Open Access

Machine learning in electricity fraud detection in smart grids with multivariate Gaussian distribution


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Aziz, S., Naqvi, S. Z. H., Khan, M. U., & Aslam, T. (2020). Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors. In 2020 International Conference on Emerging Trends in Smart Technologies, ICETST 2020. https://doi.org/10.1109/ICETST49965.2020.9080727.10.1109/ICETST49965.2020.9080727 Search in Google Scholar

Basu, K., Debusschere, V., Douzal-Chouakria, A., & Bacha, S. (2015). Time series distance-based methods for non-intrusive load monitoring in residential buildings. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2015.03.021.10.1016/j.enbuild.2015.03.021 Search in Google Scholar

Cody, C., Ford, V., & Siraj, A. (2016). Decision tree learning for fraud detection in consumer energy consumption. In Proceedings – 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. https://doi.org/10.1109/ICMLA.2015.80.10.1109/ICMLA.2015.80 Search in Google Scholar

Coma-Puig, B., Carmona, J., Gavalda, R., Alcoverro, S., & Martin, V. (2016). Fraud detection in energy consumption: A supervised approach. In Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016. https://doi.org/10.1109/DSAA.2016.19.10.1109/DSAA.2016.19 Search in Google Scholar

Jain, S., Choksi, K. A., & Pindoriya, N. M. (2019). Rule-based classification of energy theft and anomalies in consumers load demand profile. IET Smart Grid. https://doi.org/10.1049/ietstg.2019.0081.10.1049/iet-stg.2019.0081 Search in Google Scholar

Lyu, L., Jin, J., Rajasegarar, S., He, X., & Palaniswami, M. (2017). Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2017.2709942.10.1109/JIOT.2017.2709942 Search in Google Scholar

Massaferro, P., Martino, J. M. Di, & Fernandez, A. (2020). Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return. IEEE Transactions on Power Systems. https://doi.org/10.1109/TPWRS.2019.2928276.10.1109/TPWRS.2019.2928276 Search in Google Scholar

Siffer, A., Fouque, P. A., Termier, A., & Largouet, C. (2017). Anomaly detection in streams with extreme value theory. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098144.10.1145/3097983.3098144 Search in Google Scholar

Spirić, J. V., Dočić, M. B., & Stanković, S. S. (2015). Fraud detection in registered electricity time series. International Journal of Electrical Power and Energy Systems. https://doi.org/10.1016/j.ijepes.2015.02.037.10.1016/j.ijepes.2015.02.037 Search in Google Scholar

Yip, S. C., Tan, W. N., Tan, C. K., Gan, M. T., & Wong, K. S. (2018). An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power and Energy Systems. https://doi.org/10.1016/j.ijepes.2018.03.025.10.1016/j.ijepes.2018.03.025 Search in Google Scholar

Zanetti, M., Jamhour, E., Pellenz, M., Penna, M., Zambenedetti, V., & Chueiri, I. (2019). A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2017.2753738.10.1109/TSG.2017.2753738 Search in Google Scholar

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English