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dc.contributor.authorWitschel, Hans Friedrich
dc.contributor.authorZanatta, H.F.
dc.contributor.authorRodriguez, M.
dc.contributor.authorZehnder, M.
dc.contributor.editorn.n.
dc.date.accessioned2015-09-21T09:24:37Z
dc.date.available2017-10-27T11:02:36Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/11654/5066
dc.identifier.urihttp://dx.doi.org/10.26041/fhnw-2786
dc.description.abstractThis 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 mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks 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 opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.
dc.language.isoen
dc.publisherIEEE Computer Society Press, 2015
dc.relation.ispartofFirst IEEE International Smart Cities Conference (ISC2-2015)
dc.accessRightsAnonymous
dc.subjectsave energy, smart homes, reduction of energy consumption
dc.subject.ddc330 - Wirtschaftde
dc.titleEnergy saving in smart homes based on consumer behavior: A case study
dc.type04 - Beitrag Sammelband oder Konferenzschrift
dc.spatialLos Alamitos, USW
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandNo
fhnw.pagination1-6
fhnw.IsStudentsWorkno


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