
- just double click the installer, next, next, next (all default values)
- set jenkins service to use local user
  - Open up the Services Manager (search for “Services” in the Windows menu)
  - locate and open the “Jenkins” service
  - make sure the Startup type is set to “Automatic” so the Jenkins starts up again after reboots
  - on the tab “Log On”, select “This account” instead of “Local System account” and enter in your credentials
    - this will allow the Jenkins to have access to your user profile. You can create an account just for this service and use that for the rest of this guide.
open browser to http://localhost:8080 (NOTE: the installer did this automatically last time tried)
- follow instructions to enter initial admin password
  - click “install suggested plugins”
  - create first admin user
    - Username: cea
    - Password: (same as cityea user in outlook, ask Jimeno or Daren for the password)
    - Full name: City Energy Analyst
    - E-mail address: cea@arch.ethz.ch
  - Click “Manage Jenkins”
    - click “Configure System” (following this guide here: https://wiki.jenkins.io/display/JENKINS/Github+Plugin#GitHubPlugin-GitHubhooktriggerforGITScmpolling)
    - set “# of executors” to 1 (let’s just make it dead simple, no concurrency, less headache)

Installation of a tunnel to the Jenkins server¶

This guide assumes you’re running the Jenkins on a Windows PC inside a corporate network. We use the ngrok service to tunnel webhooks triggered by GitHub back to the Jenkins server.

download ngrok for Windows (https://ngrok.com/download)
extract ngrok.exe to %PROGRAMDATA%\ceajenkins\ngrok.exe
- (you might need to create the folder ceajenkins first)

create a file ngrok.yml in the folder %PROGRAMDATA%\ceajenkins with the following contents:

authtoken: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
tunnels:
  ceajenkins:
    proto: http
    addr: 8080
    subdomain: ceajenkins

(replace the authtoken variable with the authtoken obtained from ngrok

test it with this command: %PROGRAMDATA%\ceajenkins\ngrok.exe start --config %PROGRAMDATA%\ceajenkins\ngrok.yml ceajenkins
- you should now be able to access your Jenkins installation by going to https://ceajenkins.ngrok.io from any computer with access to the internet
- press CTRL+C to shutdown the tunnel
copy the CityEnergyAnalyst\bin\ceajenkins.py file to %PROGRAMDATA%\ceajenkins
- if you haven’t checked out the CEA, download it from the CEA GitHub repository
copy the CEA Dependencies folder (after installing CEA, it should be in %USERPROFILE%\Documents\CityEnergyAnalysts\Dependencies) twice
- once to C:\ProgramData\ceajenkins\ceatest
- once to C:\ProgramData\ceajenkins\ceatestall
- (actually rename the folder Dependencies to ceatest and ceatestall respectively)
in order for the service to find required DLL’s, ensure the PATH includes the following folders (use the windows search function to find the control panel item “Edit System Environment Variables”):
- C:\ProgramData\ceajenkins\ceatestall\Python\
- C:\ProgramData\ceajenkins\ceatestall\Python\lib\site-packages\win32
- C:\ProgramData\ceajenkins\ceatestall\Python\lib\site-packages\pywin32_system32
- make sure you edit the System Variables, not the User Environment Variables
open cmd.exe with admin rights (right click, then “Run as Administrator”)
run python %PROGRAMDATA%\ceajenkins\ceajenkins.py install
open the windows services panel (just search for “Services” in the windows menu)
- locate “CEA Jenkins keepalive”, right click, “Properties”
- set Startup type to “Automatic”
- set the account in the “Log On” tab to your user account (the one that you used to install all of the above stuff)
- start the service!
- you should now be able to access your Jenkins installation by going to https://ceajenkins.ngrok.io from any computer with access to the internet (test this)

Global configuration of Jenkins¶

Now that we have a tunnel set up, we can start configuring the Jenkins server, mainly following this guide:

open browser to http://ceajenkins.ngrok.io and log in
click “Manage Jenkins” and then “Configure System”
- set “# of executors” to 1 (let’s just make it dead simple, no concurrency, less headache)
- in the “Jenkins Location” section set Jenkins URL to “https://ceajenkins.ngrok.io”
  - (Jenkins might be smart enough to figure this out and has filled it in for you already)
- scroll to “GitHub” section
- click “Advanced”
- dropdown “Manage additional GitHub actions”, click “Convert login and password to token”
- choose “From login and password”, enter GitHub user and password, click “Create token credentials”
- Click “Add GitHub Server”
  - Name: (leave blank)
  - Credentials: (choose the GitHub credentials auto-generated for your username)
  - click “Test connection” - expect this message: “Credentials verified for user <username>”
  - check “Override Hook URL”
  - enter hook url https://ceajenkins.ngrok.io
- click “Save”

Next, we make sure all the required Jenkins plugins are installed

open browser to http://ceajenkins.ngrok.io and log in
click “Manage Jenkins” and then “Manage Plugins”
- install the following plugin:
  - GitHub Pull Request Builder Plugin (https://github.com/jenkinsci/ghprb-plugin)

Next, we configure the GitHub Pull Request Builder plugin, following the instructions here: https://github.com/jenkinsci/ghprb-plugin

open browser to http://ceajenkins.ngrok.io and log in
click “Manage Jenkins” and then “Configure System”
scroll down to the “GitHub Pull Request Builder” section
- leave the GitHub Server API URL: https://api.github.com
- set the Jenkins URL overrride: https://ceajenkins.ngrok.io
- leave the Shared secret: (bunch of *’s… idk…)
- select the credentials (This should be the GitHub auto generated token credentials you created above)
- select Auto-manage webhooks
- set the Admin list to the two lines daren-thomas and JIMENOFONSECA
click Save

Finally, make sure Jenkins knows where to find git.exe - if it’s not in %PATH%:

open browser to https://ceajenkins.ngrok.io and log in
click “Manage Jenkins” and then “Global Tool Configuration”
set “Path to Git executable” to C:\ProgramData\ceajenkins\ceatestall\cmder\vendor\git-for-windows\bin\git.exe

Configuration of the Jenkins items¶

First, we configure a Jenkins item for pull requests:

open browser to https://ceajenkins.ngrok.io and log in
click “New Item”
Enter an item name: run cea test for pull requests
- Choose “Freestyle project”
- Project name: “run cea test for pull requests”
- Description: “Check out the CityEnergyAnalyst, and run binceatest.bat”
- check “Discard old builds”
  - Strategy: “Log Rotation”
  - Max # of builds to keep: 10
- check “GitHub project”
- Project url: “https://github.com/architecture-building-systems/CityEnergyAnalyst”
- section “Source Code Management”:
  - select “Git”
  - Repository URL: https://github.com/architecture-building-systems/CityEnergyAnalyst.git
  - Credentials: (add a new username/password credential)
  - Branches to build: ${ghprbActualCommit}
- section “Build Triggers”:
  - check “GitHub Pull Request Builder”
  - GitHub API credentials: choose your credentials from the list
  - check “Use github hooks for build triggering”
  - click “Advanced”
  - List of organizations. Their members will be whitelisted: architecture-building-systems
- section “Build”
  - Execute Windows batch command: bin\ceatest.bat
- section “Build Environment”
  - select “Delete workspace before build starts”

Next, we configure a Jenkins item for merging to master:

open browser to https://ceajenkins.ngrok.io and log in
click “New Item”
Enter an item name: run cea test on merge to master
- Choose “Freestyle project”
- Project name: “run cea test on merge to master”
- Description: “Check out the CityEnergyAnalyst, and run binceatestall.bat”
- check “Discard old builds”
  - Strategy: “Log Rotation”
  - Max # of builds to keep: 10
- check “GitHub project”
- Project url: “https://github.com/architecture-building-systems/CityEnergyAnalyst”
- section “Source Code Management”:
  - select “Git”
  - Repository URL: https://github.com/architecture-building-systems/CityEnergyAnalyst.git
  - Credentials: (use the ones created above)
  - Branches to build: refs/heads/master
- section “Build Triggers”:
  - check “GitHub hook trigger for GITScm polling”
  - check “Poll SCM”
- section “Build”
  - Execute Windows batch command: bin\ceatestall.bat
- section “Build Environment”
  - select “Delete workspace before build starts”
open GitHub Webhooks
- (NOTE: This should already be set up for the CEA Repository, but here’s how to configure it just in case)
- dropdown “Add webhook”
  - Payload URL: http://ceajenkins.ngrok.io/git/notifyCommit?url=https://github.com/architecture-building-systems/CityEnergyAnalyst
  - under “Which events would you like to trigger this webhook?” select “Let me select individual events.”
  - select “Just the push event”

Running the CEA in Docker¶

Docker is an open-source project for automating the deployment of applications as portable, self-sufficient containers that can run on the cloud or on-premises. (Source)

The CEA can be run in docker. The main steps are:

install Docker on your computer (out of scope of this document)
build the image
run the image

Note, that “CEA” in this context refers to the backend (server, cli) part of the CEA and not the GUI.

Building the image¶

Set server host to 0.0.0.0. You can do it by editing default.config in the cea folder. In the [server] section, change the default address from 127.0.0.1 to 0.0.0.0.

To build the docker image, navigate to CityEnergyAnalyst repository where the Dockerfile is located. Execute the following command:

> docker build -t dockeruser/cea:latest .

Notice the . at the end of the command - be sure to include it, as it tells docker where to find the Dockerfile.

The docker image should show up in your local computer:

> docker images
REPOSITORY                      TAG       IMAGE ID       CREATED          SIZE
dockeruser/cea                  latest    9963cd876a48   19 minutes ago   3.06GB

To share the docker image, push the image to Dockerhub:

> docker login
    username: dockeruser
    password:
> docker push dockeruser/cea:latest

Pull docker image¶

If you wish to use the latest cea image without building it on your own, you can pull it from our dockerhub.

To pull a docker image from Dockerhub:

> docker pull dockeruser/cea

Running the image in a new container¶

To test the docker image:
```
> docker run --rm dockeruser/cea cea test
```
- The --rm flag removes the container after it finishes running. This is useful when running the cea test

command so that the container does not persist after it exits after running the tests.

To run the docker container via shell (as the CEA Console):
```
> docker run -it -v /home/cea_projects:/projects dockeruser/cea /bin/bash
root@df9d4b16e5c0:> source /venv/bin/activate
(venv) root@df9d4b16e5c0:> cea --help
```
- The -it flag sets up interactive and tty so you can actually _do_ something there. Note, in order to use any of the CEA functionality, you’ll need to type source /venv/bin/activate.
- The -v /home/cea_projects:/projects binds the folder /home/cea_projects in host to the folder /projects inside the container. Files saved in /home/cea_projects will be shared with the container.

To run cea workflow. First make sure the project folder and workflow.yml are in the correct path, in the example is /home/cea_projects.:

> docker run --name cea_container -v /home/cea_projects:/projects dockeruser/cea cea workflow --workflow /projects/workflow.yml

To connect the GUI, CEA Dashboard, to a container

> docker run -t -p 5050:5050 dockeruser/cea

This command will start the CEA server and display it’s output. You should see something like this:
City Energy Analyst version 3.24.0
Running `cea dashboard` with the following parameters:
- general:debug = False
  (default: False)
start socketio.run
There’s quite a lot going on here and if this is seems daunting, I suggest reading up on some Docker tutorials - I don’t understand it well enough myself to feel confident enough to explain. But here are some observations:

The -t flag connects the container to your terminal, so you can see the output. You can drop this argument, but then you’ll not be able to see any error messages etc. of the backend.

The -p 5050:5050 flag connects the port 5050 on the host machine (your computer) to the port 5050 in the container (an instance of the cea-server docker image).

If you browse to http://localhost:5050/api/ you will see a description of the api you can use. This is the same api used by the CityEnergyAnalyst-GUI project, so you can essentially do anything that can be done in the GUI programmatically using this api.

How to clean up CEA git repository¶

This section describes the steps necessary to clean up the City Energy Analyst (CEA) git repository.

CEA Git Repository¶

Over the years, the CEA git repository has accumulated a lot of files in it’s git history, many of which we do not use anymore. Because of this, the size of the repository to be around 1.7GB (as of December 2021) even though the size of the main source code is only around 200MB. This makes it really slow to clone the repository from GitHub.

Cleaning up the Repository¶

With the help of the git-filter-repo tool, we will remove large and/or old files, which are not relevant to the current version of CEA anymore, “permanently” from the git history.

Note

These steps are considered “permanent” only if the steps here are followed correctly. It is still possible for the changes to be reverted if the old git history is reintroduced (merged) into the new (clean) history by some way. In that case, we can redo these steps again to remove it.

Pre-flight Checks¶

Make sure that you have backed up the existing CEA repository somewhere safe.
- This could be done by cloning the latest CEA repository to your local machine and copying folder somewhere else.
Inform internal developers to complete and merge any branches that they are currently working on into the main branch.
- This is to ensure that local git branches (local machines) are not going to reintroduce any old history into the remote git repository (GitHub).
- If for some reason that is not possible, one way to solve it is to do a rebase instead of a merge commit when merging the branch using GitHub Pull Requests. Read here if you want to know more about the difference.

Prerequisites (software)¶

Have git installed and accessible through terminal.
- For Windows, use the Git Bash Terminal that is installed with the Git for Windows installation.
Install git-filter-repo tool.

Using the git-filter-repo tool¶

After all the necessary checks are done, we can run the git-filter-repo tool using 2 ways, manually or using a script.

Manually¶

Open a Terminal / Command Prompt.
Enter cd PATH_TO_CEA_REPO, replacing PATH_TO_CEA_REPO with the path of your local CEA git repository.
Enter git filter-repo --invert-paths --paths-from-file ./bin/files_to_remove.txt

Using Script (Experimental)¶

Open a Terminal
- For Windows, use the Git Bash Terminal that is installed with the Git for Windows installation.
Enter PATH_TO_CEA_REPO/bin/repo_cleanup.sh, replacing PATH_TO_CEA_REPO with the path of your local CEA git repository.

We can then proceed to update these changes to GitHub

Updating git history of CEA repo on GitHub¶

To update the git history on GitHub, follow these steps from step 7.

Adding additional files to clean from history¶

If you want to remove other files from the history, other than the ones found in bin/files_to_remove.txt, add the path of the new lines to the file and re-run the tool as per above. Read this document for more information on how to use the git-filter-repo tool.

API reference¶

cea package¶

exception cea.ConfigError[source]¶

Bases: exceptions.Exception

Raised when the configuration of a tool contains some invalid values.

rc = 100¶

exception cea.CustomDatabaseNotFound[source]¶

Bases: exceptions.Exception

Raised when the InputLocator can’t find a user-provided database (region==’custom’)

rc = 101¶

exception cea.InvalidOccupancyNameException[source]¶

Bases: exceptions.Exception

Raised when the occupancy.dbf has an invalid / unknown occupancy column

rc = 104¶

exception cea.MissingInputDataException[source]¶

Bases: exceptions.Exception

Raised when a script can’t run because some information is missing

rc = 103¶

exception cea.ScriptNotFoundException[source]¶

Bases: exceptions.Exception

Raised when an invalid script name is used.

rc = 102¶

cea.suppress_3rd_party_debug_loggers()[source]¶: set logging level to WARN for fiona and shapely and others

Subpackages¶

cea.analysis package¶

Subpackages¶

cea.analysis.costs package¶

Submodules¶

cea.analysis.costs.equations module¶

cea.analysis.costs.system_costs module¶

cea.analysis.lca package¶

Submodules¶

cea.analysis.lca.embodied module¶

cea.analysis.lca.main module¶

cea.analysis.lca.operation module¶

cea.analysis.multicriteria package¶

Submodules¶

cea.analysis.multicriteria.main module¶

cea.datamanagement package¶

Submodules¶

cea.datamanagement.archetypes_mapper module¶

cea.datamanagement.constants module¶

This file contains the constants used in the data management tools

cea.datamanagement.create_new_scenario module¶

cea.datamanagement.data_initializer module¶

cea.datamanagement.data_migrator module¶

cea.datamanagement.databases_verification module¶

cea.datamanagement.schedule_helper module¶

cea.datamanagement.streets_helper module¶

cea.datamanagement.surroundings_helper module¶

cea.datamanagement.terrain_helper module¶

cea.datamanagement.weather_helper module¶

cea.datamanagement.zone_helper module¶

cea.demand package¶

Subpackages¶

cea.demand.schedule_maker package¶

Submodules¶

cea.demand.schedule_maker.schedule_maker module¶

Submodules¶

cea.demand.airconditioning_model module¶

cea.demand.building_properties module¶

cea.demand.calc_tm module¶

cea.demand.constants module¶

This file contains the constants used in the building energy demand calculations

cea.demand.control_heating_cooling_systems module¶

cea.demand.control_ventilation_systems module¶

cea.demand.datacenter_loads module¶

cea.demand.demand_main module¶

cea.demand.demand_writers module¶

cea.demand.electrical_loads module¶

cea.demand.hotwater_loads module¶

cea.demand.hourly_procedure_heating_cooling_system_load module¶

cea.demand.latent_loads module¶

cea.demand.rc_model_SIA module¶

cea.demand.rc_model_sia_cc module¶

cea.demand.refrigeration_loads module¶

cea.demand.sensible_loads module¶

cea.demand.space_emission_systems module¶

cea.demand.thermal_loads module¶

cea.demand.ventilation_air_flows_detailed module¶

cea.demand.ventilation_air_flows_simple module¶

cea.dev package¶

Submodules¶

cea.dev.build module¶

cea.examples package¶

Submodules¶

cea.examples.extract_reference_case module¶

cea.examples.template module¶

cea.interfaces package¶

Subpackages¶

cea.interfaces.cli package¶

Submodules¶

cea.interfaces.cli.cea_config module¶

cea.interfaces.cli.cea_dev module¶

cea.interfaces.cli.cea_doc module¶

cea.interfaces.cli.cli module¶

cea.interfaces.cli.dbf_to_excel module¶

cea.interfaces.cli.excel_to_dbf module¶

cea.interfaces.cli.excel_to_shapefile module¶

cea.interfaces.cli.list_demand_graphs_fields module¶

cea.interfaces.cli.shapefile_to_excel module¶

cea.interfaces.dashboard package¶

Subpackages¶

cea.interfaces.dashboard.api package¶

Submodules¶

cea.interfaces.dashboard.api.dashboard module¶

cea.interfaces.dashboard.api.databases module¶

cea.interfaces.dashboard.api.glossary module¶

cea.interfaces.dashboard.api.inputs module¶

cea.interfaces.dashboard.api.project module¶

cea.interfaces.dashboard.api.tools module¶

cea.interfaces.dashboard.api.utils module¶

cea.interfaces.dashboard.plots package¶

Submodules¶

cea.interfaces.dashboard.plots.routes module¶

cea.interfaces.dashboard.server package¶

Submodules¶

cea.interfaces.dashboard.server.jobs module¶

cea.interfaces.dashboard.server.streams module¶

Submodules¶

cea.interfaces.dashboard.dashboard module¶

cea.optimization package¶

Subpackages¶

cea.optimization.distribution package¶

Submodules¶

cea.optimization.distribution.network_optimization_features module¶

cea.optimization.master package¶

Submodules¶

cea.optimization.master.cost_model module¶

cea.optimization.master.crossover module¶

Crossover routines

class cea.optimization.master.crossover.CrossOverMethodsContinuous(crossover_method)[source]¶

Bases: object

mutation methods for integers

__init__(crossover_method)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

crossover(individual_1, individual_2, probability)[source]¶

class cea.optimization.master.crossover.CrossOverMethodsInteger(crossover_method)[source]¶

Bases: object

mutation methods for integers

__init__(crossover_method)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

crossover(individual_1, individual_2, probability)[source]¶

cea.optimization.master.crossover.crossover_main(ind1, ind2, indpb, column_names, heating_unit_names_share, cooling_unit_names_share, column_names_buildings_heating, column_names_buildings_cooling, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed, crossover_method_integer, crossover_method_continuous)[source]¶

cea.optimization.master.data_saver module¶

cea.optimization.master.emissions_model module¶

cea.optimization.master.emissions_model.calc_emissions_Whyr_to_tonCO2yr(E_Wh_yr, factor_kgCO2_to_MJ)[source]¶

cea.optimization.master.emissions_model.calc_pen_Whyr_to_MJoilyr(E_Wh_yr, factor_MJ_to_MJ)[source]¶

cea.optimization.master.evaluation module¶

cea.optimization.master.generation module¶

Create individuals

cea.optimization.master.generation.calc_building_connectivity_dict(building_names_all, building_names_heating, building_names_cooling, DHN_barcode, DCN_barcode)[source]¶

cea.optimization.master.generation.generate_main(individual_with_names_dict, column_names, column_names_buildings_heating, column_names_buildings_cooling, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed)[source]¶: Creates an individual configuration for the evolutionary algorithm. The individual is divided into four parts namely Heating technologies, Cooling Technologies, Heating Network and Cooling Network Heating Technologies: This block consists of heating technologies associated with % of the peak capacity each technology is going to supply, i.e. 10.1520.2030, which translates into technology 1 corresponding to 15% of peak capacity, technology 2 corresponding to 20% and technology 3 corresponding to 0%. 0% can also be just done by replacing 3 with 0. The technologies block is then followed by supply temperature of the DHN and the number of units it is supplied to among AHU, ARU, SHU. So if it is 6 degrees C supplied by DHN to AHU and ARU, it is represented as 6.02. The temperature is represented with 1 decimal point. Cooling Technologies: This follows the same syntax as heating technologies, but will be represented with cooling technologies. The block length of heating and cooling can be different. Heating Network: Network of buildings connected to centralized heating Cooling Network: Network of buildings connected to centralized cooling. Both these networks can be different, and will always have a fixed length corresponding to the total number of buildings in the neighborhood :param nBuildings: number of buildings :type nBuildings: int :return: individual: representation of values taken by the individual :rtype: list

cea.optimization.master.generation.individual_to_barcode(individual, building_names_all, building_names_heating, building_names_cooling, column_names, column_names_buildings_heating, column_names_buildings_cooling)[source]¶: Reads the 0-1 combination of connected/disconnected buildings and creates a list of strings type barcode i.e. (“12311111123012”) :param individual: list containing the combination of connected/disconnected buildings :type individual: list :return: indCombi: list of strings :rtype: list

cea.optimization.master.generation.populate_individual(empty_individual_with_names_dict, name_share_conversion_technologies, technologies_allowed, columns_buildings_name)[source]¶

cea.optimization.master.master_main module¶

cea.optimization.master.master_to_slave module¶

cea.optimization.master.mutations module¶

Mutation routines

class cea.optimization.master.mutations.MutationMethodContinuos(mutation_method)[source]¶

Bases: object

mutation methods for continuos variables

__init__(mutation_method)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

mutate(individual, probability)[source]¶

class cea.optimization.master.mutations.MutationMethodInteger(mutation_method)[source]¶

Bases: object

mutation methods for integers

__init__(mutation_method)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

mutate(individual, probability)[source]¶

cea.optimization.master.mutations.mutation_main(individual, indpb, column_names, heating_unit_names_share, cooling_unit_names_share, column_names_buildings_heating, column_names_buildings_cooling, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed, mutation_method_integer, mutation_method_continuous)[source]¶

cea.optimization.master.normalization module¶

cea.optimization.master.normalization.minmax_scaler(value, min_value, max_value)[source]¶

cea.optimization.master.normalization.normalize_fitnesses(scaler_dict, fitnesses_population)[source]¶

cea.optimization.master.normalization.scaler_for_normalization(number_of_objectives, fitnesses)[source]¶

cea.optimization.master.performance_aggregation module¶

cea.optimization.master.summarize_network module¶

cea.optimization.master.validation module¶

Validation

cea.optimization.master.validation.validation_main(individual_with_name_dict, column_names_buildings_heating, column_names_buildings_cooling, district_heating_network, district_cooling_network, technologies_heating_allowed, technologies_cooling_allowed)[source]¶

cea.optimization.preprocessing package¶

Submodules¶

cea.optimization.preprocessing.decentralized_building_main module¶

cea.optimization.preprocessing.decentralized_buildings_cooling module¶

cea.optimization.preprocessing.decentralized_buildings_heating module¶

cea.optimization.preprocessing.preprocessing_main module¶

cea.optimization.preprocessing.processheat module¶

cea.optimization.slave package¶

Subpackages¶

cea.optimization.slave.seasonal_storage package¶

Submodules¶

cea.optimization.slave.seasonal_storage.Import_Network_Data_functions module¶

cea.optimization.slave.seasonal_storage.SolarPowerHandler_incl_Losses module¶

cea.optimization.slave.seasonal_storage.design_operation module¶

cea.optimization.slave.seasonal_storage.storage_main module¶

Submodules¶

cea.optimization.slave.cooling_main module¶

cea.optimization.slave.cooling_resource_activation module¶

cea.optimization.slave.electricity_main module¶

cea.optimization.slave.heating_main module¶

cea.optimization.slave.heating_resource_activation module¶

cea.optimization.slave.natural_gas_main module¶

cea.optimization.slave.test module¶

cea.optimization.slave.test.main()[source]¶

Submodules¶

cea.optimization.constants module¶

This file contains the constants used in objective function calculation in optimization

cea.optimization.lca_calculations module¶

cea.optimization.optimization_main module¶

cea.optimization.prices module¶

cea.optimization.slave_data module¶

cea.plots package¶

Subpackages¶

cea.plots.comparisons package¶

Submodules¶

cea.plots.comparisons.Annual_costs module¶

cea.plots.comparisons.Annual_emissions module¶

cea.plots.demand package¶

Submodules¶

cea.plots.demand.comfort_chart module¶

cea.plots.demand.energy_balance module¶

cea.plots.demand.energy_end_use module¶

cea.plots.demand.energy_end_use_intensity module¶

cea.plots.demand.energy_final_use module¶

cea.plots.demand.energy_use_intensity module¶

cea.plots.demand.heating_reset_schedule module¶

cea.plots.demand.load_curve module¶

cea.plots.demand.load_curve_supply module¶

cea.plots.demand.load_duration_curve module¶

cea.plots.demand.load_duration_curve_supply module¶

cea.plots.demand.peak_load module¶

cea.plots.demand.peak_load_supply module¶

cea.plots.optimization package¶

Submodules¶

cea.plots.optimization.a_pareto_curve module¶

cea.plots.optimization.b_parallel_coordinates module¶

cea.plots.optimization.c_annual_costs module¶

cea.plots.optimization.d_annual_emissions module¶

cea.plots.optimization.e_Investment_costs module¶

cea.plots.optimization.f_paretocurve_convergence module¶

cea.plots.solar_potential package¶

Submodules¶

cea.plots.solar_potential.a_solar_radiation module¶

cea.plots.supply_system package¶

Submodules¶

cea.plots.supply_system.a_supply_system_map module¶

cea.plots.supply_system.b_installed_capacities module¶

cea.plots.supply_system.c_requirements_curve_electricity module¶

cea.plots.supply_system.d_dispatch_curve_electricity module¶

cea.plots.supply_system.e_dispatch_curve_heating_plant module¶

cea.plots.supply_system.f_dispatch_curve_cooling_plant module¶

cea.plots.supply_system.g_grid_ramping_capacity module¶

cea.plots.technology_potentials package¶

Submodules¶

cea.plots.technology_potentials.a_photovoltaic_potential module¶

cea.plots.technology_potentials.b_photovoltaic_thermal_potential module¶

cea.plots.technology_potentials.c_solar_collector_ET_potential module¶

cea.plots.thermal_networks package¶

Submodules¶

cea.plots.thermal_networks.a_network_design module¶

cea.plots.thermal_networks.b_demand_curve module¶

cea.plots.thermal_networks.c_annual_energy_consumption module¶

cea.plots.thermal_networks.d_energy_loss_bar module¶

cea.plots.thermal_networks.e_heating_reset_curve module¶

cea.plots.thermal_networks.f_pump_duration_curve module¶

Submodules¶

cea.plots.base module¶

cea.plots.cache module¶

cea.plots.categories module¶

cea.plots.colors module¶

cea.plots.plot_cli module¶

cea.plots.variable_naming module¶

cea.resources package¶

Subpackages¶

cea.resources.radiation_daysim package¶

Submodules¶

cea.resources.radiation_daysim.daysim_main module¶

cea.resources.radiation_daysim.geometry_generator module¶

cea.resources.radiation_daysim.radiance module¶

cea.resources.radiation_daysim.radiation_main module¶

cea.resources.radiation_daysim.visualization module¶

Submodules¶

cea.resources.geothermal module¶

cea.resources.natural_gas module¶

natural gas

cea.resources.natural_gas.calc_Cinv_gas(PnomGas)[source]¶

Calculate investment cost of natural gas connections.

Parameters:	PnomGas (float) – peak natural gas supply in [W]
Returns InvCa:
Rtype InvCa:

cea.resources.sewage_heat_exchanger module¶

cea.resources.water_body_potential module¶

cea.technologies package¶

Subpackages¶

cea.technologies.network_layout package¶

Submodules¶

cea.technologies.network_layout.connectivity_potential module¶

cea.technologies.network_layout.main module¶

cea.technologies.network_layout.minimum_spanning_tree module¶

cea.technologies.network_layout.steiner_spanning_tree module¶

cea.technologies.network_layout.substations_location module¶

cea.technologies.network_layout.utility module¶

Shapefile¶

Generates a networkx.DiGraph from point and line shapefiles.

“The Esri Shapefile or simply a shapefile is a popular geospatial vector data format for geographic information systems software. It is developed and regulated by Esri as a (mostly) open specification for data interoperability among Esri and other software products.” See https://en.wikipedia.org/wiki/Shapefile for additional information.

cea.technologies.network_layout.utility.read_shp(path, simplify=True, geom_attrs=True, strict=True)[source]¶

Generates a networkx.DiGraph from shapefiles. Point geometries are translated into nodes, lines into edges. Coordinate tuples are used as keys. Attributes are preserved, line geometries are simplified into start and end coordinates. Accepts a single shapefile or directory of many shapefiles.

“The Esri Shapefile or simply a shapefile is a popular geospatial vector data format for geographic information systems software [1].”

Parameters:	path (str) – File, directory, or filename to read. simplify (bool) – If True, simplify line geometries to start and end coordinates. If False, and line feature geometry has multiple segments, the non-geometric attributes for that feature will be repeated for each edge comprising that feature. geom_attrs (bool) – If True, include the Wkb, Wkt and Json geometry attributes with each edge. NOTE: if these attributes are available, write_shp will use them to write the geometry. If nodes store the underlying coordinates for the edge geometry as well (as they do when they are read via this method) and they change, your geomety will be out of sync. strict (bool) – If True, raise NetworkXError when feature geometry is missing or GeometryType is not supported. If False, silently ignore missing or unsupported geometry in features.
Returns:	the NetworkX graph
Raises:	ImportError – If ogr module is not available. RuntimeError – If file cannot be open or read. NetworkXError – If strict=True and feature is missing geometry or GeometryType is not supported.

[1]	https://en.wikipedia.org/wiki/Shapefile

cea.technologies.network_layout.utility.write_shp(G, outdir)[source]¶

Writes a networkx.DiGraph to two shapefiles, edges and nodes. Nodes and edges are expected to have a Well Known Binary (Wkb) or Well Known Text (Wkt) key in order to generate geometries. Also acceptable are nodes with a numeric tuple key (x,y).

“The Esri Shapefile or simply a shapefile is a popular geospatial vector data format for geographic information systems software [2].”

:param str outdir : directory path, Output directory for the two shapefiles. :rtype: None

Examples:

nx.write_shp(digraph, '/shapefiles') # doctest +SKIP

[2]	https://en.wikipedia.org/wiki/Shapefile

cea.technologies.solar package¶

Submodules¶

cea.technologies.solar.constants module¶

Parameters used for solar technologies

cea.technologies.solar.photovoltaic module¶

cea.technologies.solar.photovoltaic_thermal module¶

cea.technologies.solar.solar_collector module¶

cea.technologies.thermal_network package¶

Submodules¶

cea.technologies.thermal_network.simplified_thermal_network module¶

cea.technologies.thermal_network.substation_matrix module¶

cea.technologies.thermal_network.thermal_network module¶

cea.technologies.thermal_network.thermal_network_costs module¶

cea.technologies.thermal_network.thermal_network_loss module¶

Hydraulic - thermal network

cea.technologies.thermal_network.thermal_network_loss.calc_temperature_out_per_pipe(t_in, m, k, t_ground)[source]¶

Parameters:	t_in – in Kelvin m – in kg/s k – in kW/K t_ground – in Kelvin
Returns:

cea.technologies.thermal_network.thermal_network_optimization module¶

Submodules¶

cea.technologies.blinds module¶

blinds

cea.technologies.blinds.calc_blinds_activation(radiation, g_gl, Rf_sh)[source]¶

This function calculates the blind operation according to ISO 13790.

Parameters:	radiation – radiation in [W/m2] g_gl – window g value Rf_sh – shading factor

cea.technologies.boiler module¶

cea.technologies.burner module¶

cea.technologies.chiller_absorption module¶

cea.technologies.chiller_vapor_compression module¶

cea.technologies.cogeneration module¶

cea.technologies.constants module¶

Constants used throughout the cea.technologies package.

History lesson: This is a first step at removing the cea.globalvars.GlobalVariables object.

cea.technologies.cooling_tower module¶

cea.technologies.direct_expansion_units module¶

cea.technologies.furnace module¶

cea.technologies.heat_exchangers module¶

cea.technologies.heating_coils module¶

cea.technologies.heatpumps module¶

cea.technologies.pumps module¶

cea.technologies.radiators module¶

cea.technologies.storage_tank module¶

cea.technologies.storage_tank_pcm module¶

cea.technologies.storagetank_cc module¶

cea.technologies.substation module¶

cea.technologies.supply_systems_database module¶

cea.technologies.tabs module¶

cea.technologies.thermal_storage module¶

cea.tests package¶

Subpackages¶

cea.tests.datamanagement package¶

Submodules¶

cea.tests.datamanagement.test_zone_helper module¶

Submodules¶

cea.tests.create_unittest_data module¶

cea.tests.run_all_plots module¶

cea.tests.run_unit_tests module¶

cea.tests.test_calc_thermal_loads module¶

cea.tests.test_check_for_radiation_input_in_demand_script module¶

cea.tests.test_chiller_vapor_compression module¶

cea.tests.test_config module¶

cea.tests.test_dbf module¶

cea.tests.test_inputlocator module¶

cea.tests.test_inputs_setup_workflow module¶

cea.tests.test_plots module¶

cea.tests.test_schedules module¶

cea.tests.test_schemas module¶

cea.tests.test_technologies module¶

cea.tests.trace_inputlocator module¶

cea.utilities package¶

class cea.utilities.devnull[source]¶

Bases: object

Suppress sys.stdout so that it goes to devnull for duration of the with block

__enter__()[source]¶

__exit__(exc_type, exc_val, exc_tb)[source]¶

__init__()[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

write(_)[source]¶

cea.utilities.identifier(s, sep='-')[source]¶

First, all characters are lowercased, then, any character that is not in ascii_lowercase is replaced with sep.

Parameters:	s (str) – the string to create an identifier of use_underscores (str) – if set to true, underscores (“_”) will be used instead of dashes (“-“)
Return type:	str

class cea.utilities.pushd(path)[source]¶

Bases: object

Manage an os.chdir so that at the end of a with block, the path is set back to what it was

__enter__()[source]¶

__exit__(exc_type, exc_val, exc_tb)[source]¶

__init__(path)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

cea.utilities.remap(x, in_min, in_max, out_min, out_max)[source]¶: Scale x from range [in_min, in_max] to [out_min, out_max] Based on this StackOverflow answer: https://stackoverflow.com/a/43567380/2260

cea.utilities.simple_memoize(obj)[source]¶

cea.utilities.unique(sequence)[source]¶

Return only the unique elements in sequence, preserving order.

Parameters:	sequence (Sequence[T]) – the sequence to unique-ify
Return type:	List[T]

Submodules¶

cea.utilities.color_fader module¶

cea.utilities.compile_pyd_files module¶

Compile the .pyd files using Numba pycc to speed up the calculation of certain modules. Currently used for:

calc_tm.pyd (used in demand/sensible_loads.py)
calc_radiator.pyd (used in technologies/radiators.py)

In order to run this script, you will need to install Numba. Try: conda install numba

cea.utilities.compile_pyd_files.compile_radiators()[source]¶

cea.utilities.compile_pyd_files.compile_rc_model_sia()[source]¶

cea.utilities.compile_pyd_files.compile_storagetank()[source]¶

cea.utilities.compile_pyd_files.copy_pyd(source, destination)[source]¶

cea.utilities.compile_pyd_files.delete_pyd(*pathspec)[source]¶: Delete the file with the pathspec. pathspec is an array of path segments.

cea.utilities.compile_pyd_files.main()[source]¶

cea.utilities.create_mixed_use_type module¶

cea.utilities.create_polygon module¶

cea.utilities.date module¶

cea.utilities.dbf module¶

cea.utilities.doc_glossary module¶

cea.utilities.doc_graphviz module¶

cea.utilities.doc_html module¶

cea.utilities.doc_schemas module¶

cea.utilities.epwreader module¶

cea.utilities.latin_hypercube module¶

cea.utilities.parallel module¶

Standardizes multiprocessing use. In the CEA, some functions are run using the standard multiprocessing library. They are run by map``ing the function to a list of arguments (see ``multiprocessing.Pool.map_async) and waiting for the processes to finish, while at the same time piping STDOUT, STDERR through cea.utilities.workerstream.QueueWorkerStream - this ensures that the dashboard interface can read the output from the sub-processes.

The way this was done in CEA < v2.23 included boiler plate code that needed to be repeated every time multiprocessing was used. Issue [#2344](https://github.com/architecture-building-systems/CityEnergyAnalyst/issues/2344) was a result of not applying this technique to the demand script.

This module exports the function map which is intended to replace both map_async and the builtin map function (which was used when config.multiprocessing == False). This simplifies multiprocessing.

cea.utilities.parallel.__apply_func_with_worker_stream(args)[source]¶

Call func, using queue to redirect stdout and stderr, with a tuple of args because multiprocessing.Pool.map only accepts one argument for the function.

This function is called _inside_ a separate process.

cea.utilities.parallel.__multiprocess_wrapper(func, processes, on_complete)[source]¶: Create a worker pool to map the function, taking care to set up STDOUT and STDERR

cea.utilities.parallel.single_process_wrapper(func, on_complete)[source]¶: The simplest form of vectorization: Just loop

cea.utilities.parallel.test(a, b)[source]¶

cea.utilities.parallel.vectorize(func, processes=1, on_complete=None)[source]¶

Similar to numpy.vectorize, this function wraps func so that it operates on sequences (of same length) of inputs and outputs a sequence of results, similar to map(func, *args).

The main point of using vectorize is to unify single-processing with multi-processing - if processes > 1, then multiprocessing is used and the function will be run on a pool of processes. STDOUT and STDERR of these processes are fed through a cea.workerstream.QueueWorkerStream so it can be shown in the dashboard job output.

The parameter on_complete is an optional callable that is called for each completed call of func. It takes 4 arguments:

i: the 0-based order in which this call was completed
n: the total number of function calls to be made
args: the arguments passed to this call to func
result: the return value of this call to func

Parameters:	func – The function to vectorize processes (int) – The number of processes to use (use `config.get_number_of_processes()`) on_complete – An optional function to call for each completed call to `func`.

cea.utilities.physics module¶

Physical functions

cea.utilities.physics.calc_rho_air(temp_air)[source]¶

Calculation of density of air according to 6.4.2.1 in [1]

temp_air : air temperature in (°C)

rho_air : air density in (kg/m3)

cea.utilities.physics.kelvin_to_fahrenheit(T_Kelvin)[source]¶

cea.utilities.rename_building module¶

cea.utilities.reporting module¶

cea.utilities.schedule_reader module¶

cea.utilities.solar_equations module¶

cea.utilities.standardize_coordinates module¶

cea.utilities.workerstream module¶

This file implements WorkerStream for capturing stdout and stderr.

class cea.utilities.workerstream.HttpWorkerStream(name, jobid, url)[source]¶

Bases: object

__init__(name, jobid, url)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

class cea.utilities.workerstream.QueueWorkerStream(name, q)[source]¶

Bases: object

File-like object for wrapping the output of the scripts with queues - to be created in child process

__init__(name, q)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

__repr__() <==> repr(x)[source]¶

close()[source]¶

flush()[source]¶

isatty()[source]¶

write(str)[source]¶

class cea.utilities.workerstream.WorkerStream(name, connection)[source]¶

Bases: object

File-like object for wrapping the output of the scripts into connection messages

__init__(name, connection)[source]¶: x.__init__(…) initializes x; see help(type(x)) for signature

__repr__() <==> repr(x)[source]¶

close()[source]¶

flush()[source]¶

isatty()[source]¶

write(str)[source]¶

cea.utilities.workerstream.stream_from_queue(q)[source]¶: Stream the contents from the queue to STDOUT / STDERR - to be called from parent process

cea.workflows package¶

Submodules¶

cea.workflows.workflow module¶

Submodules¶

cea.api module¶

cea.config module¶

cea.constants module¶

This file contains the constants used in many folders in CEA. IF few constants are only used in a subfolder, it is highly recommended to keep those constants in a separate file in the subfolder. This is to make sure we declare the constants closest to the point of usage.