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Title: Detection of abnormalities and electricity theft using genetic support vector machines
Authors: Nagi, J. 
Yap, K.S. 
Tiong, S.K. 
Ahmed, S.K. 
Mohammad, A.M. 
Issue Date: 2008
Journal: 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 
Abstract: 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.
DOI: 10.1109/TENCON.2008.4766403
Appears in Collections:COE Scholarly Publication

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