A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
Hai‐Kun Wang, Ke Song, Yi Cheng
Abstract
Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.