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Multi-scale DenseNet-Based Electricity Theft Detection

  • Bo Li
  • Kele Xu
  • Xiaoyan Cui
  • Yiheng Wang
  • Xinbo Ai
  • Yanbo Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

Electricity theft detection issue has drawn lots of attention during last decades. Timely identification of the electricity theft in the power system is crucial for the safety and availability of the system. Although sustainable efforts have been made, the detection task remains challenging and falls short of accuracy and efficiency, especially with the increase of the data size. Recently, convolutional neural network-based methods have achieved better performance in comparison with traditional methods, which employ handcrafted features and shallow-architecture classifiers. In this paper, we present a novel approach for automatic detection by using a multi-scale dense connected convolution neural network (multi-scale DenseNet) in order to capture the long-term and short-term periodic features within the sequential data. We compare the proposed approaches with the classical algorithms, and the experimental results demonstrate that the multi-scale DenseNet approach can significantly improve the accuracy of the detection. Moreover, our method is scalable, enabling larger data processing while no handcrafted feature engineering is needed.

Keywords

Electricity theft detection Convolutional neural network DenseNet Multi-scale 

References

  1. 1.
    Mcdaniel, P., Mclaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7, 75–77 (2009)CrossRefGoogle Scholar
  2. 2.
    Navani, J.P., Sharma, N.K., Sapra, S.: Technical and non-technical losses in power system and its economic consequence in Indian economy. Int. J. Electr. Comput. Sci. Eng. 1(2), 757–761 (2012)Google Scholar
  3. 3.
    Lo, C.H., Ansari, N.: CONSUMER: a novel hybrid intrusion detection system for distribution networks in smart grid. IEEE Tran. Emer. Topic Comput. 1, 33–34 (2013)CrossRefGoogle Scholar
  4. 4.
    Xiao, Z., Xiao, Y., Du, H.C.: Non-repudiation in neighborhood area networks for smart grid. Commun. Mag. IEEE. 51, 18–26 (2015)CrossRefGoogle Scholar
  5. 5.
    Cardenas, A.A., Amin, S., Schwartz, G., Dong, R.: A game theory model for electricity theft detection and privacy-aware control in AMI systems. In: 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1830–1837 (2015)Google Scholar
  6. 6.
    Angelos, E.W.S., Saavedra, O.R., Cortés, O.A.C., De Souza, A.N.: Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Trans. Power Delivery 26, 2436–2442 (2011)CrossRefGoogle Scholar
  7. 7.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Support vector machine-based data classification for detection of electricity theft. In: Power Systems Conference and Exposition (PSCE), pp. 1–8 (2011)Google Scholar
  8. 8.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Green, R.C.: High performance computing for detection of electricity theft. Int. J. Electr. Power Energ. Syst. 47, 21–30 (2013)CrossRefGoogle Scholar
  9. 9.
    Di, M., Decia, F., Molinelli, J., Fernández, A.: Improving electric fraud detection using class imbalance strategies. In: International Conference on Pattern Recognition Applications and Methods, vol. 3, pp. III-841–III-844 (2012)Google Scholar
  10. 10.
    Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., Mishra, S.: Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Ind. Inform. 12, 1005–1016 (2016)CrossRefGoogle Scholar
  11. 11.
    Krizhevsky, A., Hinton, G.E., Sutskever, I.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)Google Scholar
  12. 12.
    Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29, 82–97 (2012)CrossRefGoogle Scholar
  13. 13.
    Johnston, G.: Statistical Models and Methods for Lifetime Data, pp. 264–265. Wiley, New York (1982)CrossRefGoogle Scholar
  14. 14.
    Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43, 1947 (2003)CrossRefGoogle Scholar
  15. 15.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, pp. 71–80. Prentice Hall PTR, Upper Saddle River (1994)Google Scholar
  16. 16.
    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Int. Syst. Appl. 13, 18–28 (1998)CrossRefGoogle Scholar
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  18. 18.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2014)Google Scholar
  19. 19.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)Google Scholar
  20. 20.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  21. 21.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Xu, K., Roussel, P., Csapo, T.G., Denby, B.: Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images. J. Acoust. Soc. Am. 141, EL531–EL537 (2017)CrossRefGoogle Scholar
  23. 23.
    Berrut, J.P., Trefethen, L.N.: Barycentric lagrange interpolation. SIAM Rev. 46, 501–517 (2004)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Xu, K., Feng, D., Mi, H.: Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22, 2054 (2017)CrossRefGoogle Scholar
  25. 25.
    Shore, J., Johnson, R.: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. Inf. Theor. IEEE Trans. 26, 26–37 (1980)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17, 299–310 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bo Li
    • 1
  • Kele Xu
    • 2
    • 3
  • Xiaoyan Cui
    • 1
  • Yiheng Wang
    • 4
  • Xinbo Ai
    • 1
  • Yanbo Wang
    • 5
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of ComputerNational University of Defense TechnologyChangshaChina
  3. 3.School of Information CommunicationNational University of Defense TechnologyWuhanChina
  4. 4.The University of MelbourneParkvilleAustralia
  5. 5.China Minsheng BankBeijingChina

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