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Comparative Study of Data Driven Approaches Towards Efficient Electricity Theft Detection in Micro Grids

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 279)

Abstract

In this research article, we tackle the following limitations: high misclassification rate, low detection rate and, class imbalance problem and no availability of malicious or theft samples. The class imbalanced problem is severe issue in electricity theft detection that affects the performance of supervised learning methods. We exploit the adaptive synthetic minority oversampling technique to tackle this problem. Moreover, theft samples are created from benign samples and we argue that the goal of theft is to report less than consumption actual electricity consumption. Different machine learning and deep learning methods including recently developed light and extreme gradient boosting (XGBoost), are trained and evaluated on a realistic electricity consumption dataset that is provided by an electric utility in Pakistan. The consumers in the dataset belong to different demographics and, different social and financial backgrounds. Different number of classifiers are trained on acquired data; however, long short-term memory (LSTM) and XGBoost attain high performance and outperform all classifiers. The XGBoost achieves a 0.981 detection rate and 0.015 misclassification rate. Whereas, LSTM attains 0.976 and 0.033 detection and misclassification rate, respectively. Moreover, the performance of all implemented classifiers is evaluated through precision, recall, F1-score, etc.

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  • DOI: 10.1007/978-3-030-79728-7_13
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Fig. 1.
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Notes

  1. 1.

    Benign and normal samples are used alternatively.

  2. 2.

    PRECON: PAKISTAN RESIDENTIAL ELECTRICITY CONSUMPTION DATASET.

References

  1. Avila, N.F., Figueroa, G., Chu, C.-C.: NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting. IEEE Trans. Power Syst. 33(6), 7171–7180 (2018)

    Google Scholar 

  2. Jokar, P., Arianpoo, N., Leung, V.C.M.: Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans. Smart Grid 7(1), 216–226 (2015)

    Google Scholar 

  3. Punmiya, R., Choe, S.: Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans. Smart Grid 10(2), 2326–2329 (2019)

    CrossRef  Google Scholar 

  4. Khan, Z.A., Adil, M., Javaid, N., Saqib, M.N., Shafiq, M., Choi, J.-G.: Electricity theft detection using supervised learning techniques on smart meter data. Sustainability 12(19), 8023 (2020)

    Google Scholar 

  5. Arif, A., Javaid, N., Aldegheishem, A., Alrajeh, N.: Big data analytics for identifying electricity theft using machine learning approaches in micro grids for smart communities. Concurrency Comput. Pract. Experience, 1532–0634 (2021)

    Google Scholar 

  6. Ghori, K.M., Abbasi, R.A., Awais, M., Imran, M., Ullah, A., Szathmary, L.: Performance analysis of different types of machine learning classifiers for non-technical loss detection. IEEE Access 8, 16033–16048 (2019)

    Google Scholar 

  7. Razavi, R., Gharipour, A., Fleury, M., Akpan, I.J.: A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energy 238 , 481–494 (2019)

    Google Scholar 

  8. Kong, X., Zhao, X., Liu, C., Li, Q., Dong, D.L., Li, Y.: Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. Int. J. Electr. Power Energy Syst.125, 106544 (2021)

    Google Scholar 

  9. Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Detection of non-technical losses using smart meter data and supervised learning. IEEE Trans. Smart Grid 10(3), 2661–2670 (2018)

    Google Scholar 

  10. Buzau, M.-M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst. 35(2), 1254–1263 (2019)

    CrossRef  Google Scholar 

  11. Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Ind. Inform. 14(4), 1606–1615 (2017)

    CrossRef  Google Scholar 

  12. Huang, Y., Qifeng, X.: Electricity theft detection based on stacked sparse denoising autoencoder. Int. J. Electr. Power Energy Syst. 125, 106448 (2021)

    Google Scholar 

  13. Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645–9657 (2019)

    CrossRef  Google Scholar 

  14. Bhat, R.R., Trevizan, R.D., Sengupta, R., Li, X., Bretas, A.: Identifying nontechnical power loss via spatial and temporal deep learning. In: 2016 15th IEEE International Conference on Machine Learning and Applications, pp. 272–279 (2016)

    Google Scholar 

  15. Hasan, M., Toma, R.N., Nahid, A.-A., Islam, M.M., Kim, J.-M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)

    Google Scholar 

  16. Ramos, C.C.O., Rodrigues, D., de Souza, A.N., Papa, J.P.: On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization. IEEE Trans. Smart Grid 9(2), 676–683 (2016)

    Google Scholar 

  17. Coma-Puig, B., Carmona, J.: Bridging the gap between energy consumption and distribution through non-technical loss detection. Energies 12(9), 1748 (2019)

    CrossRef  Google Scholar 

  18. Hu, T., Guo, Q., Sun, H., Huang, T.-E., Lan, J.: Nontechnical losses detection through coordinated BIWGAN and SVDD. IEEE Trans. Neural Netw. Learn. Syst. 32, 1866–1880 (2020)

    Google Scholar 

  19. He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks, pp. 1322–1328 (2008)

    Google Scholar 

  20. Javaid, N., Jan, N., Javed, M.U.: An adaptive synthesis to handle imbalanced big data with deep Siamese network for electricity theft detection in smart grids. J. Parallel Distrib. Comput, 0743–7315 (2021)

    Google Scholar 

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Shehzad, F. et al. (2022). Comparative Study of Data Driven Approaches Towards Efficient Electricity Theft Detection in Micro Grids. In: Barolli, L., Yim, K., Chen, HC. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2021. Lecture Notes in Networks and Systems, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-79728-7_13

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