Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System

@article{Nagi2011ImprovingSN,
  title={Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System},
  author={Jawad Nagi and Keem Siah Yap and S. K. Tiong and S. K. Zamir Ahmed and Farrukh Nagi},
  journal={IEEE Transactions on Power Delivery},
  year={2011},
  volume={26},
  pages={1284-1285}
}
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… CONTINUE READING
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