Occupant location prediction in smart buildings using association rule mining

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dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNoen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorBrown, Kenneth N.en
dc.contributor.authorRyan, Conor
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2016-05-17T08:48:17Z
dc.date.available2016-05-17T08:48:17Z
dc.date.issued2016
dc.date.submitted2016
dc.description.abstractHeating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.en
dc.description.sponsorshipScience Foundation Ireland (SFI Grant ITOBO 07.SRC.I1170; SFI Grant Insight 12/RC/2289)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRyan, C. 2016. Occupant location prediction in smart buildings using association rule mining. PhD Thesis, University College Cork.en
dc.identifier.endpage151en
dc.identifier.urihttps://hdl.handle.net/10468/2583
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2016, Conor Ryan.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectAssociation rulesen
dc.subjectData miningen
dc.subjectEnergy efficiencyen
dc.subjectOccupant predictionen
dc.thesis.opt-outfalse
dc.titleOccupant location prediction in smart buildings using association rule miningen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisork.brown@cs.ucc.ie
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