An occupant-centric air-conditioning system for occupant thermal preference recognition control in personal micro-environment. (June 2021) - Zhu, Mingya; Pan, Yiqun; Wu, Zejun; Xie, Jiantong; Huang, Zhizhong; Kosonen, Risto
Abstract: Thermal comfort is one of the most important factors of indoor environment quality, affecting occupants' well-being and work efficiency. With the advent of smart control technology, personalized and intelligent air conditioners have been promoted for occupant-centric intelligent air-conditioning control. Based on commonly used air-conditioning (AC), this paper quantitatively describes the method for occupant thermal preference adaptation, and proposes a rule-based classification method of occupant thermal preference recognition. With the quantitative description and classification of the occupant thermal preference, this paper proposes a multi-step input control method for an occupant-centric fan-coil system. This method provides an indoor thermal environment that fulfills the demands of different preferences and is easy to implement with existing air-conditioning control systems without additional sensors. To perform an application-oriented, closed-loop research of the proposed control method, two prediction models of occupant thermal preferences are developed based on an occupant behavior dataset and they could be used as the well initialized models for future online tunning by continually accumulated dataset. Moreover, aiming for a practical operation guide for conventional occupant-centric air-conditioning systems, this paper validates the effectiveness and accuracy of the proposed multi-step input control method, integrated with occupant thermal preference recognition. This was done by using Programmable Logic Controller (PLC) control experiments and Simulink simulations of an actual personal office room, equipped with a fan-coil unit (FCU) in Shanghai. The research results indicate that dynamic indoor air temperature response with different air-conditioning control modes can meet the control needs of different occupant thermal preference patterns. Highlights: Occupant thermal preference is classified by two indexes: AC setpoint and change rate of indoor air temperature. We establish ZeroR and BP neural network models to characterize occupant thermal preference in personal micro-environment. Multi-step input control strategy is proposed for integration with occupant thermal preference recognition. Multi-step input control strategy is effective for AC control system to accommodate variety of occupant thermal preference.