A Comparative Study of Human-Centered HVAC System Energy Optimization for Demand Response

The effective control of heating, ventilation, and air conditioning (HVAC) systems can reduce peak energy demand and balance energy usage throughout the day. Co-optimizing energy cost and comfort is one of the main paradigms in HVAC systems' control. This paper presents a feasibility study of a human-centered co-optimization-based controller. Two methods were utilized to factor in comfort in optimization formulations including deviation from a temperature setpoint and the integration of personalized thermal comfort models. We compared the effectiveness of these two approaches in achieving comfort during peak times by hypothesizing that the integration of personalized comfort measures will result in an improved comfort experience for users during demand response. The controllers' performance was evaluated using a simulated benchmark small office and various combinations of comfort profiles for single occupancy scenarios. Our results indicate that integrating personalized comfort profiles into an optimization-based controller has the potential to improve users' comfort experience by considerably decreasing the variance of comfort experience compared to the conventional controller.