Multi-step Ultra-short-term Wind Power Prediction Based on CNN-GRU-Attention
Aiming at the problem of information loss and low accuracy in the current prediction model,a prediction model based on GRU network is proposed.Firstly,the CNN-GRU-Attention prediction model structure is established.The model has the advantage of high prediction accuracy and solves the problem of sequence information loss in the prediction process.The GRU model,which is good at processing time series data and solves the problem of long-term dependence of neural network,is proposed to introduce CNN network and Attention layer to deeply mine and process the input data and the intermediate data of model network respectively.In order to improve the stability and accuracy of the prediction model during training,the Adam optimizer is used to optimize the model parameters.Finally,the historical operation data of a wind farm is predicted.The results show that the wind power prediction model based on CNN-GRU-Attention further improves the prediction accuracy and verifies the applicability of this model.
Wind power predictiondeep learninggated recurrent unitattention mechanism
万方数据
维普
