Research on Ultra-Short- Term Wind Power Generation Forecasting Based on Expert System Method and RBF Neural Network Prediction Model

To achieve the “dual carbon” goals, China is actively promoting the use of clean energy, among which wind power generation has been rapidly applied due to its unique advantages. However, wind power generation is greatly affected by weather conditions, posing challenges to the stable power supply of the power grid. Therefore, accurate forecasting of wind power generation is extremely important. This paper uses an expert system method combined with an RBF neural network model to construct an optimized composite forecasting model to improve forecasting accuracy. Through this model, wind power generation can be accurately predicted, thus ensuring the stable operation of the power grid and improving the utilization rate of wind energy resources. The case study results show that the forecasting model is significantly effective in predicting wind power generation and helps promote the transformation to green energy and the stable operation of the power system.