Anomaly Detection and Visualization of School Electricity Consumption Data
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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 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.