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

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 and Ronaldo R. B. de Aquino ()
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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

Energies, 2024, vol. 17, issue 7, 1-16

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.

Keywords: non-technical loss; distribution systems; smart grids; artificial intelligence (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: 2024
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