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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 742461, 10 pages
http://dx.doi.org/10.1155/2012/742461
Research Article

Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

1School of DCIT, University of Newcastle, Callaghan, NSW 2308, Australia
2ICT Centre, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, Australia
3Energy Technology Division, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, Australia

Received 30 April 2012; Revised 8 August 2012; Accepted 18 October 2012

Academic Editor: F. Morabito

Copyright © 2012 Lei Jiang et al. 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.

Abstract

Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.