Skip to Main Content
Dynamic thermal comfort control can effectively improve the levels of comfortable feeling, energy-saving, and human health. Traditional control theories and methods based on accurate physical model can not solve the control problems met during analyzing and controlling thermal comfort because of difficulties in obtaining the accurate model of a thermal environment. This paper presented a novel control strategy of dynamic thermal comfort using online and offline dada collected in thermal environment. Family members' fuzzy sensations are involved in the closed control loop. A six-input one-output CMAC neural network was used to learn the relationship between thermal variables and parameters and Predicted Mean Vote (PMV). This CMAC functions as a predictive PMV model. Another three-input one-output CMAC learned the nonlinear relationship between temperature decrease and humidity deduction in summer, as humidity deduction model when temperature changes in value. We also defined the concepts of personal and dynamic thermal comfort zones, proposing a fuzzy information fusion method of thermal sensation using fuzzy set theory. Procedure of computational experiments with these data-based predictive models above was designed to determine appropriate set points of thermal variables in order for thermal comfort value to periodically be within the desired personal comfort zone or dynamic thermal comfort zone. The control strategy given in the paper can help researchers design dynamic thermal comfort system in detail.