Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks
This research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabli...
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2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/12/3156 |
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| author | Ihor Blinov Virginijus Radziukynas Pavlo Shymaniuk Artur Dyczko Kinga Stecuła Viktoriia Sychova Volodymyr Miroshnyk Roman Dychkovskyi |
| author_facet | Ihor Blinov Virginijus Radziukynas Pavlo Shymaniuk Artur Dyczko Kinga Stecuła Viktoriia Sychova Volodymyr Miroshnyk Roman Dychkovskyi |
| author_sort | Ihor Blinov |
| collection | DOAJ |
| description | This research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabling more efficient and intelligent grid operation. Two predictive approaches were explored: the first involves separate forecasting of nodal loads followed by loss calculations, while the second directly estimates network-wide energy losses. For model implementation, Long Short-Term Memory (LSTM) networks and the enhanced Residual Network (eResNet) architecture, developed at the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, were utilized. The models were validated using retrospective data from a Ukrainian Distribution System Operator (DSO) covering the period from 2017 to 2019 with 30 min sampling intervals. An adapted CIGRE benchmark medium-voltage network was employed to simulate real-world conditions. Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. The results indicate a Mean Absolute Percentage Error (MAPE) of just 3.29% for nodal load forecasts, which significantly outperforms conventional methods. These findings affirm the feasibility of integrating such models into Smart Grid infrastructures to improve decision-making, minimize operational losses, and reduce the costs associated with energy loss compensation. This study provides a practical framework for data-driven energy loss management, emphasizing the growing role of artificial intelligence in modern power systems. |
| format | Article |
| id | doaj-art-c371d7feccf047d8b0f70fd48405dbbf |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c371d7feccf047d8b0f70fd48405dbbf2025-08-20T03:24:35ZengMDPI AGEnergies1996-10732025-06-011812315610.3390/en18123156Smart Management of Energy Losses in Distribution Networks Using Deep Neural NetworksIhor Blinov0Virginijus Radziukynas1Pavlo Shymaniuk2Artur Dyczko3Kinga Stecuła4Viktoriia Sychova5Volodymyr Miroshnyk6Roman Dychkovskyi7Institute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, UkraineSmart Grids and Renewable Energy Laboratory, Lithuanian Energy Institute, 44403 Kaunas, LithuaniaInstitute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, UkraineMineral and Energy Economy Research Institute, Polish Academy of Sciences, 7A Wybickiego St., 31-261 Krakow, PolandFaculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, PolandInstitute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, UkraineInstitute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, UkraineDepartment of Mining Engineering and Education, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, UkraineThis research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabling more efficient and intelligent grid operation. Two predictive approaches were explored: the first involves separate forecasting of nodal loads followed by loss calculations, while the second directly estimates network-wide energy losses. For model implementation, Long Short-Term Memory (LSTM) networks and the enhanced Residual Network (eResNet) architecture, developed at the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, were utilized. The models were validated using retrospective data from a Ukrainian Distribution System Operator (DSO) covering the period from 2017 to 2019 with 30 min sampling intervals. An adapted CIGRE benchmark medium-voltage network was employed to simulate real-world conditions. Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. The results indicate a Mean Absolute Percentage Error (MAPE) of just 3.29% for nodal load forecasts, which significantly outperforms conventional methods. These findings affirm the feasibility of integrating such models into Smart Grid infrastructures to improve decision-making, minimize operational losses, and reduce the costs associated with energy loss compensation. This study provides a practical framework for data-driven energy loss management, emphasizing the growing role of artificial intelligence in modern power systems.https://www.mdpi.com/1996-1073/18/12/3156energy loss forecastingdeep neural networksdata preprocessingpower system efficiencyartificial intelligence in energyelectrical grid optimization |
| spellingShingle | Ihor Blinov Virginijus Radziukynas Pavlo Shymaniuk Artur Dyczko Kinga Stecuła Viktoriia Sychova Volodymyr Miroshnyk Roman Dychkovskyi Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks Energies energy loss forecasting deep neural networks data preprocessing power system efficiency artificial intelligence in energy electrical grid optimization |
| title | Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks |
| title_full | Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks |
| title_fullStr | Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks |
| title_full_unstemmed | Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks |
| title_short | Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks |
| title_sort | smart management of energy losses in distribution networks using deep neural networks |
| topic | energy loss forecasting deep neural networks data preprocessing power system efficiency artificial intelligence in energy electrical grid optimization |
| url | https://www.mdpi.com/1996-1073/18/12/3156 |
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