The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Center for Information Analytical and Technical Support of Nuclear Power Facilities Monitoring of the National Academy of Sciences of Ukraine Akademika Palladina Avenue 34 A Kyiv Ukraine

Department of Electrical and Electronics Engineering Vignans Foundation for Research Science and Technology Guntur Andhra Pradesh 522213 India

Department of Electrical Engineering Graphic Era Dehradun India

Department of Electrical Engineering School of Physics and Electronic Engineering Hanjiang Normal University Shiyan 442000 Hubei People's Republic of China

Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment Institute of Electrodynamics National Academy of Sciences of Ukraine Peremogy 56 Kyiv 57 03680 Ukraine

Graphic Era Hill University Dehradun 248002 India

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

MIT World Peace University Pune Maharashtra India

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