Advertisement

Comparing Different Labeling Strategies in Anomalous Power Consumptions Detection

  • Fernanda Rodríguez
  • Federico Lecumberry
  • Alicia Fernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9443)

Abstract

Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the \(F_{measure}\) with inspection labels, avoiding hours of experts labeling.

Keywords

Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE 

Notes

Acknowledgements

This work was supported by the program Sector Productivo CSIC UTE. Authors would like to thank UTE, especially Juan Pablo Kosut and Fernando Santomauro, for providing datasets and share fraud detection expertise.

References

  1. 1.
    Leon, C., Biscarri, F.X.E.L., Monedero, I.X.F.I., Guerrero, J.I., Biscarri, J.X.F.S., Millan, R. X.E.O.: Variability and trend-based generalized rule induction model to NTL detection in power companies (2011)Google Scholar
  2. 2.
    dos Angelos, E., Saavedra, O., Corts, O., De Souza, A.: Detection and identification of abnormalities in customer consumptions in power distribution systems (2011)Google Scholar
  3. 3.
    Markoc, Z., Hlupic, N., Basch, D.: Detection of suspicious patterns of energy consumption using neural network trained by generated samples (2011)Google Scholar
  4. 4.
    Sforna, M.: Data mining in power company customer database. Electr. Power Syst. Res. 55(3), 201–209 (2000)CrossRefGoogle Scholar
  5. 5.
    Monedero, I., Biscarri, F., León, C., Guerrero, J.I., Biscarri, J., Millán, R.: 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.) KES 2010, Part I. LNCS, vol. 6276, pp. 410–419. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  6. 6.
    Filho, J.R., Gontijo, E.M., Delaiba, A.C., Mazina, E., Cabral, J.E., Pinto, J.O.P.: Fraud identification in electricity company customers using decision tree (2004)Google Scholar
  7. 7.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft (2011)Google Scholar
  8. 8.
    Yap, K.S., Hussien, Z., Mohamad, A.: Abnormalities and fraud electric meter detection using hybrid support vector machine and genetic algorithm (2007)Google Scholar
  9. 9.
    Yap, K.S., Tiong, S.K., Nagi, J., Koh, J.S.P., Nagi, F.: Comparison of supervised learning techniques for non-technical loss detection in power utility (2012)Google Scholar
  10. 10.
    Biscarri, F., Monedero, I., Leon, C., Guerrero, J.I., Biscarri, J., Millan, R.: A data mining method based on the variability of the customer consumption - a special application on electric utility companies. In: Volume AIDSS, pp. 370–374. Inst. for Syst. and Technol. of Inf. Control and Commun. (2008)Google Scholar
  11. 11.
    Di Martino, J., Decia, F., Molinelli, J., Fernández, A.: Improving electric fraud detection using class imbalance strategies. In: 1st International Conference in Pattern Recognition Aplications and Methods, vol. 2, pp. 135–141 (2012)Google Scholar
  12. 12.
    Galvn, J., Elices, E., Noz, A.M., Czernichow, T., Sanz-Bobi, M.: System for detection of abnormalities and fraud in customer consumption (1998)Google Scholar
  13. 13.
    Jiang, R., Tagaris, H., Laschusz, A.: Wavelets based feature extraction and multiple classifiers for electricity fraud detection (2002)Google Scholar
  14. 14.
    Romero, J.: Improving the efficiency of power distribution system through technical and non-technical losses reduction (2012)Google Scholar
  15. 15.
    Lo, Y.L., Huang, S.C., Lu, C.N.: Non-technical loss detection using smart distribution network measurement data. In: 2012 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), pp. 1–5 (2012)Google Scholar
  16. 16.
    Di Martino, M., Decia, F., Molinelli, J., Fernández, A.: A novel framework for nontechnical losses detection in electricity companies. In: Latorre Carmona, P., Sánchez, J.S., Fred, A.L.N. (eds.) Pattern Recognition - Applications and Methods. AISC, vol. 204, pp. 109–120. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  17. 17.
    Rodriguez, F., Lecumberry, F., Fernndez, A.: Non technical loses detection: experts labels vs. inspection labels in the learning stage (2014)Google Scholar
  18. 18.
    Alcetegaray, D., Kosut, J.: One class SVM para la detección de fraudes en el uso deenergía eléctrica. Trabajo Final Curso de Reconocimiento de Patrones, Dictado por el IIE- Facultad de Ingeniería- UdelaR (2008)Google Scholar
  19. 19.
    Muniz, C., Vellasco, M., Tanscheit, R., Figueiredo, K.: Ifsa-eusflat 2009 a neuro-fuzzy system for fraud detection in electricity distribution (2009)Google Scholar
  20. 20.
    Nagi, J., Mohamad, M.: Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Trans. Power Deliv. 25(2), 1162–1171 (2010)CrossRefGoogle Scholar
  21. 21.
    Ramos, C., de Sousa, A.N., Papa, J., Falcao, A.: A new approach for nontechnical losses detection based on optimum-path forest. IEEE Trans. Power Syst. (2010)Google Scholar
  22. 22.
    Garcia, V., Sanchez, J., Mollineda, R.: On the suitability if numerical performance evaluation measures for class imbalance problems. In: 1st International Conference in Pattern Recognition Aplications and Methods, vol. 2, pp. 310–313 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fernanda Rodríguez
    • 1
  • Federico Lecumberry
    • 1
  • Alicia Fernández
    • 1
  1. 1.Facultad de Ingeniería, Instituto de Ingeniería EléctricaUniversidad de la RepúblicaMontevideoUruguay

Personalised recommendations