Data Driven Type-2 Fuzzy Modeling and Control of Personalized Thermal Comfort

Since the room thermal comfort has high uncertainties, it is difficult but necessary to study the modeling and control of thermal comfort. This paper tries to solve the modeling and control problem for the room thermal comfort by utilizing the temperature and humidity data collected from the working or living room. Firstly, in order to discard the noisy data and to obtain the reasonable intervals for modeling the temperature and humidity, a statistic method based preprocessing strategy is presented. Secondly, the Gaussian interval type-2 fuzzy set models are constructed to depict the personalized temperature and humidity comfort by measuring the uncertainty degrees of the obtained intervals. Thirdly, a interval type-2 fuzzy method based control scheme is proposed to realize the personalized thermal comfort regulation. Finally, some experimental results are given. And, the results show that the proposed thermal comfort models and control scheme can not only recommend a reasonable temperature and humidity range, but also can realize the optimal thermal comfort control.