An occupant-centric air-conditioning system for occupant thermal preference recognition control in personal micro-environment

Thermal comfort is one of the most important factors indoor environment quality, affecting occupants’ well-being and work efficiency. With advent smart control technology, personalized intelligent air conditioners have been promoted for occupant-centric air-conditioning control. Based on commonly used (AC), this paper quantitatively describes method occupant thermal preference adaptation, proposes a rule-based classification recognition. quantitative description preference, multi-step input an fan-coil system. This provides that fulfills demands different preferences easy to implement with existing systems without additional sensors. To perform application-oriented, closed-loop research proposed method, two prediction models are developed based behavior dataset they could be as well initialized future online tunning by continually accumulated dataset. Moreover, aiming practical operation guide conventional systems, validates effectiveness accuracy integrated was done using Programmable Logic Controller (PLC) experiments Simulink simulations actual personal office room, equipped unit (FCU) in Shanghai. The results indicate dynamic temperature response modes can meet needs patterns.