Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models
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 pr...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/7/1729 |
| _version_ | 1797212644316282880 |
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| author | 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 Ronaldo R. B. de Aquino |
| author_facet | 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 Ronaldo R. B. de Aquino |
| author_sort | Murilo A. Souza |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:45:39Z |
| format | Article |
| id | doaj.art-90929b3272a241e59394f1871dade386 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-24T10:45:39Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-90929b3272a241e59394f1871dade3862024-04-12T13:18:12ZengMDPI AGEnergies1996-10732024-04-01177172910.3390/en17071729Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence ModelsMurilo A. Souza0Hugo T. V. Gouveia1Aida A. Ferreira2Regina Maria de Lima Neta3Otoni Nóbrega Neto4Milde Maria da Silva Lira5Geraldo L. Torres6Ronaldo R. B. de Aquino7Department of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilIndependent Researcher, Recife 50740-550, BrazilDepartment of Electrical Systems, Federal Institute of Pernambuco, Recife 50740-545, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco, Recife 50740-550, BrazilNon-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.https://www.mdpi.com/1996-1073/17/7/1729non-technical lossdistribution systemssmart gridsartificial intelligence |
| spellingShingle | 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 Ronaldo R. B. de Aquino Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models Energies non-technical loss distribution systems smart grids artificial intelligence |
| title | Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models |
| title_full | Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models |
| title_fullStr | Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models |
| title_full_unstemmed | Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models |
| title_short | Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models |
| title_sort | detection of non technical losses on a smart distribution grid based on artificial intelligence models |
| topic | non-technical loss distribution systems smart grids artificial intelligence |
| url | https://www.mdpi.com/1996-1073/17/7/1729 |
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