Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system

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Nagi J.
Yap K.S.
Tiong S.K.
Ahmed S.K.
Nagi F.
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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.
Computational intelligence system , fuzzy logic , nontechnical loss , pattern classification , Artificial intelligence , Computer crime , Crime , Engineering research , Fuzzy systems , Computational intelligence , Cost effective , Detection framework , Electricity theft , Fraud detection , Fuzzy if-then rules , Fuzzy inference systems , Human knowledge , Non-technical loss , pattern classification , Post-processing scheme , Power distributions , Power utility , Fuzzy inference