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dc.contributor.authorCui, Wenqiang
dc.contributor.authorWang, Hao
dc.date.accessioned2018-02-13T14:21:02Z
dc.date.available2018-02-13T14:21:02Z
dc.date.created2017-10-27T20:02:04Z
dc.date.issued2017
dc.identifier.isbn978-1-5090-3619-6
dc.identifier.urihttp://hdl.handle.net/11250/2484426
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 energy data. We have investigated five models to detect anomalies in the school electricity consumption data. Furthermore, we propose a hybrid model which combines polynomial regression and Gaussian distribution. Based on this model, we have developed a data detection and visualization system for a facilities management company to detect anomalous events in school electricity facilities. The system is tested and evaluated by the facilities managers of the company. According to the result of the evaluation, it reduces the effort required by facilities managers to identify anomalous events in school electricity facilities.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofProceedings of 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)
dc.titleAnomaly Detection and Visualization of School Electricity Consumption Datanb_NO
dc.typeChapternb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.pagenumber606-611nb_NO
dc.identifier.doi10.1109/ICBDA.2017.8078707
dc.identifier.cristin1508477
dc.description.localcode© 2017 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.nb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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