Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
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 hi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | |
| Published: |
2018
|
| Online Access: | http://dspace.uniten.edu.my/jspui/handle/123456789/8916 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|