R Singh Arvind, Kumar R Seshu, Bajaj Mohit, Khadse Chetan B, Zaitsev Ievgen
Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, 442000, Hubei, People's Republic of China.
Department of Electrical and Electronics Engineering, Vignans Foundation for Research Science and Technology (Deemed to be University), Guntur, Andhra Pradesh, 522213, India.
Sci Rep. 2024 Aug 19;14(1):19207. doi: 10.1038/s41598-024-70336-3.
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.
可再生能源越来越多地融入并网微电网，这在发电预测和能源管理方面带来了新的挑战。本文探讨了使用先进的机器学习算法，特别是支持向量回归（SVR），来提高这些系统的效率和可靠性。所提出的SVR算法利用全面的历史能源生产数据、详细的天气模式和动态电网条件来准确预测发电。与传统线性回归模型相比，我们的模型显示出显著更低的误差指标，太阳能光伏发电预测的均方误差为2.002，风力发电预测的均方误差为3.059。太阳能光伏发电的平均绝对误差降至0.547，风力发电场景的平均绝对误差降至0.825，太阳能光伏发电的均方根误差（RMSE）为1.415，风力发电的均方根误差为1.749，展示了该模型的卓越准确性。提高预测准确性直接有助于优化资源分配，能够更精确地控制能源发电计划，并减少对外部电源的依赖。我们的SVR模型的应用使总体运营成本降低了8.4%，突出了其在提高能源管理效率方面的有效性。此外，该系统预测能源输出波动的能力实现了自适应实时能源管理，减轻了电网压力并增强了系统稳定性。这种方法使供需平衡提高了10%，峰值负荷需求降低了15%，可再生能源利用率提高了12%。我们的方法通过更好地平衡供需、减轻可再生能源的可变性和间歇性来增强电网稳定性。这些进展促进了可再生能源更可持续地融入微电网，有助于建立更清洁、更具弹性和高效的能源基础设施。本研究结果为开发能够适应不断变化的条件的智能能源系统提供了有价值的见解，为未来能源管理创新铺平了道路。此外，这项工作强调了机器学习通过为将可再生能源集成到现有电网基础设施中提供更准确、可靠和具有成本效益的解决方案来彻底改变能源管理实践的潜力。
