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This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control. • Performance of two MPC controllers for HVAC systems is studied. • The study spans a wide range of climate and weather conditions, a first of its kind. • MPC controller can perform poorly unless latent heat is considered in design. • Poor performance occurs typically in moist and marine climate zones. [ABSTRACT FROM AUTHOR]

Even though energy-efficient climate control of commercial buildings using model predictive control (MPC) has been widely investigated, 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 this work, we propose an MPC controller in which humidity and latent heat are incorporated in a principled manner. We construct low order data-driven 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. Additionally, the performance of the proposed controller is compared with that of a naive MPC controller which does not explicitly consider humidity, and also to that of a conventional rule-based controller. Simulations show that the proposed MPC controller outperforms the other two in terms of energy use and thermal comfort. It is also observed that the naive MPC formulation which does not consider humidity leads to poor humidity control under certain conditions. Such violations in humidity can adversely affect occupant comfort and health. • Proposes an energy-efficient climate controller for HVAC systems in buildings. • The controller is based on MPC and incorporates humidity in a principled manner. • Performance comparison is made with a naive MPC which ignores humidity. • Proposed controller outperforms the naive MPC and a rule-based controller. • MPC ignoring humidity causes poor humidity control that can affect occupant health. [ABSTRACT FROM AUTHOR]