The Neurothermostat: Predictive Optimal Control of Residential Heating Systems

Abstract

The Neurothermostat is an adaptive controller that regulates in(cid:173) door air temperature in a residence by switching a furnace on or off. The task is framed as an optimal control problem in which both comfort and energy costs are considered as part of the con(cid:173) trol objective. Because the consequences of control decisions are delayed in time, the N eurothermostat must anticipate heating de(cid:173) mands with predictive models of occupancy patterns and the ther(cid:173) mal response of the house and furnace. Occupancy pattern predic(cid:173) tion is achieved by a hybrid neural net / look-up table. The Neu(cid:173) rothermostat searches, at each discrete time step, for a decision sequence that minimizes the expected cost over a fixed planning horizon. The first decision in this sequence is taken, and this pro(cid:173) cess repeats. Simulations of the Neurothermostat were conducted using artificial occupancy data in which regularity was systemat(cid:173) ically varied, as well as occupancy data from an actual residence. The Neurothermostat is compared against three conventional poli(cid:173) cies, and achieves reliably lower costs. This result is robust to the relative weighting of comfort and energy costs and the degree of variability in the occupancy patterns.