A data-driven framework for thermal comfort assessment method based on user interaction

Abstract

The rapid development of smart home systems requires a transition of thermal comfort studies. A feasible, user-friendly, consumer-oriented thermal comfort assessment method for large-scale residential applications is extremely urgent. However, most studies of thermal comfort models have focused on large commercial buildings. The application and deployment of such methods often rely on multiple prerequisites like relatively steady thermal conditions, regular energy consumption schedule, and additional sensor information. While situations are different in residential scenarios. In this paper, over 100 thousand air conditioner users were analyzed and a fundamental but overlooked question in thermal comfort research was established. In the household environment, the thermal condition is more complicated. Difference in age, gender, and usage preference leads to diversity in individual thermal comfort. Furthermore, introducing more sensors for better comfort prediction performance is not acceptable both from the cost perspective and the user perspective. To bridge the above missing gap, a novel thermal comfort assessment framework combining user interaction and metric learning was constructed. The proposed framework can be used to construct thermal comfort assessment systems by exploiting user interaction actions, which is a low-cost alternative and complementary to the traditional methods based on thermal equilibrium. The ease of construction makes the framework easy to integrate into smart home systems, addressing the difficulty of applying thermal comfort studies in residential scenarios.