A Data Mining Based NTL Analysis Method

This paper presents a method of determining which type of data provides maximum accuracy with reference to non-technical loss analysis in the electricity distribution sector. The method is based on two popular classification algorithms, Naive Bayesian and Decision Tree. It involves extracting the patterns of customers' kWh consumption behaviour from historical data and arranging the data in various ways by averaging them yearly, monthly, weekly, and daily. Both techniques are used and compared. The intention is to ensure the acquisition of optimum results in developing representative load profiles to be used as the reference for non-technical loss analysis directed at detecting any significant activities that may contribute to such losses.

[1]  Thomas B. Smith,et al.  Electricity theft: a comparative analysis , 2004 .

[2]  João O. P. Pinto,et al.  Fraud identification in electricity company customers using decision tree , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[3]  F. Gubina,et al.  Determining the load profiles of consumers based on fuzzy logic and probability neural networks , 2004 .

[4]  Zhao Yang Dong,et al.  An improved Naive Bayesian classifier with advanced discretisation method , 2007, Int. J. Intell. Syst. Technol. Appl..

[5]  Rung-Fang Chang,et al.  Load profiling and its applications in power market , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[6]  B.D. Pitt,et al.  Application of data mining techniques to load profiling , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[7]  S. Benchluch,et al.  A novel approach to computing distribution losses , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[8]  F. Gubina,et al.  An approach to customers daily load profile determination , 2002, IEEE Power Engineering Society Summer Meeting,.

[9]  Innocent E. Davidson.  Evaluation and effective management of nontechnical losses in electrical power networks , 2002, IEEE AFRICON. 6th Africon Conference in Africa,.

[10]  C. S. Chen,et al.  Temperature Effect to Distribution System Load Profiles and Feeder Losses , 2001, IEEE Power Engineering Review.

[11]  F. Gubina,et al.  Allocation of the load profiles to consumers using probabilistic neural networks , 2005, IEEE Transactions on Power Systems.

[12]  P. Postolache,et al.  Customer Characterization Options for Improving the Tariff Offer , 2002, IEEE Power Engineering Review.

[13]  Z. Vale,et al.  An electric energy consumer characterization framework based on data mining techniques , 2005, IEEE Transactions on Power Systems.

[14]  A.H. Nizar,et al.  Load Profiling Method in Detecting non-Technical Loss Activities in a Power Utility , 2006, 2006 IEEE International Power and Energy Conference.

[15]  M. Poveda.  A new method to calculate power distribution losses in an environment of high unregistered loads , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).