A personal touch to demand response: An occupant-centric control strategy for HVAC systems using personalized comfort models

Abstract

HAVC systems account for the majority of energy use in buildings. Therefore, research efforts have been made to develop control strategies to improve energy efficiency during the day and peak time. Studies have traditionally emphasized energy optimization while treating occupant experience using temperature constraints or standard generic metrics of comfort. A well-known strategy, in this category, is the use of a penalizing term when the temperature in an environment is deviated from a pre-defined setpoint. However, in reality, individual differences lead to diverse preferences and sensitivities to indoor thermal environments. Prior studies have not systematically evaluated the impact of such differences on user experience when using advanced control strategies for demand response. Accordingly, in this study, we have proposed a novel occupant-centric control framework (PICO: Personalization-Integrated Co-Optimization) that seeks to minimize energy cost (using dynamic pricing) with a penalizing term that is informed by probabilistic personalized comfort models of the occupants. We hypothesized that such integration results in increased efficiency (during the day and peak time) and improved user experience. Through a comprehensive uncertainty quantification analysis (to account for diversity in occupants' preferences, sensitivities, and number of occupants), we evaluated this framework by comparing it against three commonly used control strategies with varying levels of emphasis on user experience. Our analysis using numerous realizations of the framework operation for different combinations of simulated occupants showed that the proposed framework can adapt to different scenarios and improve the efficiency of operations. Summarizing the energy saving and user comfort experience metrics in an energy productivity measure (that quantifies the comfort gain per unit of energy use) we demonstrated that PICO increases peak time productivity up to 18.37% across various realizations. Moreover, the framework was demonstrated to be more consistent in providing an improved user experience reflected in a considerable reduction of standard deviations for thermal comfort experience, specifically for one occupant scenarios.