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

Vanessa Gindri Vieira (), Daniel Pinheiro Bernardon (), Vinícius André Uberti, Rodrigo Marques de Figueiredo, Lucas Melo de Chiara and Juliano Andrade Silva
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Vanessa Gindri Vieira: Graduate Program in Electrical Engineering-PPGEE, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, RS, Brazil
Daniel Pinheiro Bernardon: Graduate Program in Electrical Engineering-PPGEE, Federal University of Santa Maria—UFSM, Santa Maria 97105-900, RS, Brazil
Vinícius André Uberti: Polytechnic School, University of Rio dos Sinos Valley, São Leopoldo 93022-750, RS, Brazil
Rodrigo Marques de Figueiredo: Polytechnic School, University of Rio dos Sinos Valley, São Leopoldo 93022-750, RS, Brazil
Lucas Melo de Chiara: Energy CPFL, Campinas 13088-900, SP, Brazil
Juliano Andrade Silva: Energy CPFL, Campinas 13088-900, SP, Brazil

Energies, 2023, vol. 16, issue 19, 1-17

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.

Keywords: non-technical losses; rural electrical grids; artificial intelligence; pilot study; energy for agricultural processes; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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