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Computational Intelligent Approaches for Non-Technical Losses Management of Electricity

Author

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  • Rubén González Rodríguez

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Jamer Jiménez Mares

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

  • Christian G. Quintero M.

    (Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia)

Abstract

This paper presents an intelligent system for the detection of non-technical losses of electrical energy associated with the fraudulent behaviors of system users. This proposal has three stages: a non-supervised clustering of consumption profiles based on a hybrid algorithm between self-organizing maps (SOM) and genetic algorithms (GA). A second stage for demand forecasting is based on ARIMA (autoregressive integrated moving average) models corrected intelligently through neural networks (ANN). The final stage is a classifier based on random forests for fraudulent user detection. The proposed intelligent approach was trained and tested with real data from the Colombian Caribbean region, where the utility reports energy losses of around 18% of the total energy purchased by the company during the five last years. The results show an average overall performance of 82.9% in the detection process of fraudulent users, significantly increasing the effectiveness compared to the approaches (68%) previously applied by the utility in the region.

Suggested Citation

  • Rubén González Rodríguez & Jamer Jiménez Mares & Christian G. Quintero M., 2020. "Computational Intelligent Approaches for Non-Technical Losses Management of Electricity," Energies, MDPI, vol. 13(9), pages 1-25, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2393-:d:356591
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    References listed on IDEAS

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    1. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    2. Ahmad, Tanveer, 2017. "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 573-589.
    3. Viegas, Joaquim L. & Esteves, Paulo R. & Melício, R. & Mendes, V.M.F. & Vieira, Susana M., 2017. "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1256-1268.
    4. Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
    5. Bengtsson, Thomas & Cavanaugh, Joseph E., 2006. "An improved Akaike information criterion for state-space model selection," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2635-2654, June.
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    Cited by:

    1. Gideon Otchere-Appiah & Shingo Takahashi & Mavis Serwaa Yeboah & Yuichiro Yoshida, 2021. "The Impact of Smart Prepaid Metering on Non-Technical Losses in Ghana," Energies, MDPI, vol. 14(7), pages 1-16, March.
    2. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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