LAUSR

Abstract The automatic control of the existing indoor thermal environment is usually based on the user's set points without considering their real-time thermal feeling, causing an uncomfortable thermal environment and high energy consumption. In this study, the wristband device's physiological data is utilized to propose a thermal sensation prediction-based fuzzy control method of indoor temperature to improve the indoor environment's thermal comfort and achieve an energy saving of air-conditioning system. The linear regression model-based updating method is utilized to update the thermal sensation prediction model in real-time. Moreover, Mamdani fuzzy model and FFSI method are adopted to develop the fuzzy control algorithm for indoor temperature set point optimization. Several comparative experiments are performed to compare control performance, thermal comfort, and energy consumption of thermal sensation prediction-based control with thermal sensation feedback-based control and temperature set point-based control. The results show that thermal sensation prediction-based control can adjust the temperature set point by monitoring physiological data from the subjects without interfering with their regular works. It is also revealed that the thermal comfort performance of thermal sensation prediction-based control is superior to the temperature set point-based control, while it is similar to the thermal sensation feedback-based control. Furthermore, the proposed control approach provides 20.07% and 10.73% savings in daily energy consumption compared with the temperature set point-based control and thermal sensation feedback-based control, respectively.