Schweizer, DanielZehnder, MichaelWache, HolgerZanatta, DaniloRodriguez, MiguelWitschel, Hans Friedrich2017-03-222017-10-272015http://hdl.handle.net/11654/24620https://doi.org/10.26041/fhnw-1004This 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.ensmart citiessmar homesenergy savingrecommender systemsassociation rulesunsupervised learninginternet of things04B - Beitrag Konferenzschrift