ebcpy 0.7.1

Python Library used for different python modules for the analysis and optimization of energy systems, buildings and indoor climate

ebcpy

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:

• 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 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).

Installation

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

How to get started?

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.

How to cite ebcpy

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}
}

Time series data

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

>>> from ebcpy.data_types import load_time_series_data
>>> df = load_time_series_data(r"path_to_a_supported_file")

# From Datetime to float
df.tsd.to_float_index()
# From float to datetime
df.tsd.to_datetime_index()
# To clean your data and create a common frequency:
df.tsd.clean_and_space_equally(desired_freq="1s")

Documentation

Visit our official Documentation.

Problems or questions?

Please raise an issue here.

For other inquires, please contact [e-mail].