Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees

International Journal of Electrical Power and Energy Systems

Volumn 34, Issue 1, 2012, Pages 90-98

  Monedero, Iñigo ^a   Biscarri, Félix ^a   León, Carlos ^a   Guerrero, Juan I ^a   Biscarri, Jesús ^b   Millán, Rocío ^b  

^a UNIVERSITY OF SEVILLE   (Spain)

^b Endesa SA   (Spain)

Author keywords

Bayesian network; Data mining; Decision tree; Non technical loss; Pearson correlation coefficient

Indexed keywords

CONSUMED ENERGY; ELECTRONIC TECHNOLOGIES; HIGH IMPACT; LATIN AMERICA; NON-TECHNICAL LOSS; PEARSON COEFFICIENT; PEARSON CORRELATION COEFFICIENTS; POWER UTILITY; SEVILLE;

CORRELATION METHODS; CUSTOMER SATISFACTION; DATA MINING; DECISION TREES; DROPS; FORESTRY; INDUSTRY; PROFITABILITY; SALES;

BAYESIAN NETWORKS;

CORRELATION; DROPS; FORESTRY; PROFITABILITY; SALES;

EID: 81055156599     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2011.09.009     Document Type: Article

References (37)

1. R. Wheeler, and S. Aitken Multiple algorithms for fraud detection Knowl Based Syst 13 2000 93 99
2. Kou Y, Lu C-T, Sinvongwattana S, Huang Y-P. Survey of fraud detection techniques. In: IEEE int conf on netw sens and control. Taiwan; 2004. p. 89-95.
3. T. Fawcett, and F. Provost Adaptative fraud detection Data Min Knowl Discov 1 1997 291 316
4. M. Art́is, M. Ayuso, and M. Guillén Modeling different types of automobile insurance frauds behavior in the spanish market Insur Math Econ 24 1999 67 81
5. S. Daskalaki, I. Kopanas, M. Goudara, and N. Avouris Data mining for decision support on customer insolvency in the telecommunication business J Oper Res 145 2003 239 255 (Pubitemid 35411975)
6. Brause R, Langsdorf T, Hepp M. Neural data mining for credit card fraud detection. In: Proc. 11th IEEE int conf on tools artif intell; 1999. p. 53-61.
7. E. Kirkos, C. Spathis, and Y. Manolopoulos Data mining techniques for the detection of fraudulent financial statements Expert Syst Appl 32 2007 995 1003 (Pubitemid 44821791)
8. Burge P, Shawe-Taylor J. Detecting cellular fraud using adaptative prototypes. In: Proc on AI approaches to fraud detect and risk manage, vol. 42, 1997. p. 9-13.
9. Cabral J, Pinto J, Linares K, Pinto A. Methodology for fraud detection using rough sets. In: IEEE int conf on granul comput; 2006. p. 246-49.
10. N. Jawad, S.Y. Keem, K.T. Sieh, A.K. Syed, and M. Malik Nontechnical loss detection for metered customers in power utility using support vector machines IEEE Trans Power Deliv 25 2 2010 1162 1171
11. Yap KS, Hussien Z, Mohamad A. Abnormalities and fraud electric meter detection using hybrid support vector machine and genetic algorithm. In: Proc third IASTED int conf adv comput science and tech Phuket. Thailand: Iasted press; 2007.
12. Cabral J, Pinto J, Gontijo EM, Reis J. Fraud detection in electrical energy consumers using rough sets. In: IEEE int conf on systems, man and cybern; 2004. p. 3625-9. (Pubitemid 40408397)
13. Cabral J, Pinto J, Martins E, Pinto A. Fraud detection in high voltage electricity consumers using data mining. In: IEEE transm and distrib conf and exposit. T&D. IEEE/PES; 2008. p. 1-5.
14. M. Sforna Data mining in power company customer database Electr Power Systs Res 55 2000 201 209
15. Jiang R, Tagiris H, Lachsz A, Jeffrey M. Wavelet based features extraction and multiple classifiers for electricity fraud detection. In: Trans and distrib conf and exhibit. 2002: Asia pacific. IEEE/PES; 2002. p. 2251-6.
16. M. Kantardzic Data mining: concepts, models methods and algorithms 1st ed. 1991 AAAI/MIT Press
17. I. Witthen, and E. Frank Data mining-practical machine learning tools and techniques with java implementations 2000 Morgan Kaufmann, Acad Press New York, San Mateo, CA
18. Editorial. Recent advances in data mining. Eng App Artif Intell; 2006. p. 19.
19. J. McCarthy Phenomenal data mining Commun ACM 43 8 2000 75 79
20. Ramos S, Vale Z. Data mining techniques application in power distribution utilities. In: IEEE transm and distrib conf and exposit. IEEE/PES; 2008. p. 1-8.
21. S. Valero, M. Ortiz, C. Senabre, A. Gabald́on, and F. Garcia Classification filtering and identification of electrical customer load pattern through the use of self-organizing maps IEEE Trans Power Syst 21 4 2006 1672 1682 (Pubitemid 46357767)
22. M.L. Othman, I. Aris, M.R. Othman, and H. Osman Rough-set-and-genetic- algorithm based data mining and rule quality measure to hypothesize distance protective relay operation characteristics from relay event report Int J Electr Power Energy Syst 33 2011 1437 1456
23. Biscarri F, Monedero I, León C, Guerrero JI, Biscarri J, Millán R. A data mining method based on the variability of the customers consumption. In: 10th int conf on enterp inf syst, ICEIS2008, Barcelona, Spain, June 12-16.
24. Biscarri F, Monedero I, León C, Guerrero JI, Biscarri J, Millán R. A mining framework to detect non-technical losses in power utilities. In: 11th Int conf on enterp inf syst, ICEIS2009, Milano, Italy, May 6-10.
25. Monedero I, Biscarri F, León C, Guerrero JI, Biscarri J. Using regression analysis to identify patterns of non-technical losses on power utilities. KES 2010, LNAI 6276; 2010. p. 410-19.
26. K. Pearson Mathematical contributions to the theory of evolution - III. Regression, heredity and panmixia Philos Trans R Soc London Ser A 187 1896 253 318
27. Moore D. Basic practice of statistics. In: Freeman WH, editor. San Francisco, CA, USA; 2006.
28. Lee KC, Jo NY. Bayesian network approach to predict mobile churn motivations: emphasis on general Bayesian network. Markov blanket, and what-if simulation, LNCS, 6284; 2010. p. 304-13.
29. Przytul KW, Dash D, Thompson D. Evaluation of Bayesian networks used for diagnostics. In: Proc IEEE Aerospace Conf, vol. 60, 2003. p 1-12.
30. Thornton J, Gustafsson T, Blumenstein M, Hine T. Robust character recognition using a hierarchical Bayesian network, LNCS, 4304; 2006. p. 1259-66.
31. S.K. Aggarwal, L.M. Saini, and A. Kumar Electricity price forecasting in deregulated markets: a review and evaluation Int J Electr Power Energy Syst 31 2009 13 22
32. L.A.M. Riascos, M.G. Simoes, and P.E. Miyagi A Bayesian network fault diagnosis system for proton membrane exchange fuel cells J. Power Sources 165 2007 267 278 (Pubitemid 46216719)
33. Buschkes R, Kesdogan D, Reichl P. How to increase security in mobile networks by anomaly detection. In: Proc 14th comput secur appl conf (ACSAC '98); 1998. p. 8.
34. Maes S, Tuyls K, Vanschoenwinkel B, Manderick B. Credit card fraud detection using Bayesian and Neural networks. In: Proc. neuro fuzzy, Havana, Cuba; 2002.
35. S.R. Samantaray, and P.K. Dash Decision tree based discrimination between inrush currents and internal faults in power transformer Int J Electr Power Energy Syst 33 2011 1043 1048
36. A. Ugedo, and E. Lobato Application of neural networks to the management of voltage constraints in the Spanish market Int J Electr Power Energy Syst 33 2011 1261 1271
37. Bonchi F, Giannotti F, Mainetto G, Pedreschi D. A classification-based methodology for planning audit strategies in fraud detection. In: conf on knowl discov data: proc. fifth Acm SigKdd; 15-18 August, 1999.