SPATIO-TEMPORAL WIND SPEED PREDICTION ALGORITHM BASED ON CBAM-DSC-UNet MODEL
In response to the problem of spatial information loss in the joint modeling methods of convolutional neural networks(CNN)and recurrent neural networks(RNN)commonly used for spatial-temporal wind speed prediction tasks,we propose a spatial-temporal wind speed prediction algorithm based on the CBAM-DSC-UNet model.This algorithm aims to enhance the utilization of spatial information and improve the accuracy of model predictions.We treat the spatial-temporal wind speed prediction problem as a video prediction problem in order to preserve spatial information while extracting spatial-temporal correlations,thereby directly outputting the spatial wind speed matrix for multiple future steps.We conducted a calculating using actual data from a wind farm in Wyoming,USA as a case study.The results show that to other algorithms,the average absolute error of the spatial-temporal wind speed prediction algorithm based on the CBAM-DSC-UNet model reduces by 8.4%to 15.9%,demonstrating a significant improvement in prediction accuracy.
wind forecastingconvolutional neural networksspatial-temporal dataUNetmulti-wind turbine units
万方数据
维普
