Scenario-based Nonlinear Model Predictive Control for Building Heating Systems

Abstract

State-of-the-art Model Predictive Control (MPC) applications for building heating adopt either a deterministic controller together with a nonlinear model or a linearized model with a stochastic MPC controller. However, deterministic MPC only considers one single realization of the disturbances and its performance strongly depends on the quality of the forecast of the disturbances, possibly leading to low performance. On the other hand, a linearized model can fail to capture some dynamics and behavior of the building under control. In this article, we combine a stochastic scenariobased MPC (SBMPC) controller together with a nonlinear Modelica model that is able to capture the dynamics of the building more accurately than linear models. The adopted SBMPC controller considers multiple realizations of the external disturbances obtained through a statistically accurate model, so as to consider different possible disturbance evolutions and to robustify the control action. We show the benefits of our proposed approach through several simulations in which we compare our method against the standard ones from the literature. We show how our approach outperforms both an SBMPC controller that uses a linearized model and a deterministic MPC controller that uses a nonlinear Modelica model.