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.
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References
Mcdaniel, P., Mclaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7, 75–77 (2009)
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)
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)
Xiao, Z., Xiao, Y., Du, H.C.: Non-repudiation in neighborhood area networks for smart grid. Commun. Mag. IEEE. 51, 18–26 (2015)
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)
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)
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)
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)
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)
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)
Krizhevsky, A., Hinton, G.E., Sutskever, I.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
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)
Johnston, G.: Statistical Models and Methods for Lifetime Data, pp. 264–265. Wiley, New York (1982)
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)
Haykin, S.: Neural Networks: A Comprehensive Foundation, pp. 71–80. Prentice Hall PTR, Upper Saddle River (1994)
Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Int. Syst. Appl. 13, 18–28 (1998)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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)
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)
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)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
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)
Berrut, J.P., Trefethen, L.N.: Barycentric lagrange interpolation. SIAM Rev. 46, 501–517 (2004)
Xu, K., Feng, D., Mi, H.: Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22, 2054 (2017)
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)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17, 299–310 (2005)
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Li, B., Xu, K., Cui, X., Wang, Y., Ai, X., Wang, Y. (2018). Multi-scale DenseNet-Based Electricity Theft Detection. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_17
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DOI: https://doi.org/10.1007/978-3-319-95930-6_17
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