Operation Strategy of Smart Thermostats That Self-Learn User Preferences

Smart thermostats can automatically adjust indoor temperature based on user preferences to save electricity bills without significantly comprising comfort. However, current smart thermostats usually require users to master programming or require a significant amount of user behavior observations to enable automatic control, which is demanding and adverse to their popularization. In this paper, we propose a practical method that enables a smart thermostat to track user preferences and derive the optimal temperature setting schedule. We assume that users are rational and aim to minimize their overall costs. First, we propose a Bayesian-inference-based method that can quickly learn user preferences with a limited number of user behavior observations. We then generate the optimal temperature setting schedule via a stochastic expected value model. Finally, we propose an operation strategy under which a thermostat can work automatically and continuously. The “virtual user” case study indicates that the proposed method can quickly yield a satisfying probabilistic estimate of user preferences based on even only 10 observations. The “real user” case study demonstrates that the method can dynamically track user preferences and continuously generate optimal temperature setting schedules to reduce overall costs an average of 12 percent. Based on the proposed method, users can conveniently enjoy a customized temperature zone with a lower overall cost.