Model Predictive Control for improving building system performance

Abstract

Model Predictive Control (MPC) has emerged as a promising technique for optimizing the performance of building systems. This paper presents a comprehensive study on the application of MPC in improving building system performance. The primary objective is to enhance energy efficiency, occupant comfort, and indoor environmental quality while considering various constraints and uncertainties. The paper reviews the theoretical foundations of MPC and its adaptation to building systems. It discusses the key challenges associated with implementing MPC in real-world scenarios, including model identification, system parameter uncertainties, and computational requirements. The study also highlights the potential benefits of MPC, such as reduced energy consumption, improved occupant comfort, and enhanced system reliability. Furthermore, various case studies are presented to demonstrate the effectiveness of MPC in different building systems, including heating, ventilation, air conditioning, and lighting. The findings contribute to the growing body of knowledge on MPC applications in building systems and offer valuable insights for researchers, engineers, and practitioners in the field of building energy management.