This Python library is designed to handle measured data from test benches or Building Energy Management Systems (BEMS). It offers physical model calibration frameworks and original AI methods.

The library includes the following features:

• Data cleaning: Based on Pandas, it uses Scikit-learn framework to simplify data cleaning process through the creation of pipelines for time series.
• Data plotting: Generates plots of measured data, visualizes gaps and cleaning methods effects.
• Physical model calibration: Provides base class to define calibration problem, uses Pymoo optimization methods for parameters identification.
• Building usage modeling: Generates time series of occupancy-related usage (Domestic Hot water consumption, grey water use...).
• AI tools for HVAC FDD: Includes artificial intelligence tools for Heating Ventilation and Air Conditioning (HVAC) systems fault detection and diagnostics (FDD).

The source code is currently hosted on GitHub at: https://github.com/BuildingEnergySimulationTools/corrai

Tutorials are available in the dedicated folder.

Released version are available at the Python Package Index (PyPI):

# PyPI
pip install corrai

The development of this library has been supported by METABUILDING LABS Project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 953193. The sole responsibility for the content of this library lies entirely with the author’s view. The European Commission is not responsible for any use that may be made of the information it contains.