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Machine Learning-Based Forecasting of Output Currents for Demand Response Management and Energy Distribution Optimization in Low-Voltage DC Microgrids
https://doi.org/10.17587/mau.26.28-38
Abstract
This study comprehensively evaluates several Machine Learning (ML) techniques to address the challenge of predicting output currents for demand response management and energy distribution optimization in low-voltage Direct Current (DC) microgrids. The study utilizes an extensive dataset of around 33,334 data sets with diverse electrical characteristics. Several prediction algorithms are used and evaluated in a planned way during this process. These include Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Lasso, and Linear Regression (LR). The Random Forest (RF) model outperforms the other models, with a high R^2score of 0.994, indicating a very accurate fit to the observed data. In contrast, the Lasso model has a R^2 score of 0.883, suggesting a somewhat lower effectiveness due to its simplicity. The findings provide a comprehensive assessment of the predictive capabilities of each model, which is further corroborated by other research utilizing measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). For instance, the Random Forest (RF) model showcased its robustness in accurately predicting output currents by attaining the lowest Mean Absolute Error (MAE) of 0.289, Mean Squared Error (MSE) of 0.140, and Root Mean Squared Error (RMSE) of 0.374. This comprehensive evaluation enhances the advancement of sustainable and efficient energy distribution networks by emphasizing the potential of Machine Learning (ML) to improve Direct Current (DC) microgrids’ operational efficiency. It also establishes the foundation for future research on integrating these algorithms into real energy systems
About the Authors
E. Abdallah
Beni-Suef University
Egypt
Egypt
Postgraduate Student, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)
K. Menoufi
Beni-Suef University
Egypt
Egypt
Lecturer, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)
N. Shehata
Beni-Suef University
Egypt
Egypt
Professor, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)
M. A. Ghalib
Beni-Suef University
Egypt
Egypt
Lecturer, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)
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Review
For citations:
Abdallah E., Menoufi K., Shehata N., Ghalib M.A. Machine Learning-Based Forecasting of Output Currents for Demand Response Management and Energy Distribution Optimization in Low-Voltage DC Microgrids. Mekhatronika, Avtomatizatsiya, Upravlenie. 2025;26(1):28-38. https://doi.org/10.17587/mau.26.28-38
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