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

• Main Authors: Nagi J., Yap K.S., Tiong S.K., Ahmed S.K., Nagi F.
• Other Authors: 25825455100
• Format: Article
• Published: 2023
• Subjects: 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; Post-processing scheme; Power distributions; Power utility; Fuzzy inference

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.