Model predictive control, energy efficiency, demand response, humidity, latent heat
Model predictive control (MPC) has been widely investigated for climate control of commercial buildings for both energy efficiency and demand flexibility. However, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably more challenging, primarily since a cooling and dehumidifying coil model which accounts for both sensible and latent heat transfers is needed. In our recent work, we proposed an MPC controller in which humidity and latent heat were incorporated in a principled manner, by using a reduced-order model of the cooling coil. Because of the highly nonlinear nature of the process in a cooling coil, the model needs to be modified based on certain weather/climatic conditions to have sufficient prediction accuracy. Doing so, however, leads to a mixed-integer nonlinear program (MINLP) that is challenging to solve. In this work, we propose an MPC formulation that retains the NLP (nonlinear programming problem) structure in all climate zones/weather conditions. This feature makes the control system capable of autonomous operation. Simulations in multiple climate zones and weather conditions verify the energy savings performance, and autonomy of the proposed controller. We also compare the performance of the proposed MPC controller with an MPC formulation that does not explicitly consider humidity. Under certain conditions, it is found that the MPC controller that excludes humidity leads to poor humidity control, or higher energy usage as it is unaware of the latent load on the cooling coil.