Bias air temperature set-point for occupant thermal complaints via Bayesian algorithm

Current Building Management System (BMS) does not integrate well with real-time occupant response. A Bayesian control algorithm is presented in this study for fine-tuning the system to meet individual demands and to maximize the occupant acceptance of thermal environment of centralized air-conditioned offices. A weighting function for air temperature control using two control parameters(namely, the control temperature constant k T and the probable acceptance of the air temperature set-point λ) and two prior control functions of air temperature set-point (namely the uniform prior and the expert's prior) were examined for the algorithm Optimum air temperature set-points of air-conditioning systems obtained from certain Hong Kong offices were then used to demonstrate the applicability of the algorithm for controlling air temperature set-point within an example range. This algorithm would be useful for adaptive thermal comfort control in a large, post-occupied air-conditioned space.