Ultra-Short-Term Wind Power Prediction Model Based on Kepler Optimization Algorithm Optimizing Gated Recurrent Unit Hyperparameter

In order to improve the accuracy of wind power prediction, an ultra-short-term wind power prediction model based on the Kepler optimization algorithm to optimize the hyperparameters of the gated recurrent unit is proposed. First, the principle of gated recurrent unit and the Kepler optimization algorithm are introduced. The ability to find the optimal hyperparameters is improved by the Kepler optimization algorithm. Then, the KOA optimized GRU wind power prediction model is constructed, which mainly includes data preprocessing, GRU hyperparameter optimization and model training. Finally, wind power generation is predicted by the optimized model. Through the data verification of actual wind farms, the results show that KOA-GRU can predict wind power more accurately than single GRU, RBF and SVM models, and has higher prediction accuracy and applicability.