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

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 irriga...

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Bibliographic Details
Published in:Energies (Basel) 2023-10, Vol.16 (19), p.6832
Main Authors: Vieira, Vanessa Gindri, Bernardon, Daniel Pinheiro, Uberti, Vinícius André, de Figueiredo, Rodrigo Marques, de Chiara, Lucas Melo, Silva, Juliano Andrade
Format: Article
Language:English
Subjects:
Artificial intelligence
Big Data
Consumer behavior
Consumers
Crops
Efficiency
Electric utilities
Electricity
Energy consumption
energy efficiency
energy for agricultural processes
Fraud
Irrigation
Laws, regulations and rules
Marketing research
non-technical losses
Pilot projects
pilot study
Productivity
Public utilities
Rice
rural electrical grids
Sugarcane
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16196832