{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:38:25Z","timestamp":1777480705388,"version":"3.51.4"},"reference-count":19,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,11,20]],"date-time":"2017-11-20T00:00:00Z","timestamp":1511136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Anomaly 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.<\/jats:p>","DOI":"10.3390\/info8040151","type":"journal-article","created":{"date-parts":[[2017,11,20]],"date-time":"2017-11-20T11:35:45Z","timestamp":1511177745000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A New Anomaly Detection System for School Electricity Consumption Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Wenqiang","family":"Cui","sequence":"first","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science &amp; Technology, Larsg\u00e5rdsvegen 2, 6009 \u00c5lesund, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-5989","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science &amp; Technology, Larsg\u00e5rdsvegen 2, 6009 \u00c5lesund, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.enbuild.2007.03.007","article-title":"A review on buildings energy consumption information","volume":"40","author":"Ortiz","year":"2008","journal-title":"Energy Build."},{"key":"ref_2","first-page":"111","article-title":"4641 Building Energy Use and Control Problems: An Assessment of Case Studies","volume":"109","author":"Ardehali","year":"2003","journal-title":"ASHRAE Trans."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.enbuild.2011.12.029","article-title":"Calibration of building energy models for retrofit analysis under uncertainty","volume":"47","author":"Heo","year":"2012","journal-title":"Energy Build."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. 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