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Information 2017, 8(4), 151; doi:10.3390/info8040151

A New Anomaly Detection System for School Electricity Consumption Data

Department of ICT and Natural Sciences, Norwegian University of Science & Technology, Larsgårdsvegen 2, 6009 Ålesund, Norway
This paper is an extended version of our paper published in the 2017 IEEE International Conference on Big Data Analysis (ICBDA), Beijing, China, 10–12 March 2017.
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Received: 29 September 2017 / Revised: 8 November 2017 / Accepted: 16 November 2017 / Published: 20 November 2017
(This article belongs to the Special Issue Supporting Technologies and Enablers for Big Data)
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Abstract

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. View Full-Text
Keywords: anomaly detection; data visualization; school electricity consumption data; time series; remote facilities management; visual analytics; data mining anomaly detection; data visualization; school electricity consumption data; time series; remote facilities management; visual analytics; data mining
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This 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).

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Cui, W.; Wang, H. A New Anomaly Detection System for School Electricity Consumption Data. Information 2017, 8, 151.

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