Identification of Nontechnical Losses in Distribution Systems Adding Exogenous Data and Artificial Intelligence

Marcelo Bruno Capeletti, Bruno Knevitz Hammerschmitt, Renato Grethe Negri, Fernando Guilherme Kaehler Guarda, Lucio Rene Prade, Nelson Knak Neto () and Alzenira da Rosa Abaide
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Marcelo Bruno Capeletti: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Bruno Knevitz Hammerschmitt: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Renato Grethe Negri: Technologic Center, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Fernando Guilherme Kaehler Guarda: Santa Maria Technical and Industrial School, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil
Lucio Rene Prade: Polytechnic School, University of Vale dos Sinos, São Leopoldo 93022-750, Rio Grande do Sul, Brazil
Nelson Knak Neto: Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil
Alzenira da Rosa Abaide: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil

Energies, 2022, vol. 15, issue 23, 1-23

Abstract: Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The test results show the importance of combining exogenous and endogenous data, which obtained a 0.0213 improvement in ROC-AUC and a 6.26% recall (1).

Keywords: nontechnical losses; outliers identification; exogenous data; power system distribution; artificial neural networks; hyperparameter optimization; data mining; big data (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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