Wind Power Prediction Based on Gray Wolf Algorithm and Long Short-Term Memory Neural Networks

The prediction of wind power generation is of great significance for improving the reliability of power system and promoting the development of renewable energy. In order to improve the accuracy of wind forecasting, a wind forecasting model based on the Grey Wolf optimisation algorithm has been developed to optimise long-term and short-term memory networks. Firstly, the principle of long short-term memory network and grey wolf optimization algorithm is introduced. Then, a GWO optimized LSTM wind power prediction model is established, which mainly includes data preprocessing, LSTM hyperparameter optimization and model training. Finally, the LSTM model with the best hyperparameters is used to predict the wind power generation. The case analysis results show that compared with single LSTM, BP, RBF and SVM methods, the GWO-LSTM can more accurately match future trends in wind power generation, improving the accuracy of wind power forecasting.