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Big data analytics: an aid to detection of non-technical losses in power utilities

Author

Listed:
  • Giovanni Micheli

    (University of Bergamo)

  • Emiliano Soda

    (CESI)

  • Maria Teresa Vespucci

    (University of Bergamo)

  • Marco Gobbi

    (CESI)

  • Alessandro Bertani

    (CESI)

Abstract

The great amount of data collected by the Advanced Metering Infrastructure can help electric utilities to detect energy theft, a phenomenon that globally costs over 25 billions of dollars per year. To address this challenge, this paper describes a new approach to non-technical loss analysis in power utilities using a variant of the P2P computing that allows identifying frauds in the absence of total reachability of smart meters. Specifically, the proposed approach compares data recorded by the smart meters and by the collector in the same neighborhood area and detects the fraudulent customers through the application of a Multiple Linear Regression model. Using real utility data, the regression model has been compared with other data mining techniques such as SVM, neural networks and logistic regression, in order to validate the proposed approach. The empirical results show that the Multiple Linear Regression model can efficiently identify the energy thieves even in areas with problems of meters reachability.

Suggested Citation

  • Giovanni Micheli & Emiliano Soda & Maria Teresa Vespucci & Marco Gobbi & Alessandro Bertani, 2019. "Big data analytics: an aid to detection of non-technical losses in power utilities," Computational Management Science, Springer, vol. 16(1), pages 329-343, February.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:1:d:10.1007_s10287-018-0325-x
    DOI: 10.1007/s10287-018-0325-x
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    Cited by:

    1. Pamir & Nadeem Javaid & Saher Javaid & Muhammad Asif & Muhammad Umar Javed & Adamu Sani Yahaya & Sheraz Aslam, 2022. "Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit," Energies, MDPI, vol. 15(8), pages 1-20, April.
    2. Russell Tatenda Munodawafa & Satirenjit Kaur Johl, 2019. "Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies," Sustainability, MDPI, vol. 11(15), pages 1-21, August.

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