Ultra-short-term wind power prediction based on CNN-BIGRU-attention

Ultra-short-term wind energy prediction refers to the prediction of wind electricity in the subsequent few minutes to a few hours. It is of great significance for the operation and management of wind farms, but due to the randomness and nonlinearity of wind speed, this task is very difficult. In this paper, an ultra-short-term wind power prediction method based on CNN- -BIGRU-Attention uses convolutional neural network (CNN) and bidirectional gated recurrent unit (BIGRU) to form a deep neural network, which can effectively extract the spatio-temporal characteristics of wind speed data and enhance the prediction ability of the model by using attention mechanism (Attention).First, the data is preprocessed to normalize the data and transform the raw data through the data preprocessing module. Secondly, the deep learning module is entered, and the data set is processed and analyzed through CNN and BIGRU to Attention layer. Finally, a wind energy prediction model for CNN-BIGRU-Attention was developed. The input of the CNN-BIGRU-Attention model is multidimensional wind speed data, including historical wind speed, real-time wind speed and future wind speed, and the output is the predicted value of wind power in the future. This paper conducts experiments on two real wind farm data sets, and the results show that the proposed method outperforms other comparative methods in ultra-short-term wind power prediction, with high accuracy and stability.