Forecast of Wind Power Generation Based on AM of CNN-BiGRU
Wind energy is highly valued as clean energy in the field of new energy.However,the unstable performance of wind energy limits its application and development.Predicting its work performance in the next stage can effectively improve the energy utilization rate and reduce the difficulty of system ma-intenance.Based on historical time series data,wind speeds and blade angle data of wind farms,a fusion model CNN BiGRU AM for short-term performance prediction of wind farms is proposed.This model adds an AM that combines convolutional neural network and bidirectionally gated recursive unit.First, the spatial characteristics of the data are extracted by CNN,then the acquired characteristics are trans-ferred to BiGRU to extract the temporal characteristics,and the model is established by training parame-ters.Finally,the most important spatiotemporal properties of the time series are captured by the AM mechanism,the wind speed data of the numerical weather forecast is entered and the final forecast results are output via the model.The final prediction results are evaluated by using two parameters:mean abso-lute error (MAE)and mean square error (RMSE),which are further compared with the prediction re-sults of several classical models,fusion models without AM mechanism and fusion models with AM mechanism at different positions.The results show that the model has a higher accuracy and stability.