Bayesian modeling, Clustering, Learning thermal preference, Personalized preference profile
Incorporating occupant preferences in sensing and control operations of thermal systems has the potential to maximize thermal satisfaction and to supply energy when and where it is needed. Learning personalized thermal preferences is an essential part in this process. The latest studies have shown that it is possible to learn personalized thermal preference profiles using machine-learning and data-driven modeling algorithms. However, adequate data is required for each occupant, which is challenging in real buildings. This study presents: (i) a data-efficient method for learning personalized thermal preference profiles, based on Bayesian formalism, which shows good performance even with unobserved variables (i.e., air speed, metabolic rate, and clothing insulation level) and (ii) an efficient occupant feedback collection algorithm that minimizes disturbance of occupants. An experiment was conducted in identical perimeter offices, where human subjects were exposed to various thermal conditions and reported their respective thermal preference votes. Air temperature, globe temperature, and relative humidity were monitored continuously during the experiment. The collected data were used to infer occupants’ personalized thermal preference profiles, which showed better prediction performance and faster learning speed compared to existing methods. The method presented in this paper enables integration of occupant thermal preferences in smart environmental control systems of office buildings.