2015, Schweizer, Daniel, Zehnder, Michael, Wache, Holger, Zanatta, Danilo, Rodriguez, Miguel, Witschel, Hans Friedrich

This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions.