Package: ebcpy
ebcpyThisPYthon package provides generic functions and classes commonly
used for the analysis and optimization ofenergy systems,buildings and indoorclimate (EBC).Key features are:TimeSeriesDataSimulationAPI'sOptimization wrapperPre-/PostprocessingModelica utilitiesIt 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 reserach. We thus decoupled these interfaces intoebcpyand used the framework, for instance in the design optimization of heat pump systems (link).InstallationTo install, simply runpip install ebcpyIn order to use all optional dependencies (e.g.pymoooptimization), install via:pip install ebcpy[full]If you encounter an error with the installation ofscikit-learn, first installscikit-learnseparatly and then installebcpy:pip install scikit-learn
pip install ebcpyIf this still does not work, we refer to the troubleshooting section ofscikit-learn:https://scikit-learn.org/stable/install.html#troubleshooting. Also checkissue 23for updates.In order to help development, install it as an egg:git clone https://github.com/RWTH-EBC/ebcpy
pip install -e ebcpyHow to get started?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.ipynbOr, clone this repo and look at the examples\README.md file.
Here you will find several examples to execute.How to cite ebcpyPlease use the following metadata to citeebcpyin 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}
}TimeSeriesDataNote that we use our ownTimeSeriesDataobject which inherits frompd.DataFrame. The aim is to make tasks like loading different filetypes or applying multiple tags to one variable more convenient, while conserving the powerful tools of the DataFrame.
Just a quick intro here:Variables and tags>>> from ebcpy.data_types import TimeSeriesData
>>> tsd = TimeSeriesData(r"path_to_a_supported_file")
>>> print(tsd)
Variables    T_heater              T_heater_1            
Tags             meas         sim        meas         sim
Time                                                     
0.0        313.165863  313.165863  293.173126  293.173126
1.0        312.090271  310.787750  293.233002  293.352448
2.0        312.090027  310.796753  293.385925  293.719055
3.0        312.109436  310.870331  293.589233  294.141754As you can see, our first column level is always a variable, and the second one a tag.
This is especially handy when dealing with calibration or processing tasks, where you will have multiple
versions (tags) for one variable. The default tag israwto indicate the unmodified data.
To access a variable, you have to call.loc. To access multiple variables that all hold one tag usexs:# All tags:tsd.loc[:,"variable_name"]# One specific tag:tsd.loc[:,("variable_name","tag_name")]# One tag, all variables:tsd.xs("tag_name",axis=1,level=1)FloatIndex and DateTimeIndexMeasured data typically holds a datetime stamps (DateTimeIndex) while simulation result files hold absolute seconds (FloatIndex).
You can easily convert back and forth using:# From Datetime to floattsd.to_float_index()# From float to datetimetsd.to_datetime_index()# To clean your data and create a common frequency:tsd.clean_and_space_equally(desired_freq="1s")DocumentationVisit our officialDocumentation.Problems?Pleaseraise an issue here.