Energy saving in smart homes based on consumer behaviour: A case study
This addresses energy efficiency for smart home residents, but it is incremental as it applies existing methods to a specific case.
The paper tackles energy saving in smart homes by developing a recommender system that uses consumer behavior data to suggest actions for reducing energy consumption without lowering comfort, achieving unspecified energy savings through a case study.
This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system mines for frequent and periodic patterns in the event data provided by the Digitalstrom home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects an opportunities to save energy without decreasing the comfort level it sends a recommendation to the residents.