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Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Author

Listed:
  • Murilo A. Souza

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Hugo T. V. Gouveia

    (Independent Researcher, Recife 50740-550, Brazil)

  • Aida A. Ferreira

    (Department of Electrical Systems, Federal Institute of Pernambuco, Recife 50740-545, Brazil)

  • Regina Maria de Lima Neta

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Otoni Nóbrega Neto

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Milde Maria da Silva Lira

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Geraldo L. Torres

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Ronaldo R. B. de Aquino

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

Abstract

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

Suggested Citation

  • Murilo A. Souza & Hugo T. V. Gouveia & Aida A. Ferreira & Regina Maria de Lima Neta & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Geraldo L. Torres & Ronaldo R. B. de Aquino, 2024. "Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models," Energies, MDPI, vol. 17(7), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1729-:d:1370124
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    References listed on IDEAS

    as
    1. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
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