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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 686345, 7 pages
Research Article

A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring

1Department of Electrical and Electronics Engineering, Lebanese International University, Mazraa, Beirut 146404, Lebanon
2Pascal Institute, UMR 6602, 24 Avenue des Landais, 63177 Aubière Cedex, France

Received 30 March 2013; Accepted 12 April 2013

Academic Editor: Baoding Liu

Copyright © 2013 Khaled Chahine and Khalil El Khamlichi Drissi. 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.


Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change concerns of the present time. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on feature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues. These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated values of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution of the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a three-dimensional feature space with reduced intercluster overlap.