Short-term wind power prediction model based on improved ACA-GRU neural network

Abstract

To improve the accuracy of wind power forecasting, the improved ACA (Ant Colony Algorithm) is used to optimize the GRU (Gated Recurrent Unit) model. First, the original power generation data is normalized; Second, the GRU neural network model is established, and the ant colony algorithm is used to optimize it; Finally, the optimized GRU model and the non-optimized model are used to predict the short-term wind power output, and the prediction results are compared to verify that the improved ACA-GRU prediction model has higher prediction accuracy for short-term wind power output.