novtexmechМехатроника, автоматизация, управлениеMekhatronika, Avtomatizatsiya, Upravlenie1684-64272619-1253Commercial Publisher «New Technologies»10.17587/mau.26.28-38novtexmech-1681Research ArticleАВТОМАТИЗАЦИЯ И УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И ПРОИЗВОДСТВАМИAUTOMATION AND CONTROL TECHNOLOGICAL PROCESSESМашинное обучение для решения задачи прогнозирования выходных токов для управления спросом и оптимизации распределения энергии в низковольтных микросетях постоянного токаMachine Learning-Based Forecasting of Output Currents for Demand Response Management and Energy Distribution Optimization in Low-Voltage DC MicrogridsАбдаллаЕ.AbdallahE.

аспирант, кафедра возобновляемой энергетики и инженерных наук, факультет последипломных исследований передовых наук (PSAS)

Postgraduate Student, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)

elham728_sd@psas.bsu.edu.egМенуфиК.MenoufiK.

преподаватель, кафедра возобновляемой энергетики и инженерных наук, факультет последипломных исследований передовых наук (PSAS)

Lecturer, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)

karim_menoufi@hotmail.comШехатаН.ShehataN.

профессор, кафедра возобновляемой энергетики и инженерных наук, факультет последипломных исследований передовых наук (PSAS)

Professor, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)

nabila.shehata@psas.bsu.edu.egГалибМ. А.GhalibM. A.

преподаватель, кафедра возобновляемой энергетики и инженерных наук, факультет последипломных исследований передовых наук (PSAS)

Lecturer, Faculty of Postgraduate Studies for Advanced Sciences (PSAS)

mohamed01177@techedu.bsu.edu.egУниверситет Бени-СуэфЕгипетBeni-Suef UniversityEgypt2025210120252612838Copyright © Commercial Publisher «New Technologies», 20252025Commercial Publisher «New Technologies»Commercial Publisher «New Technologies»https://mech.novtex.ru/jour/about/submissions#copyrightNoticehttps://mech.novtex.ru/jour/article/view/1681

Всесторонне оцениваются несколько методов машинного обучения для решения задачи прогнозирования выходных токов для управления спросом и оптимизации распределения энергии в низковольтных микросетях постоянного тока. Исследование использует обширный набор данных, включающий около 33 334 наборов данных с различными электрическими характеристиками. В процессе исследования используются и оцениваются несколько алгоритмов прогнозирования, включая Decision Tree, Random Forest (RF), Support Vector Regression, Multi-layer Perceptron, Lasso и Linear Regression. Модель RF превосходит другие модели, демонстрируя высокий коэффициент детерминации R2 = 0,994, что указывает на очень точное соответствие наблюдаемым данным. Напротив, модель Lasso имеет коэффициент R2 = 0,883, что указывает на несколько более низкую эффективность из-за ее простоты. Результаты исследования предоставляют всестороннюю оценку прогностических возможностей каждой модели, что дополнительно подтверждается другими исследованиями, использующими такие метрики, как Mean Absolute Error (MAE), Mean Squared Error (MSE) и Root Mean Squared Error (RMSE). Например, модель RF продемонстрировала свою надежность в точном прогнозировании выходных токов, достигнув наименьших значений MAE = 0,289, MSE = 0,140 и RMSE = 0,374. Эта всесторонняя оценка способствует развитию устойчивых и эффективных сетей распределения энергии, подчеркивая потенциал машинного обучения для повышения операционной эффективности микросетей постоянного тока. Также это закладывает основу для будущих исследований по интеграции этих алгоритмов в реальные энергетические системы.

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

машинное обучениемикросети постоянного токараспределение энергииуправление спросомMachine LearningDC MicrogridsEnergy DistributionDemand ResponseReferencesKumar J., Agarwal A., Agarwal V. A review on overall control of DC microgrids, Journal of Energy Storage, 2019, available at: https://doi.org/10.1016/J.EST.2018.11.013.Kumar J., Agarwal A., Agarwal V. A review on overall control of DC microgrids, Journal of Energy Storage, 2019, available at: https://doi.org/10.1016/J.EST.2018.11.013.Al-Ismail F. DC Microgrid Planning, Operation, and Control: A Comprehensive Review, IEEE Access, 2021, no. 9, pp. 36154— 36172, available at: https://doi.org/10.1109/ACCESS.2021.3062840.Al-Ismail F. DC Microgrid Planning, Operation, and Control: A Comprehensive Review, IEEE Access, 2021, no. 9, pp. 36154— 36172, available at: https://doi.org/10.1109/ACCESS.2021.3062840.Wang D., Wu X., Zhao L., Shi Q., Kong W., Dai H. Research on DC Microgrids in the Context of the Rural Energy Internet, 2023 6th Asia Conference on Energy and Electrical Engineering (ACEEE), 2023, pp. 87—91, available at: https://doi.org/10.1109/ACEEE58657.2023.10239631.Wang D., Wu X., Zhao L., Shi Q., Kong W., Dai H. Research on DC Microgrids in the Context of the Rural Energy Internet, 2023 6th Asia Conference on Energy and Electrical Engineering (ACEEE), 2023, pp. 87—91, available at: https://doi.org/10.1109/ACEEE58657.2023.10239631.Ullah S., Haidar A., Hoole P., Zen H., Ahfock T. The current state of Distributed Renewable Generation, challenges of interconnection and opportunities for energy conversion based DC microgrids, Journal of Cleaner Production, 2020, no. 273, p. 122777, available at: https://doi.org/10.1016/j.jclepro.2020.122777Ullah S., Haidar A., Hoole P., Zen H., Ahfock T. The current state of Distributed Renewable Generation, challenges of interconnection and opportunities for energy conversion based DC microgrids, Journal of Cleaner Production, 2020, no. 273, p. 122777, available at: https://doi.org/10.1016/j.jclepro.2020.122777Saponara S., Saletti R., Mihet-Popa L. Recent Trends in DC and Hybrid Microgrids: Opportunities from Renewables Sources, Battery Energy Storages and Bi-Directional Converters, Applied Sciences, 2020, available at: https://doi.org/10.3390/app10124388.Saponara S., Saletti R., Mihet-Popa L. Recent Trends in DC and Hybrid Microgrids: Opportunities from Renewables Sources, Battery Energy Storages and Bi-Directional Converters, Applied Sciences, 2020, available at: https://doi.org/10.3390/app10124388.Davatgaran V., Saniei M., Mortazavi S. Smart distribution system management considering electrical and thermal demand response of energy hubs, Energy, 2019, https://doi.org/10.1016/J.ENERGY.2018.12.005.Davatgaran V., Saniei M., Mortazavi S. Smart distribution system management considering electrical and thermal demand response of energy hubs, Energy, 2019, https://doi.org/10.1016/J.ENERGY.2018.12.005.Jordehi A. Optimisation of demand response in electric power systems, a review, Renewable and Sustainable Energy Reviews, 2019, available at: https://doi.org/10.1016/J.RSER.2018.12.054.Jordehi A. Optimisation of demand response in electric power systems, a review, Renewable and Sustainable Energy Reviews, 2019, available at: https://doi.org/10.1016/J.RSER.2018.12.054.Kou X., Li F., Dong J., Starke M., Munk J., Kuruganti T., Zandi H. A Distributed Energy Management Approach for Residential Demand Response, 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC), 2019, pp. 170—175, available at: https://doi.org/10.1109/ICSGSC.2019.00004.Kou X., Li F., Dong J., Starke M., Munk J., Kuruganti T., Zandi H. A Distributed Energy Management Approach for Residential Demand Response, 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC), 2019, pp. 170—175, available at: https://doi.org/10.1109/ICSGSC.2019.00004.Antonopoulos I., Robu V., Robu V., Couraud B., Kirli D., Norbu S., Kiprakis A., Flynn D., Elizondo-González S., Wattam S. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review, Renewable and Sustainable Energy Reviews, 2020, available at: https://doi.org/10.1016/j.rser.2020.109899.Antonopoulos I., Robu V., Robu V., Couraud B., Kirli D., Norbu S., Kiprakis A., Flynn D., Elizondo-González S., Wattam S. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review, Renewable and Sustainable Energy Reviews, 2020, available at: https://doi.org/10.1016/j.rser.2020.109899.Cordeiro-Costas M., Villanueva D., Eguía-Oller P. Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques, Applied Sciences, 2021, available at: https://doi.org/10.3390/app11177991.Cordeiro-Costas M., Villanueva D., Eguía-Oller P. Optimization of the Electrical Demand of an Existing Building with Storage Management through Machine Learning Techniques, Applied Sciences, 2021, available at: https://doi.org/10.3390/app11177991.Tang Z., Xie H., Du C., Liu Y., Khalaf O., Allimuthu U. Machine Learning Assisted Energy Optimization in Smart Grid for Smart City Applications, J. Interconnect. Networks,, 2022, no. 22, pp. 2144006:1—2144006:23, available at: https://doi.org/10.1142/s0219265921440060.Tang Z., Xie H., Du C., Liu Y., Khalaf O., Allimuthu U. Machine Learning Assisted Energy Optimization in Smart Grid for Smart City Applications, J. Interconnect. Networks,, 2022, no. 22, pp. 2144006:1—2144006:23, available at: https://doi.org/10.1142/s0219265921440060.Sidorov D., Liu F., Sun Y. Machine Learning for Energy Systems, Energies, 2020, available at: https://doi.org/10.3390/en13184708.Sidorov D., Liu F., Sun Y. Machine Learning for Energy Systems, Energies, 2020, available at: https://doi.org/10.3390/en13184708.Donti P., Kolter J. Machine Learning for Sustainable Energy Systems, Annual Review of Environment and Resources, 2021, available at: https://doi.org/10.1146/annurev-environ-020220-061831.Donti P., Kolter J. Machine Learning for Sustainable Energy Systems, Annual Review of Environment and Resources, 2021, available at: https://doi.org/10.1146/annurev-environ-020220-061831.Chen C., Zuo Y., Ye W., Li X., Deng Z., Ong S. A Critical Review of Machine Learning of Energy Materials, Advanced Energy Materials, 2020, no. 10, available at: https://doi.org/10.1002/aenm.201903242.Chen C., Zuo Y., Ye W., Li X., Deng Z., Ong S. A Critical Review of Machine Learning of Energy Materials, Advanced Energy Materials, 2020, no. 10, available at: https://doi.org/10.1002/aenm.201903242.Ghoddusi H., Creamer G., Rafizadeh N. Machine lear ning in energy economics and finance: A review, Energy Economics, 2019, available at: https://doi.org/10.1016/J.ENECO.2019.05.006.Ghoddusi H., Creamer G., Rafizadeh N. Machine lear ning in energy economics and finance: A review, Energy Economics, 2019, available at: https://doi.org/10.1016/J.ENECO.2019.05.006.Gao T., Lu W. Machine learning toward advanced energy storage devices and systems, iScience, 2020, no. 24, available at: https://doi.org/10.1016/j.isci.2020.101936.Gao T., Lu W. Machine learning toward advanced energy storage devices and systems, iScience, 2020, no. 24, available at: https://doi.org/10.1016/j.isci.2020.101936.Mele E., Elias C.,& Ktena A. Machine Learning Platform for Profiling and Forecasting at Microgrid Level, Electrical, Control and Communication Engineering, 2019, no. 15, pp. 21—29, available at: https://doi.org/10.2478/ecce-2019-0004.Mele E., Elias C.,& Ktena A. Machine Learning Platform for Profiling and Forecasting at Microgrid Level, Electrical, Control and Communication Engineering, 2019, no. 15, pp. 21—29, available at: https://doi.org/10.2478/ecce-2019-0004.Rao S., Yellapragada V., Padma K., Pradeep D., Reddy C., Amir M., Refaat S. Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods, Energies, 2022, available at: https://doi.org/10.3390/en15176124.Rao S., Yellapragada V., Padma K., Pradeep D., Reddy C., Amir M., Refaat S. Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods, Energies, 2022, available at: https://doi.org/10.3390/en15176124.Fan C., Xiao F., Yan C., Liu C., Li Z., Wang J. A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning, Applied Energy, 2019, available at: https://doi.org/10.1016/J.APENERGY.2018.11.081.Fan C., Xiao F., Yan C., Liu C., Li Z., Wang J. A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning, Applied Energy, 2019, available at: https://doi.org/10.1016/J.APENERGY.2018.11.081.Mosavi A., Salimi M., Ardabili S., Rabczuk T., Shamshirband S., Várkonyi-Kóczy A. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies, 2019, available at: https://doi.org/10.3390/EN12071301.Mosavi A., Salimi M., Ardabili S., Rabczuk T., Shamshirband S., Várkonyi-Kóczy A. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies, 2019, available at: https://doi.org/10.3390/EN12071301.Chicco D., Warrens M., Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 2021, no. 7, available at: https://doi.org/10.7717/peerj-cs.623.Chicco D., Warrens M., Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 2021, no. 7, available at: https://doi.org/10.7717/peerj-cs.623.

The authors declare that there are no conflicts of interest present.