End-to-end deep metamodeling to calibrate and optimize energy loads
This addresses energy management in large buildings, offering a practical solution for optimizing performance and comfort, though it appears incremental in applying existing techniques to this domain.
The paper tackles building energy optimization by developing an end-to-end metamodel using a Transformer network, calibrated with CMA-ES and real sensor data, to minimize energy loads while maintaining thermal comfort and air quality. The method achieves significant energy efficiency gains and is computationally more efficient than traditional physical models.
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.