Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks

Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko (), Kinga Stecuła (), Viktoriia Sychova, Volodymyr Miroshnyk and Roman Dychkovskyi
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Ihor Blinov: Institute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, Ukraine
Virginijus Radziukynas: Smart Grids and Renewable Energy Laboratory, Lithuanian Energy Institute, 44403 Kaunas, Lithuania
Pavlo Shymaniuk: Institute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, Ukraine
Artur Dyczko: Mineral and Energy Economy Research Institute, Polish Academy of Sciences, 7A Wybickiego St., 31-261 Krakow, Poland
Kinga Stecuła: Faculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, Poland
Viktoriia Sychova: Institute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, Ukraine
Volodymyr Miroshnyk: Institute of Electrodynamics NASU, Department of Modelling of Electrical Power Objects and Systems, 03057 Kyiv, Ukraine
Roman Dychkovskyi: Department of Mining Engineering and Education, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, Ukraine

Energies, 2025, vol. 18, issue 12, 1-17

Abstract: 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.

Keywords: energy loss forecasting; deep neural networks; data preprocessing; power system efficiency; artificial intelligence in energy; electrical grid optimization (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: 2025
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