The Neurothermostat: Predictive Optimal Control of Residential Heating Systems

The Neurothermostat is an adaptive controller that regulates indoor air temperature in a residence by switching a furnace on or oo. The task is framed as an optimal control problem in which both comfort and energy costs are considered as part of the control objective. Because the consequences of control decisions are delayed in time, the Neurothermostat must anticipate heating demands with predictive models of occupancy patterns and the thermal response of the house and furnace. Occupancy pattern prediction is achieved by a hybrid neural net / look-up table. The Neu-rothermostat searches, at each discrete time step, for a decision sequence that minimizes the expected cost over a xed planning horizon. The rst decision in this sequence is taken, and this process repeats. Simulations of the Neurothermostat were conducted using artiicial occupancy data in which regularity was systematically varied, as well as occupancy data from an actual residence. The Neurothermostat is compared against three conventional policies , 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. For over a quarter century, the home automation industry has promised to revolutionize our lifestyle with the so-called Smart House r in which appliances, lighting, stereo, video, and security systems are integrated under computer control. However , home automation has yet to make signiicant inroads, at least in part because software must be tailored to the home occupants. Instead of expecting the occupants to program their homes or to hire someone to do so, one would ideally like the home to essentially program itself by observing the lifestyle of the occupants. This is the goal of the Neural Network House (Mozer et al., 1995), an actual residence that has been outttted with over 75 sensors| including temperature, light, sound, motion|and actuators to control air heating, water heating, lighting, and ventilation. In this paper, we describe one research