Even though energy efficient climate control of 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 consistently. 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.
Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
College of Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA；Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA；Center for High Performance Buildings, Purdue University, 177 S Russell St, West Lafayette, IN 47907, USA；Trane Technologies, Residential R\&D Group, 6200 Troup Hwy, Tyler, TX 75707, USA；Bosch Center for Artificial Intelligence
Data-driven HVAC Control Using Symbolic Regression: Design and Implementation
Graduate school of Information Science and Technology, Osaka University（大阪大学信息科学与技术研究院）；Campus Sustainability Office, Osaka University（大阪大学校园可持续发展办公室）；Graduate School of Engineering, Osaka University（大阪大学工学院）
Determining optimal thermal energy storage charging temperature for cooling using integrated building and coil modeling
Industrial Science and Technology Research Institute, Inha University（仁荷大学工业科学与技术研究所）；Department of Smart City Engineering, Inha University（仁荷大学智慧城市工程学院）；Department of Architectural Engineering, INHA University（仁荷大学建筑工程学院）
