Open Access
E3S Web Conf.
Volume 256, 2021
2021 International Conference on Power System and Energy Internet (PoSEI2021)
Article Number 01025
Number of page(s) 5
Section Smart Grid Technology and Power System Regulation Modeling
Published online 10 May 2021
  1. 96 Billion is Lost Every Year to Electricity Theft. Accessed: May 8, 2017. [Online]. Available at: to-electricity-theft-300453411.html [Google Scholar]
  2. Chen Z, Meng D, Zhang Y, et al. Electricity Theft Detection Using Deep Bidirectional Recurrent Neural Network[C]//2020 22nd International Conference on Advanced Communication Technology (ICACT). IEEE, 2020: 401-406. [Google Scholar]
  3. Salman Saeed M, Mustafa M W, Sheikh U U, et al. An efficient boosted C5. 0 Decision-Tree-Based classification approach for detecting non-technical losses in power utilities[J]. Energies, 2020, 13(12): 3242. [Google Scholar]
  4. Nizar A H, Dong Z Y, Wang Y. Power utility nontechnical loss analysis with extreme learning machine method[J]. IEEE Transactions on Power Systems, 2008, 23(3): 946–955. [Google Scholar]
  5. Tacón J, Melgarejo D, Rodríguez F, et al. Semisupervised approach to non technical losses detection[C]//Iberoamerican Congress on Pattern Recognition. Springer, Cham, 2014: 698-705. [Google Scholar]
  6. Punmiya, R., & Choe, S. (2019). Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Transactions on Smart Grid [PubMed] [Google Scholar]
  7. Spirić J V, Stanković S S, Dočić M B. Identification of suspicious electricity customers[J]. International Journal of Electrical Power & Energy Systems, 2018, 95: 635-643. [Google Scholar]
  8. Viegas J L, Esteves P R, Vieira S M. Clustering-based novelty detection for identification of non-technical losses[J]. International Journal of Electrical Power & Energy Systems, 2018, 101: 301-310. [Google Scholar]
  9. Krishna V B, Weaver G A, Sanders W H. PCA-based method for detecting integrity attacks on advanced metering infrastructure[C]//International Conference on Quantitative Evaluation of Systems. Springer, Cham, 2015: 70-85. [Google Scholar]
  10. Ramos C C O, de Sousa A N, Papa J P, et al. A new approach for nontechnical losses detection based on optimum-path forest[J]. IEEE Transactions on Power Systems, 2010, 26(1): 181–189. [Google Scholar]
  11. ISSDA. Data from the Commission for Energy Regulation – <>. [Google Scholar]
  12. Jin, H., Song, Q., & Hu, X. (2019). Auto-Keras: An Efficient Neural Architecture Search System. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. [Google Scholar]
  13. Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th international conference on Machine learning. 2008: 1096-1103. [Google Scholar]
  14. Hoffmann H. Kernel PCA for novelty detection[J]. Pattern recognition, 2007, 40(3): 863–874. [Google Scholar]
  15. He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10), pp.1641-1650. [CrossRef] [Google Scholar]
  16. Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In KI-2012: Poster and Demo Track, pp.59-63. [Google Scholar]
  17. Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In KDD ‘08, pp. 444-452. ACM. [Google Scholar]
  18. Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In International Conference on Data Mining, pp. 413-422. IEEE. [Google Scholar]

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