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Pré-Publication, Document De Travail Année : 2020

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

Résumé

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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Dates et versions

hal-02873577 , version 1 (18-06-2020)
hal-02873577 , version 2 (30-01-2021)
hal-02873577 , version 3 (04-11-2021)

Identifiants

  • HAL Id : hal-02873577 , version 1

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Max Cohen, Maurice Charbit, Sylvain Le Corff, Marius Preda, Gilles Nozière. End-to-end deep metamodeling to calibrate and optimize energy loads. 2020. ⟨hal-02873577v1⟩
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