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ebcpy

Provides generic functions and classes commonly used for the analysis and optimization of energy systems, buildings and indoor climate.
https://github.com/RWTH-EBC/ebcpy

Category: Consumption
Sub Category: Buildings and Heating

Keywords

maintained tool

Keywords from Contributors

buildings urban-energy-modeling fiware fiware-iot-agents fiware-ngsi-v2 fiware-orion fiware-quantum-leap modelica modelica-library bim

Last synced: about 21 hours ago
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Repository metadata

README.md

E.ON EBC RWTH Aachen University

DOI
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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 ebc-tools@eonerc.rwth-aachen.de.

Owner metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 18 days ago

Total Commits: 679
Total Committers: 25
Avg Commits per committer: 27.16
Development Distribution Score (DDS): 0.43

Commits in past year: 53
Committers in past year: 6
Avg Commits per committer in past year: 8.833
Development Distribution Score (DDS) in past year: 0.453

Name Email Commits
fabian.wuellhorst f****t@r****e 387
fabian.wuellhorst f****t@e****e 103
Hendrik van der Stok h****k@r****e 84
Sebastian S****s@r****e 15
Thomas Storek t****k@e****e 15
jkriwet j****t@e****e 15
Philipp Mehrfeld p****d@r****e 10
Jonas Klingebiel j****l@e****e 9
David Jansen d****n@e****e 8
MichaMans m****s@h****m 6
marvin.kluge m****e@e****e 4
Tobias Schellen t****n@e****e 3
jonas.michael.baumgaertner j****r@r****e 3
FelixStege 3****e 3
Hannah Romberg h****g@e****e 2
Larissa l****n@r****e 2
mre H****3 2
Kai Droste k****e@e****e 1
Moritz Zuschlag m****g@r****e 1
Sebastian Blechmann 5****n 1
Tobias Spratte 1****e 1
saaiiravi s****1@g****m 1
thomas.storek t****k@r****e 1
Hendrik van der Stok h****k@e****e 1
zhiyu.pan z****n@r****e 1

Committer domains:

Issue and Pull Request metadata

Last synced: about 2 months ago

Total issues: 74
Total pull requests: 103
Average time to close issues: 4 months
Average time to close pull requests: 22 days
Total issue authors: 19
Total pull request authors: 15
Average comments per issue: 0.53
Average comments per pull request: 0.5
Merged pull request: 88
Bot issues: 0
Bot pull requests: 0

Past year issues: 10
Past year pull requests: 15
Past year average time to close issues: 6 days
Past year average time to close pull requests: 4 days
Past year issue authors: 6
Past year pull request authors: 5
Past year average comments per issue: 0.1
Past year average comments per pull request: 0.13
Past year merged pull request: 10
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/RWTH-EBC/ebcpy

Top Issue Authors

  • FWuellhorst (36)
  • HvanderStok (12)
  • tstorek (3)
  • jkriwet (3)
  • FelixNienaber (2)
  • HannahRomberg (2)
  • larissakuehn (2)
  • FelixStege (2)
  • MichaMans (2)
  • KaiDroste (1)
  • Cudok (1)
  • MZuschlag (1)
  • Maghnie (1)
  • SBlechmann (1)
  • DaJansenGit (1)

Top Pull Request Authors

  • FWuellhorst (58)
  • HvanderStok (20)
  • jkriwet (5)
  • FelixStege (3)
  • tosch4 (2)
  • MichaMans (2)
  • larissakuehn (2)
  • HannahRomberg (2)
  • SBlechmann (2)
  • DaJansenGit (2)
  • KaiDroste (1)
  • saaiiravi (1)
  • MZuschlag (1)
  • KBeeser (1)
  • tstorek (1)

Top Issue Labels

  • enhancement (8)
  • bug (7)
  • persistent (2)
  • good first issue (1)
  • refactor (1)
  • documentation (1)

Top Pull Request Labels

  • documentation (2)
  • hackday (1)
  • hacktoberfest-accepted (1)
  • bug (1)

Package metadata

pypi.org: ebcpy

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

  • Homepage: https://github.com/RWTH-EBC/ebcpy
  • Documentation: https://ebcpy.readthedocs.io/
  • Licenses: BSD 3-Clause
  • Latest release: 0.6.1 (published 3 months ago)
  • Last Synced: 2025-12-19T12:06:24.135Z (6 days ago)
  • Versions: 22
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 2,668 Last month
  • Rankings:
    • Dependent packages count: 10.052%
    • Downloads: 15.04%
    • Average: 16.469%
    • Stargazers count: 16.507%
    • Forks count: 19.104%
    • Dependent repos count: 21.642%
  • Maintainers (1)

Dependencies

requirements.txt pypi
  • fmpy >=0.2.27
  • h5py >=3.1.0
  • matplotlib >=3.3.4
  • numpy >=1.19.5
  • openpyxl >=3.0.5
  • pandas >=1.1.5
  • pydantic >=1.8.2
  • pymoo >=0.4.2
  • scikit-learn >=0.24.2
  • scipy >=1.5.4
  • tables >=3.6.1
  • xlrd >=2.0.1
docs/requirements.txt pypi
  • autodoc_pydantic *
  • m2r2 *
  • sphinx ==6.2.1
  • sphinx-material *
  • sphinx-rtd-theme *
setup.py pypi

Score: 14.053148555428937