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ThisPYthon package provides generic functions and classes commonlyused for the analysis and optimization ofenergy systems,buildings and indoorclimate (EBC).

Key features are:

• SimulationAPI's
• Optimization wrapper
• Useful loading of time series data and time series data accessor for DataFrames
• Pre-/Postprocessing
• Modelica utilities

It was developed together withAixCaliBuHA, a framework for an automated calibration of dynamic building and HVAC models. During this development, we found several interfaces relevant to further research. We thus decoupled these interfaces intoebcpy and used the framework, for instance in the design optimization of heat pump systems (link).

To install, simply run

pip install ebcpy

In order to use all optional dependencies (e.g.pymoo optimization), install via:

pip install ebcpy[full]

If you encounter an error with the installation ofscikit-learn, first installscikit-learn separatly and then installebcpy:

pip install scikit-learnpip install ebcpy

If this still does not work, we refer to the troubleshooting section ofscikit-learn:https://scikit-learn.org/stable/install.html#troubleshooting. Also checkissue 23 for updates.

In order to help development, install it as an egg:

git clone https://github.com/RWTH-EBC/ebcpypip install -e ebcpy

We recommend running our jupyter-notebook to be guided through ahelpful tutorial.
For this, run the following code:

# If jupyter is not already installed:pip install jupyter# Go into your ebcpy-folder (cd \path_to_\ebcpy) or change the path to tutorial.ipynb and run:jupyter notebook tutorial\tutorial.ipynb

Or, clone this repo and look at the examples\README.md file.Here you will find several examples to execute.

Please use the following metadata to citeebcpy in your research:

@article{Wuellhorst2022,  doi = {10.21105/joss.03861},  url = {https://doi.org/10.21105/joss.03861},  year = {2022},  publisher = {The Open Journal},  volume = {7},  number = {72},  pages = {3861},  author = {Fabian Wüllhorst and Thomas Storek and Philipp Mehrfeld and Dirk Müller},  title = {AixCaliBuHA: Automated calibration of building and HVAC systems},  journal = {Journal of Open Source Software}}

Note that we use steamline time series data based on apd.DataFrameusing a common function and the accessortsd.The aim is to make tasks like loading different filetypes or common functionsmore convenient, while conserving the powerful tools of the DataFrame.Just a example intro here:

>>>fromebcpy.data_typesimportload_time_series_data>>>df=load_time_series_data(r"path_to_a_supported_file")# From Datetime to floatdf.tsd.to_float_index()# From float to datetimedf.tsd.to_datetime_index()# To clean your data and create a common frequency:df.tsd.clean_and_space_equally(desired_freq="1s")

Visit our officialDocumentation.

Pleaseraise an issue here.

For other inquires, please contactebc-tools@eonerc.rwth-aachen.de.