Control and learning of ambience by an intelligent building

TLDR: This work describes a multiagent framework for intelligent building control that is deployed in a commercial building equipped with sensors and effectors and implements a novel unsupervised online real-time learning algorithm that constructs a fuzzy rule-base, derived from very sparse data in a nonstationary environment.

Abstract: Modern approaches to the architecture of living and working environments emphasize the dynamic reconfiguration of space and function to meet the needs, comfort, and preferences of its inhabitants. Although it is possible for a human operator to specify a configuration explicitly, the size, sophistication, and dynamic requirements of modern buildings demands that they have autonomous intelligence that could satisfy the needs of its inhabitants without human intervention. We describe a multiagent framework for such intelligent building control that is deployed in a commercial building equipped with sensors and effectors. Multiple agents control subparts of the environment using fuzzy rules that link sensors and effectors. The agents communicate with one another by asynchronous, interest-based messaging. They implement a novel unsupervised online real-time learning algorithm that constructs a fuzzy rule-base, derived from very sparse data in a nonstationary environment. We have developed methods for evaluating the performance of systems of this kind. Our results demonstrate that the framework and the learning algorithm significantly improve the performance of the building.