Abstract
The escalating global energy demand, with buildings accounting for a significant portion, necessitates innovative approaches to enhance energy efficiency, particularly within Heating, Ventilation, and Air Conditioning (HVAC) systems. Traditional HVAC control, often based on fixed schedules, fails to adapt to dynamic occupancy patterns, leading to substantial energy waste and suboptimal thermal comfort. This paper investigates the integration of real-time occupancy detection with Model Predictive Control (MPC) strategies to optimize HVAC operation in smart office buildings. We review various occupancy detection technologies, from passive infrared to environmental sensors and advanced machine learning techniques, assessing their accuracy and application suitability. Subsequently, the principles of MPC are explored, emphasizing its capability to anticipate future conditions and make informed control decisions. A conceptual framework is proposed, detailing the synergistic operation of these technologies to achieve significant energy savings while maintaining occupant comfort. Our analysis, drawing upon existing research, demonstrates that occupancy-aware MPC can yield substantial reductions in HVAC energy consumption, ranging from 15% to 40% in typical office environments, alongside improved thermal comfort indices. Challenges such as sensor data uncertainty, computational complexity, and privacy concerns are critically discussed. The findings underscore the transformative potential of these integrated strategies for sustainable building management and pave the way for future research into adaptive learning and scalable deployment.