Occupant location prediction in smart buildings using association rule mining

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dc.contributor.advisor Brown, Kenneth N. en
dc.contributor.author Ryan, Conor
dc.date.accessioned 2016-05-17T08:48:17Z
dc.date.available 2016-05-17T08:48:17Z
dc.date.issued 2016
dc.date.submitted 2016
dc.identifier.citation Ryan, C. 2016. Occupant location prediction in smart buildings using association rule mining. PhD Thesis, University College Cork. en
dc.identifier.endpage 151 en
dc.identifier.uri http://hdl.handle.net/10468/2583
dc.description.abstract Heating, 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.sponsorship Science Foundation Ireland (SFI Grant ITOBO 07.SRC.I1170; SFI Grant Insight 12/RC/2289) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2016, Conor Ryan. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Association rules en
dc.subject Data mining en
dc.subject Energy efficiency en
dc.subject Occupant prediction en
dc.title Occupant location prediction in smart buildings using association rule mining en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD (Science) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Computer Science en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor k.brown@cs.ucc.ie
dc.internal.conferring Summer 2016 en


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© 2016, Conor Ryan. Except where otherwise noted, this item's license is described as © 2016, Conor Ryan.
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