In current centralized building climate control, occupants do not have much opportunity to intervene the
automated control system. This study explores the benefit of using thermal comfort feedback from
occupants in the model predictive control (MPC) design based on a novel dynamic thermal sensation
(DTS) model. This DTS model based MPC was evaluated in chamber experiments. A hierarchical structure
for thermal control was adopted in the chamber experiments. At the high level, an MPC controller
calculates the optimal supply air temperature of the chamber heating, ventilation, and air conditioning
(HVAC) system, using the feedback of occupants’ votes on thermal sensation. At the low level, the actual
supply air temperature is controlled by the chiller/heater using a PI control to achieve the optimal set
point. This DTS-based MPC was also compared to an MPC designed based on the Predicted Mean Vote
(PMV) model for thermal sensation. The experiment results demonstrated that the DTS-based MPC using
occupant feedback allows significant energy saving while maintaining occupant thermal comfort
compared to the PMV-based MPC.