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

Research output: Contribution to journalArticle

60 Citations (Scopus)

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.

Original languageEnglish
Article number5738432
Pages (from-to)1284-1285
Number of pages2
JournalIEEE Transactions on Power Delivery
Volume26
Issue number2
DOIs
Publication statusPublished - 01 Apr 2011

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Fuzzy inference
Support vector machines
Artificial intelligence
Electricity
Costs

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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title = "Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system",
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.",
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Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system. / Nagi, Jawad; Yap, Keem Siah; Tiong, Sieh Kiong; Ahmed, Syed Khaleel; Nagi, Farrukh.

In: IEEE Transactions on Power Delivery, Vol. 26, No. 2, 5738432, 01.04.2011, p. 1284-1285.

Research output: Contribution to journalArticle

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