The neural network house: An overview
Typical home comfort systems utilize only rudimentary forms of energy
management and conservation. The most sophisticated technology in common use
today is an automatic setback thermostat. Tremendous potential remains for
improving the efficiency of electric and gas usage. However, home residents
who are ignorant of the physics of energy utilization cannot design
environmental control strategies, but neither can energy management experts
who are ignorant of the behavior patterns of the inhabitants. Adaptive
control seems the only alternative. We have begun building an adaptive
control system that can infer appropriate rules of operation for home comfort
systems based on the lifestyle of the inhabitants and energy conservation
goals. Recent research has demonstrated the potential of neural networks for
intelligent control. We are constructing a prototype control system in an
actual residence using neural network reinforcement learning and prediction
techniques. The residence is equipped with sensors to provide information
about environmental conditions (e.g., temperatures, ambient lighting level,
sound and motion in each room) and actuators to control the gas furnace,
electric space heaters, gas hot water heater, lighting, motorized blinds,
ceiling fans, and dampers in the heating ducts. This paper presents an
overview of the project as it now stands.
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