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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 | Journal: | IEEE Transactions on Power Delivery Volume 26, Issue 2, April 2011, Article number 5738432, Pages 1284-1285 | 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. | DOI: | 10.1109/TPWRD.2010.2055670 |
Appears in Collections: | COE Scholarly Publication |
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