Energy reduction platform based on occupant behavior pattern detection in enhanced living environments

The increasing of device interoperability creates a new way to design smart houses and to support enhanced living environments having as main aim the increasing of quality of life. In this context more supporting platforms for smart houses were developed, some of them using Cloud systems for remote supervision and control. An important aspect, which is an open issue for both industry and academia, is represented by how to reduce and estimate energy consumption for a smart house. In this paper we propose a modular platform that both increases device interoperability and uses machine learning models to detect occupant behavior patterns. This platform describes the data collection and aggregation procedures, monitoring and control algorithms, batch training of machine learning models, and offers internal and external (based on Cloud services) access point for the user. In this way we create a model and use it with the purpose of creating energy-awareness by advising the user on how he/she can improve daily habits while reducing costs at the same time.