Leveraging Meteorological Big Data for Optimized Modeling and Forecasting of Renewable Energy Sources in Smart Grids

On the basis of the continuous development of the global renewable energy field, smart grid optimization has become one of the inevitable requirements. Therefore, this paper proposes a reliable modeling and prediction method based on meteorological big data, and optimizes it in terms of comprehensive meteorological conditions and renewable energy power generation to make the smart grid model more reliable and practical. The results show that the model improves the utilization rate of renewable energy, the smart grid is reduced from 89.35% to 5.59%, which greatly saves the cost of reduction, and its atmospheric emissions are also reduced from 28.78 to 150.56, and The optimization of the overall power grid is reduced from 8.26% to 5.59%, significantly reducing the duration of power outages and greatly reducing the intensity of carbon emissions. The above results show that this method can improve energy efficiency, enhance grid stability, and improve the power generation efficiency of renewable energy.