Optimization of indoor air temperature set-point for centralized air-conditioned spaces in subtropical climates

Current Building Management System (BMS) does not integrate well with real-time occupant response. In order to fine-tune the system to meet individual demands and to maximize the occupant acceptance of indoor thermal environment, a new notion of Bayesian control algorithm was developed in this study. Control parameters of a weighting function for air temperature control (namely, the control temperature constant k_T and the probable acceptance of the air temperature set-point λ) and two prior distribution functions of air temperature set-point, namely the uniform prior and the expert's prior, were examined. Optimum air temperature set-points of air-conditioning systems obtained from certain Hong Kong offices were then used to demonstrate the applicability of the new algorithm for controlling an example air temperature set-point ranged between 0.2 °C and 1 °C. This algorithm would be useful for adaptive thermal comfort control in a large, post-occupied air-conditioned space.