A New Anomaly Detection System for School Electricity Consumption Data
| Autor: | Hao Wang, Wenqiang Cui |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Visual analytics
Computer science 020209 energy Image processing 02 engineering and technology visual analytics computer.software_genre remote facilities management Data visualization 0202 electrical engineering electronic engineering information engineering data visualization anomaly detection school electricity consumption data time series data mining Polynomial regression lcsh:T58.5-58.64 business.industry lcsh:Information technology Credit card fraud 020207 software engineering Visualization Anomaly detection Electricity Data mining business computer Information Systems |
| Zdroj: | Information, Vol 8, Iss 4, p 151 (2017) Information Information; Volume 8; Issue 4; Pages: 151 |
| ISSN: | 2078-2489 |
| Popis: | 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. 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|