Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage

Fernanda Rodríguez, Federico Lecumberry, Alicia Fernández

2014

Abstract

Non-technical losses detection is a complex task, with high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detection the largest number of frauds with the least amount of experts’ hours involved in preprocessing and inspections. This article analyzes 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 Fmeasure with inspection labels, avoiding hours of experts labeling.

References

  1. Alcetegaray, D. and Kosut, J. (2008). One class svm para la detección de fraudes en el uso de energía eléctrica. Trabajo Final Curso de Reconocimiento de Patrones, Dictado por el IIE- Facultad de Ingeniería- UdelaR.
  2. Biscarri, F., Monedero, I., Leon, C., Guerrero, J. I., Biscarri, J., and Millan, R. (2008). A data mining method based on the variability of the customer consumption - A special application on electric utility companies, volume AIDSS, pages 370-374. Inst. for Syst. and Technol. of Inf. Control and Commun.
  3. Depuru, S. S. S. R., Wang, L., and Devabhaktuni, V. (2011). Support vector machine based data classification for detection of electricity theft.
  4. Di Martino, J., Decia, F., Molinelli, J., and Fernández, A. (2012). Improving electric fraud detection using class imbalance strategies. 1st International Conference In Pattern Recognition Aplications and Methods, pages 135-141, volumen 2.
  5. Di Martino, M., Decia, F., Molinelli, J., and Fernndez, A. (2013). A novel framework for nontechnical losses detection in electricity companies. In Latorre Carmona, P., Snchez, J. S., and Fred, A. L., editors, Pattern Recognition - Applications and Methods, volume 204 of Advances in Intelligent Systems and Computing, pages 109-120. Springer Berlin Heidelberg.
  6. dos Angelos, E., Saavedra, O., Corts, O., and De Souza, A. (2011). Detection and identification of abnormalities in customer consumptions in power distribution systems.
  7. Filho, J. R., Gontijo, E. M., Delaiba, A. C., Mazina, E., Cabral, J. E., and Pinto, J. O. P. (2004). Fraud identification in electricity company customers using decision tree.
  8. Galvn, J., Elices, E., Noz, A. M., Czernichow, T., and SanzBobi, M. (1998). System for detection of abnormalities and fraud in customer consumption.
  9. Garcia, V., Sanchez, J., and Mollineda, R. (2012). On the suitability if numerical performance evaluation measures for class imbalance problems. 1st International Conference In Pattern Recognition Aplications and Methods, pages 310-313, volumen 2.
  10. Jiang, R., Tagaris, H., and Laschusz, A. (2002). Wavelets based feature extraction and multiple classifiers for electricity fraud detection.
  11. Leon, C., Biscarri, F. X. E. L., Monedero, I. X. F. I., Guerrero, J. I., Biscarri, J. X. F. S., and Millan, R. X. E. O. (2011). Variability and trend-based generalized rule induction model to ntl detection in power companies.
  12. Lo, Y.-L., Huang, S.-C., and Lu, C.-N. (2012). Nontechnical loss detection using smart distribution network measurement data. In Innovative Smart Grid Technologies - Asia (ISGT Asia), 2012 IEEE, pages 1-5.
  13. Markoc, Z., Hlupic, N., and Basch, D. (2011). Detection of suspicious patterns of energy consumption using neural network trained by generated samples.
  14. Monedero, I., Biscarri, F., Len, C., Guerrero, J., Biscarri, J., and Milln, R. (2010). Using regression analysis to identify patterns of non-technical losses on power utilities. In Setchi, R., Jordanov, I., Howlett, R., and Jain, L., editors, Knowledge-Based and Intelligent Information and Engineering Systems, volume 6276 of Lecture Notes in Computer Science, pages 410-419. Springer Berlin Heidelberg.
  15. Muniz, C., Vellasco, M., Tanscheit, R., and Figueiredo, K. (2009). Ifsa-eusflat 2009 a neuro-fuzzy system for fraud detection in electricity distribution.
  16. Nagi, J. and Mohamad, M. (2010). Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 25, NO. 2.
  17. Ramos, C., de Sousa, A. N., Papa, J., and Falcao, A. (2010). A new approach for nontechnical losses detection based on optimum-path forest. IEEE TRANSACTIONS ON POWER SYSTEMS.
  18. Romero, J. (2012). Improving the efficiency of power distribution system through technical and non-technical losses reduction.
  19. Sforna, M. (2000). Data mining in power company customer database.
  20. Yap, K. S., Hussien, Z., and Mohamad, A. (2007). Abnormalities and fraud electric meter detection using hybrid support vector machine and genetic algorithm.
  21. Yap, K. S., Tiong, S. K., Nagi, J., Koh, J. S. P., and Nagi, F. (2012). Comparison of supervised learning techniques for non-technical loss detection in power utility.
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Paper Citation


in Harvard Style

Rodríguez F., Lecumberry F. and Fernández A. (2014). Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 624-628. DOI: 10.5220/0004823506240628


in Bibtex Style

@conference{icpram14,
author={Fernanda Rodríguez and Federico Lecumberry and Alicia Fernández},
title={Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={624-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004823506240628},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Non Technical Loses Detection - Experts Labels vs. Inspection Labels in the Learning Stage
SN - 978-989-758-018-5
AU - Rodríguez F.
AU - Lecumberry F.
AU - Fernández A.
PY - 2014
SP - 624
EP - 628
DO - 10.5220/0004823506240628