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 language | English |
|---|---|
| Article number | 5738432 |
| Pages (from-to) | 1284-1285 |
| Number of pages | 2 |
| Journal | IEEE Transactions on Power Delivery |
| Volume | 26 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 01 Apr 2011 |
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All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Cite this
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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 journal › Article
TY - JOUR
T1 - Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
AU - Nagi, Jawad
AU - Yap, Keem Siah
AU - Tiong, Sieh Kiong
AU - Ahmed, Syed Khaleel
AU - Nagi, Farrukh
PY - 2011/4/1
Y1 - 2011/4/1
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=79953193105&partnerID=8YFLogxK
U2 - 10.1109/TPWRD.2010.2055670
DO - 10.1109/TPWRD.2010.2055670
M3 - Article
AN - SCOPUS:79953193105
VL - 26
SP - 1284
EP - 1285
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
SN - 0885-8977
IS - 2
M1 - 5738432
ER -