  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.
