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Detection of Non-Technical Losses in Irrigant Consumers through Artificial Intelligence: A Pilot Study

Author

Listed:
  • Vanessa Gindri Vieira

    (Graduate Program in Electrical Engineering-PPGEE, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, RS, Brazil
    These authors contributed equally to this work.)

  • Daniel Pinheiro Bernardon

    (Graduate Program in Electrical Engineering-PPGEE, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, RS, Brazil
    These authors contributed equally to this work.)

  • Vinícius André Uberti

    (Polytechnic School, University of Rio dos Sinos Valley, São Leopoldo 93022-750, RS, Brazil
    These authors contributed equally to this work.)

  • Rodrigo Marques de Figueiredo

    (Polytechnic School, University of Rio dos Sinos Valley, São Leopoldo 93022-750, RS, Brazil
    These authors contributed equally to this work.)

  • Lucas Melo de Chiara

    (Energy CPFL, Campinas 13088-900, SP, Brazil
    These authors contributed equally to this work.)

  • Juliano Andrade Silva

    (Energy CPFL, Campinas 13088-900, SP, Brazil
    These authors contributed equally to this work.)

Abstract

Non-technical losses (NTLs) verified in the power distribution grids cause great financial losses to power utilities. In rural distribution grids, fraudulent consumers contribute to technical problems. The Southern region in Brazil contains more than 70% of the total rice production and power irrigation systems. These systems operate seasonally in distribution grids with high NTL conditions. This work aimed to present an artificial intelligence-based system to help power distribution companies detect potential consumers causing NTLs. This minimizes the challenge of maintaining compliance with current regulations and ensuring the quality of services and products. In the proposed methodology, historical energy consumption information, meteorological data, satellite images, and data from energy suppliers are processed by artificial intelligence, indicating the suspicious consumer units of NTL. This work presents every step developed in the proposed methodology and the tool application in a pilot area. We detected a high number of consumers responsible for NTLs, with an accuracy of 63% and an average reduction of 78% in the search area. These results corroborated the effectiveness of the tool and instigated the research team to expand the application to other rice production areas.

Suggested Citation

  • Vanessa Gindri Vieira & Daniel Pinheiro Bernardon & Vinícius André Uberti & Rodrigo Marques de Figueiredo & Lucas Melo de Chiara & Juliano Andrade Silva, 2023. "Detection of Non-Technical Losses in Irrigant Consumers through Artificial Intelligence: A Pilot Study," Energies, MDPI, vol. 16(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6832-:d:1248403
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    References listed on IDEAS

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    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.
    2. Cheong Hee Park & Taegong Kim, 2020. "Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(15), pages 1-10, July.
    3. Muhammad Salman Saeed & Mohd Wazir Mustafa & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Ilyas Khan & Samer Atawneh & Nawaf N. Hamadneh, 2020. "An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities," Energies, MDPI, vol. 13(12), pages 1-19, June.
    4. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
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