Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)
Michael C. Mozer, Lucky Vidmar, Robert Dodier
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.
For over a quarter century, the home automation industry has promised to revolu(cid:173) tionize our lifestyle with the so-called Smart House@ in which appliances, lighting, stereo, video, and security systems are integrated under computer control. How(cid:173) ever, home automation has yet to make significant 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 outfitted with over 75 sensors(cid:173) including temperature, light, sound, motion-and actua.tors to control air heating, water heating, lighting, and ventilation. In this paper, we describe one research
M. C. Mozer. L. Vidmar and R. H. Dodier
project within the house, the Neurothermostat, that learns to regulate the indoor air temperature automatically by observing and detecting patterns in the occupants' schedules and comfort preferences. We focus on the problem of air heating with a whole-house furnace, but the same approach can be taken with alternative or multiple heating devices, and to the problems of cooling and ventilation.
1 TEMPERATURE REGULATION AS AN OPTIMAL