Optimization of wind power generation prediction model based on machine learning

This research aims to optimize a wind power generation prediction model based on machine learning to improve its accuracy and efficiency. Firstly, a variety of machine learning technologies, such as gated cycle unit GRU, artificial neural network (ANN), RMSE and other models, were used to model and predict wind power generation data. By mixing the predictions of these models, more accurate and robust predictions can be obtained. Second, the study combines statistical models such as time series analysis and regression models to capture trends and cyclical changes in wind power data. These statistical models can provide forecasts of long-term and short-term wind power generation trends, thereby enhancing the reliability of the overall forecast model. In addition, intelligent optimization algorithms, such as genetic algorithm and particle swarm optimization algorithm, are used to optimize the model parameters to further improve the prediction accuracy. Through the comprehensive use of machine learning, hybrid prediction, statistical model and intelligent optimization algorithm, this study is committed to creating an efficient and accurate wind power generation prediction model, providing a reliable forecasting tool for the wind power industry, and promoting the development and utilization of clean energy.