The Neural Network House: An Environment that Adapts to its Inhabitants

TLDR

Automated homes have remained unpopular because the cost of programming them outweighs perceived benefits, and most residents are unwilling to configure even simple devices. The authors propose a system that lets the home program itself by observing inhabitants’ lifestyles and anticipating their needs. The system controls core residential comfort functions—heating, lighting, ventilation, and water heating—using adaptive algorithms. A prototype installed in a real house demonstrates initial results and outlines the project's current status.

Abstract

Although the prospect of computerized homes has a long history, ho/ne automation has never become terribly popular because the benefits are seldom seen to outweigh the costs. One significant cost of an automated home is that someone has to program it to behave appropriately. Typical inhabitants do not want to program simple devices such as VCRs, let alone a much broader range of electronic devices, appliances, and comfort systems that have even greater functionality. We describe an alternative approach t in which the goal is for the home to essentially program itself by observing the lifestyle and desires of the inhabitants, and learning to anticipate and accommodate their needs. The system we have developed controls basic residential comfort systems--air heating, lighting, ventilation, and water heating. We have constructed a prototype system in an actual residence, and describe initial results and the current state of the project.