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 off. 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 Neurothermostat
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 process repeats. Simulations
of the Neurothermostat were conducted using artificial 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.
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