Intelligent Building Using Hybrid Inference with Building Automation System to Improve Energy Efficiency

Most existing building automation systems are operated with rule-based settings. These systems are wasting a lot of energy because the systems can not properly cope with changes in indoor/outdoor environments. In this paper, we propose hybrid inference for inferring indoor environments in the building using real-time stream data coming from BAS. Hybrid inference consists of Runtime Stream Processing and Semantic Lift Processing. Runtime Stream Processing deduces occupancy and thermal comfort using machine learning technology with historical data. Semantic Lift Processing uses the semantic inference to extract new knowledge based on inferred results from Runtime Stream Processing. On the basis of stored semantic-based data in the ontology, Semantic Lift Processing derives energy waste space based on occupancy and thermal comfort by using semantic technology.