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Title: Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
Authors: Nagi, J. 
Yap, K.S. 
Tiong, S.K. 
Ahmed, S.K. 
Nagi, F. 
Issue Date: 2011
Abstract: This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy if-then rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective. © 2011 IEEE.
Appears in Collections:COE Scholarly Publication

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