Energy-comfort optimization using discomfort history and probabilistic occupancy prediction

Heating ventilation and air-conditioning (HVAC) systems consume a significant portion of the energy within buildings. Current HVAC control systems use simple fixed occupant schedules, while proposed energy optimization schemes do not consider past discomfort in making future energy optimization decisions. We propose a Model-based predictive control (MPC) algorithm that adaptively balances energy and comfort while the system is in operation. The algorithm combines occupancy prediction with the history of occupant discomfort to constrain expected discomfort to an allowed budget. Our approach saves energy by dynamically shifting discomfort over time based on its real time performance. The system adapts its behavior according to the past discomfort and thus plays the dual role of saving energy when discomfort is smaller than the target budget, and maintaining comfort when the discomfort margin is small. Simulation results using synthetic benchmarks and occupancy traces demonstrate considerable energy savings over a smart reactive approach while meeting occupant comfort objectives.