This PYthon package provides generic functions and classes commonly used for the analysis and optimization of energy systems, buildings and indoor climate (EBC).
Key features are:
TimeSeriesDataSimulationAPI's- Optimization wrapper
- Pre-/Postprocessing
- Modelica utilities
It was developed together with AixCaliBuHA, 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 into ebcpy 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 of scikit-learn, first install scikit-learn separatly and then install ebcpy:
pip install scikit-learn
pip install ebcpy
If this still does not work, we refer to the troubleshooting section of scikit-learn: https://scikit-learn.org/stable/install.html#troubleshooting. Also check issue 23 for updates.
In order to help development, install it as an egg:
git clone https://github.com/RWTH-EBC/ebcpy
pip install -e ebcpy
We recommend running our jupyter-notebook to be guided through a helpful 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 cite ebcpy 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 our own TimeSeriesData object which inherits from pd.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:
>>> 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 294.141754
As 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 is raw to indicate the unmodified data.
To access a variable, you have to call .loc. To access multiple variables that all hold one tag use xs:
# 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)Measured 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 float
tsd.to_float_index()
# From float to datetime
tsd.to_datetime_index()
# To clean your data and create a common frequency:
tsd.clean_and_space_equally(desired_freq="1s")Visit our official Documentation.
Please raise an issue here.