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dc.contributor.authorCui, Wenqiang
dc.contributor.authorWang, Hao
dc.date.accessioned2017-11-23T07:25:10Z
dc.date.available2017-11-23T07:25:10Z
dc.date.created2017-11-22T10:14:07Z
dc.date.issued2017
dc.identifier.citationInformation. 2017, 8 (4), .nb_NO
dc.identifier.issn2078-2489
dc.identifier.urihttp://hdl.handle.net/11250/2467664
dc.description.abstractAnomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data.nb_NO
dc.language.isoengnb_NO
dc.publisherMDPInb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA New Anomaly Detection System for School Electricity Consumption Datanb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber18nb_NO
dc.source.volume8nb_NO
dc.source.journalInformationnb_NO
dc.source.issue4nb_NO
dc.identifier.doi10.3390/info8040151
dc.identifier.cristin1517078
dc.description.localcodeThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).nb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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