Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders
The implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neur...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6403 |
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| author | Levi da Costa Pimentel Ricardo Wagner Correia Guerra Filho Juan Moises Mauricio Villanueva Yuri Percy Molina Rodriguez |
| author_facet | Levi da Costa Pimentel Ricardo Wagner Correia Guerra Filho Juan Moises Mauricio Villanueva Yuri Percy Molina Rodriguez |
| author_sort | Levi da Costa Pimentel |
| collection | DOAJ |
| description | The implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neural Network autoencoders. The proposed approach is submitted across multiple scenarios using real-world data from a substation, where the influence of the variation in the number of outliers present in the database is evaluated, as well as the variation in their amplitudes on the functioning of the algorithms. The results provide an overview of the operation as well as demonstrate the effectiveness of the proposed methodology that manages to improve some indices achieved by previous works, reaching accuracy and F-score superior to 99% and 97%, respectively, for the detection algorithm, as well as a square root mean squared error (RMSE) and a mean absolute percentage error (MAPE) of less than 0.2 MW and 2%, respectively. |
| format | Article |
| id | doaj-art-4ccff06126c9450a8bdfbdfce15bd629 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-4ccff06126c9450a8bdfbdfce15bd6292025-08-20T02:57:07ZengMDPI AGEnergies1996-10732024-12-011724640310.3390/en17246403Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse AutoencodersLevi da Costa Pimentel0Ricardo Wagner Correia Guerra Filho1Juan Moises Mauricio Villanueva2Yuri Percy Molina Rodriguez3Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, BrazilDepartment of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, BrazilDepartment of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, BrazilDepartment of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, BrazilThe implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neural Network autoencoders. The proposed approach is submitted across multiple scenarios using real-world data from a substation, where the influence of the variation in the number of outliers present in the database is evaluated, as well as the variation in their amplitudes on the functioning of the algorithms. The results provide an overview of the operation as well as demonstrate the effectiveness of the proposed methodology that manages to improve some indices achieved by previous works, reaching accuracy and F-score superior to 99% and 97%, respectively, for the detection algorithm, as well as a square root mean squared error (RMSE) and a mean absolute percentage error (MAPE) of less than 0.2 MW and 2%, respectively.https://www.mdpi.com/1996-1073/17/24/6403smart gridssmart metersoutlier detectionautoencodersartificial neural networks |
| spellingShingle | Levi da Costa Pimentel Ricardo Wagner Correia Guerra Filho Juan Moises Mauricio Villanueva Yuri Percy Molina Rodriguez Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders Energies smart grids smart meters outlier detection autoencoders artificial neural networks |
| title | Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders |
| title_full | Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders |
| title_fullStr | Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders |
| title_full_unstemmed | Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders |
| title_short | Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders |
| title_sort | outlier detection and correction in smart grid energy demand data using sparse autoencoders |
| topic | smart grids smart meters outlier detection autoencoders artificial neural networks |
| url | https://www.mdpi.com/1996-1073/17/24/6403 |
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