MPC-Based Building Climate Controller Incorporating Humidity

Abstract

Although Model Predictive Control (MPC) has been widely investigated for energy efficient climate control of buildings, most prior works have neglected humidity in the problem formulation and performance evaluations. A climate control algorithm that ignores humidity cannot be used in practice, especially in hot-humid climates. Apart from the discomfort of occupants, high humidity over long periods will lead to issues such as mold growth, adversely impacting occupant health. In this paper, we provide an MPC formulation that explicitly accounts for humidity constraints in a principled manner. We show how to construct data-driven low order models of a cooling and dehumidifying coil that can be used in the MPC formulation. The resulting controller's performance is tested in simulation using a plant that differs significantly from the model used by the optimizer. In spite of the large plant model mismatch, the proposed MPC controller performs well. Humidity constraints are seen to be active for a large part of the day, especially in the summer. MPC formulations that ignore humidity would lead to a poor indoor climate in these situations.