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Detection of abnormalities and electricity theft using genetic support vector machines

Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards Non-Technical Loss (NTL) analysis for electric utilities using Genetic Algorithm (GA) and Support Vector Machine (SVM). The main motivation of this study...

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Main Authors: Nagi, J., Yap, K.S., Tiong, S.K., Ahmed, S.K., Mohammad, A.M.
Format: Conference Paper
Language:English
Published: 2017
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spelling my.uniten.dspace-50412017-11-14T08:12:02Z Detection of abnormalities and electricity theft using genetic support vector machines Nagi, J. Yap, K.S. Tiong, S.K. Ahmed, S.K. Mohammad, A.M. Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards Non-Technical Loss (NTL) analysis for electric utilities using Genetic Algorithm (GA) and Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. 2017-11-14T03:21:35Z 2017-11-14T03:21:35Z 2008 Conference Paper 10.1109/TENCON.2008.4766403 en IEEE Region 10 Annual International Conference, Proceedings/TENCON 2008, Article number 4766403 2008 IEEE Region 10 Conference, TENCON 2008; Hyderabad; India; 19 November 2008 through 21 November 2008; Category number08CH38026; Code 75688
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards Non-Technical Loss (NTL) analysis for electric utilities using Genetic Algorithm (GA) and Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.
format Conference Paper
author Nagi, J.
Yap, K.S.
Tiong, S.K.
Ahmed, S.K.
Mohammad, A.M.
spellingShingle Nagi, J.
Yap, K.S.
Tiong, S.K.
Ahmed, S.K.
Mohammad, A.M.
Detection of abnormalities and electricity theft using genetic support vector machines
author_facet Nagi, J.
Yap, K.S.
Tiong, S.K.
Ahmed, S.K.
Mohammad, A.M.
author_sort Nagi, J.
title Detection of abnormalities and electricity theft using genetic support vector machines
title_short Detection of abnormalities and electricity theft using genetic support vector machines
title_full Detection of abnormalities and electricity theft using genetic support vector machines
title_fullStr Detection of abnormalities and electricity theft using genetic support vector machines
title_full_unstemmed Detection of abnormalities and electricity theft using genetic support vector machines
title_sort detection of abnormalities and electricity theft using genetic support vector machines
publishDate 2017
_version_ 1644493598147739648
score 13.222552

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