BESOS is a collection of modules for the simulation and optimization of buildings and urban energy systems. BESOS is designed to help researchers and practitioners to design more sustainable, district-integrated buildings. It integrates EnergyPlus and EnergyHub simulation software with optimization and machine learning functionality. this includes lots of help with 'surrogate modelling', where machine learning models are fitted to data generated by parametric runs of detailed simulation models. BESOS facilitates running large-scale parametric analyses of EnergyPlus or EnergyHub models with output in a pandas DataFrame and using this to train machine learning surrogate models with scikit-learn or TensorFlow. We provide access to commonly used optimization algorithms via existing optimization toolboxes.

The development of the besos library and the BESOS Platform was largely conducted by undergraduate students studying Computer Science or Software Engineering at the University of Victoria. Special thanks to coop programming team; Will Beckett, Peter Wilson, Mark Hills, Chase Xu, Dylan Kemp, Madhur Panwar, James Comish and Polina Chaikovsky. The work was funded by the CANARIE Research Software Program (https://www.canarie.ca/software/) (grant RS-327).