Development and evaluation of data-driven controls for residential smart thermostats

Abstract The advent of smart thermostats with real-time sensing raises the question of how to preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize energy usage while maintaining occupant comfort. To this end, we empirically compare a standard reactive deadband control to two new smart thermostat HVAC control methods: (1) a model-free reinforcement learning (RL) approach and (2) a novel model predictive control (MPC) method, whose solution is optimal with respect to its data-driven linear model. We evaluated the controls with 500 unique energy models of houses located in the United States. The models were modified to facilitate the short-term performance simulation required for residential HVAC systems. Overall, we found the MPC controller offers three distinct advantages over the RL and deadband methods: (1) MPC had the lowest average cost (defined as a custom weighted combination of runtime and comfort) of the evaluated controllers; (2) the MPC control’s linear model was able to reliably extrapolate from the sparse sample of training observations, thus enabling it to adapt quickly to recent data; and (3) in contrast to RL methods, MPC did not subject the houses or occupants to the discomfort of system exploration.