Skip to main content

Using Regression Analysis to Identify Patterns of Non-Technical Losses on Power Utilities

  • Conference paper
Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010)

Abstract

A non-technical loss (NTL) is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. This paper describes new advances that we have developed for Midas project. This project is being developed in the Electronic Technology Department of the University of Seville and its aim is to detect non-technical losses in the database of the Endesa Company. The main symptom of a NTL in a customer is an important drop in his billed energy. Thus, a main task for us is to detect customers with anomalous drops in their consumed energy. Concretely, in the paper we present two new algorithms based on a regression analysis in order to detect two types of patterns of decreasing consumption typical in customers with NTLs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wheeler, R., Aitken, S.: Multiple algorithms for fraud detection. Knowledge based systems (13), 93–99 (2000)

    Google Scholar 

  2. Kou, Y., Lu, C.-T., Sinvongwattana, S., Huang, Y.-P.: Survey of fraud detection techniques. In: Proceeding of the 2004 IEEE International Conference on Networking, Sensing and Control, Taiwan, March 21, pp. 89–95. IEEE press, Los Alamitos (2004)

    Google Scholar 

  3. Fawcett, T., Provost, F.: Adaptative fraud detection. Data mining and Knowledge Discovery 1, 291–316 (1997)

    Article  Google Scholar 

  4. Art´ıs, M., Ayuso, M., Guillén, M.: Modeling different types of automobile insurance frauds behavior in the spanish market. In: Insurance Mathematics and Economics, vol. 24, pp. 67–81. Elsevier Press, Amsterdam (1999)

    Google Scholar 

  5. Daskalaki, S., Kopanas, I., Goudara, M., Avouris, N.: Data mining for decision support on customer insolvency in the telecommunication business. European Journal of Operational Research 145, 239–255 (2003)

    Article  MATH  Google Scholar 

  6. Brause, R., Langsdorf, T., Hepp, M.: Neural data mining for credit card fraud detection. In: Proceeding 11th IEEE International Conference on Tools with Artificial Intelligence. IEEE press, Los Alamitos (1999)

    Google Scholar 

  7. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32, 995–1003 (2007)

    Article  Google Scholar 

  8. Burge, P., Shawe-Taylor, J.: Detecting cellular fraud using adaptative prototypes. In: Proceeding on AI Approaches to Fraud Detection and Risk Management, pp. 9–13. AAAI Press, Menlo Park (1997)

    Google Scholar 

  9. Cabral, J., Pinto, J., Linares, K., Pinto, A.: Methodology for fraud detection using rough sets. In: 2006 IEEE International Conference on Granular Computing. IEEE press, Los Alamitos (2006)

    Google Scholar 

  10. Denning, D.: An intrusion-detection model. IEEE transactions on Software Engineering 13, 222–232 (1987)

    Article  Google Scholar 

  11. Yap, K.S., Hussien, Z., Mohamad, A.: Abnormalities and fraud electric meter detection using hybrid support vector machine and genetic algorithm. In: Proceeding of the Third IASTED International Conference Advances in Computer Science and Technology, Phuket, Thailand, April 2-4, Iasted Press (2007)

    Google Scholar 

  12. Filho, J., als: Fraud identification in electricity company customers using decision tree. In: IEEE International Conference on Systems, Man and Cibernetics, IEEE/PES, The Hague (2004)

    Google Scholar 

  13. Cabral, J., Pinto, J., Gontijo, E.M., Reis, J.: Fraud detection in electrical energy consumers using rough sets. In: 2004 IEEE International Conference on Systems, Man and Cybernetics. IEEE press, Los Alamitos (2004)

    Google Scholar 

  14. Cabral, J., Pinto, J., Martins, E., Pinto, A.: Fraud detection in high voltage electricity consumers using data mining. In: IEEE Transmission and Distribution Conference and Exposition T&D, April 21-24, IEEE/PES (2008)

    Google Scholar 

  15. Sforna, M.: Data mining in power company customer database. In: Electric Power Systems Research, vol. 55, pp. 201–209. Elsevier Press, England (2000)

    Google Scholar 

  16. Jiang, R., Tagiris, H., Lachsz, A., Jeffrey, M.: Wavelet based features extraction and multiple classifiers for electricity fraud detection. In: Transmission and Distribution Conference and Exhibition 2002: Asia pacific, October 6-10. IEEE/PES (2002)

    Google Scholar 

  17. Kantardzic, M.: Data mining: concepts, models methods and algorithms, 1st edn. AAAI/MIT Press (1991)

    Google Scholar 

  18. Witthen, I., Frank, E.: Data Mining–Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann/Academic Press, New York/San Mateo (2000)

    Google Scholar 

  19. Editorial, Recent advances in data mining. Engineering applications of Artificial Intelligence 19, 361–362 (2006)

    Google Scholar 

  20. McCarthy, J.: Phenomenal data mining. Communications of the ACM 43(8), 75–79 (2000)

    Article  Google Scholar 

  21. Ramos, S., Vale, Z.: Data mining techniques application in power distribution utilities. In: IEEE Transmision and Distribution Conference and Exposition T&D, April 21-24, IEEE/PES (2008)

    Google Scholar 

  22. Nizar, A., Dong, Z., Zhao, J.: Load profiling and data mining techniques in electricity deregukated market. In: Power Engineering Society General Meeting, June 18-22. IEEE/PES (2006)

    Google Scholar 

  23. Biscarri, F., Monedero, I., León, C., Guerrero, J.I., Biscarri, J., Millán, R.: A data mining method based on the variability of the customers consumption. In: 10th Int. Conf. on Enterp. Inf. Systs., ICEIS 2008, Barcelona, Spain, June 12-16 (2008)

    Google Scholar 

  24. Biscarri, F., Monedero, I., León, C., Guerrero, J.I., Biscarri, J., Millán, R.: A mining Framework to detect non-technical losses in power utilities. In: 11th Int. Conf. on Enterp. Inf. Systs., ICEIS 2009, Milano, Italy, May 6-10 (2009)

    Google Scholar 

  25. Pearson, K.: Mathematical contributions to the theory of evolution.—III. Regression, heredity and panmixia. Philos. Trans. R. Soc. London, ser. A 187, 253–318 (1896)

    Article  Google Scholar 

  26. Moore, D.: Basic Practice of Statistics. W.H. Freeman, San Francisco (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Monedero, I., Biscarri, F., León, C., Guerrero, J.I., Biscarri, J., Millán, R. (2010). Using Regression Analysis to Identify Patterns of Non-Technical Losses on Power Utilities. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15387-7_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15386-0

  • Online ISBN: 978-3-642-15387-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics