2015/09/18 by Michael Zehnder, Holger Wache, Zehnder, Michael +7 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Multiagent Systems (cs.MA) #Recommender Systems and Techniques #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1509.05722
openalex publication_date 2015/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.