Learning occupancy in single person offices with mixtures of multi-lag Markov chains

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dc.contributor.authorManna, Carlo
dc.contributor.authorFay, Damien
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorWilson, Nic
dc.contributor.funderIrish Research Council for Science, Engineering and Technologyen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderIntel Labs Europeen
dc.contributor.funderIntel Corporationen
dc.date.accessioned2014-02-18T15:52:35Z
dc.date.available2014-02-18T15:52:35Z
dc.date.issued2013-11
dc.date.updated2014-01-16T12:44:52Z
dc.description.abstractThe problem of real-time occupancy forecastingfor single person offices is critical for energy efficient buildings which use predictive control techniques. Due to the highly uncertain nature of occupancy dynamics, the modeling and prediction of occupancy is a challenging problem. This paper proposes an algorithm for learning and predicting single occupant presence in office buildings, by considering the occupant behaviour as an ensemble of multiple Markov models at different time lags. This model has been tested using real occupancy data collected from PIR sensors installed in three different buildings and compared with state of the art methods, reducing the error rate by on average 5% over the best comparator method.en
dc.description.sponsorshipIrish Research Council for Science Engineering and Technology (Enterprise Partnership Scheme); Science Foundation Ireland (SFI Research Cluster ITOBO 07.SRC.I1170); Science Foundation Ireland (08/PI/I1912); Intel Labs Europe (Enterprise Partnership Scheme)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMANNA, C., FAY, D., BROWN, K. N. & WILSON, N. 2013. Learning occupancy in single person offices with mixtures of multi-lag Markov chains. In: Proceedings 25th International Conference on Tools with Artificial Intelligence ICTAI 2013. Washington DC, USA, 4-6 Nov. Los Alamitos, California: IEEE Computer Society, pp. 151-158.en
dc.identifier.doi10.1109/ICTAI.2013.32
dc.identifier.endpage158en
dc.identifier.isbn978-1-4799-2971-9
dc.identifier.issn1082-3409
dc.identifier.startpage151en
dc.identifier.urihttps://hdl.handle.net/10468/1393
dc.language.isoenen
dc.publisherIEEE Computer Societyen
dc.relation.ispartofICTAI 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, Washington DD, USA, 4-6 Nov 2013
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Strategic Research Cluster/07/SRC/I1170/IE/SRC ITOBO: Information and Communication Technology for Sustainable and Optimised Building Operation/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/08/IN.1/I1912/IE/The Development of Artificial intelligence Approaches for Preferences in Combinational Problems/en
dc.rights© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectMarkov chainsen
dc.subjectOccupancy predictionen
dc.subjectBuilding controlen
dc.titleLearning occupancy in single person offices with mixtures of multi-lag Markov chainsen
dc.typeConference itemen
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