In this paper, we propose a new end-to-end methodology to optimize the energy performance and the comfort, air quality and hygiene of large buildings. A metamodel based on a Transformer network is introduced and trained using a dataset sampled with a simulation program. Then, a few physical parameters and the building management system settings of this metamodel are calibrated using the CMA-ES optimization algorithm and real data obtained from sensors. Finally, the optimal settings to minimize the energy loads while maintaining a target thermal comfort and air quality are obtained using a multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency while being computationally much more appealing than models requiring a huge number of physical parameters to be estimated.
@article{arxiv.2006.12390,
title = {End-to-end deep metamodeling to calibrate and optimize energy loads},
author = {Max Cohen and Maurice Charbit and Sylvain Le Corff and Marius Preda and Gilles Nozière},
journal= {arXiv preprint arXiv:2006.12390},
year = {2020}
}