Stochastic learning and control of building dynamics for thermal comfort

In the past few decades, thermal comfort has been considered an aspect of a sustainable building in almost all sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear and complex building dynamics characterized by the time-varying environment with disturbances. The primary focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian Processes and incorporating it into Model Predictive Control (MPC) to minimize energy consumption and provide thermal comfort satisfaction. The full probabilistic capabilities of GPs is exploited from two perspectives: the mean prediction is used for the room temperature model, while the uncertainty is involved in the MPC objective not to lose the desired performance and design a robust controller. We illustrated the potentials of the proposed method in a numerical example with simulation results.

Keywords:

gaussian processes , indoor climate , machine learning , Model Predictive Control