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.
Highlights
In recent years, with the ever-growing shortage of natural resources, energy has been a major political, social and economic topic
Precision and false negative rate (FNR) are three common criteria used for evaluating anomaly detection methods, which are given as: Recall =
We have examined five models to detect anomalies in electricity consumption data
Summary
With the ever-growing shortage of natural resources, energy has been a major political, social and economic topic. Detection in building electricity consumption data is one of the most important methods to identify anomalous events in buildings. We present an innovative method and build a system to detect and visualize anomalies in school electricity consumption data for a facilities management company. Every week facilities managers look over spreadsheet graphs of the data to identify anomalous events in school facilities, unusually high electricity consumption. Parts of the results of the model investigation and the system development contributed to anomaly detection of school electricity consumption data have appeared in [8].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
