End-to-end deep metamodeling to calibrate and optimize energy loads

• URL: http://arxiv.org/abs/2006.12390v1
• Date: Fri, 19 Jun 2020 07:40:11 GMT
• Title: End-to-end deep metamodeling to calibrate and optimize energy loads
• Authors: Max Cohen (TSP, IP Paris, SAMOVAR), Maurice Charbit (LTCI), Sylvain Le Corff (TSP, IP Paris, SAMOVAR), Marius Preda (TSP, IP Paris, SAMOVAR), Gilles Nozi\`ere
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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