基于CBAM-DSC-UNet模型的时空风速预测算法  

SPATIO-TEMPORAL WIND SPEED PREDICTION ALGORITHM BASED ON CBAM-DSC-UNet MODEL

作　　者：赵陆阳 刘长良[1,2] 刘卫亮 李洋 王昕[3] 康佳垚 Zhao Luyang;Liu Changliang;Liu Weiliang;Li Yang;Wang Xin;Kang Jiayao(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System,Baoding 071003,China;National Energy Group New Energy Technology Research Institute Co.,Ltd.,Beijing 102209,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]保定市综合能源系统状态检测与优化调控重点实验室,保定071000 [3]国家能源集团新能源技术研究院有限公司,北京102209

出　　处：《太阳能学报》2024年第10期497-505,共9页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(62203172);中央高校基本科研业务费(2023JG005,2020JG006,2020MS117)。

摘　　要：针对时空风速预测任务通常使用的卷积神经网络(CNN)和循环神经网络(RNN)联合建模方法中空间信息损失的问题,提出一种基于CBAM-DSC-UNet模型的时空风速预测算法,用于提升空间信息利用率与模型预测精度。该算法将时空风速预测问题视为视频预测问题,在提取时空相关性的同时保持空间信息,进而直接输出未来多步的空间风速矩阵。以美国怀俄明州某风电场实际数据为算例进行实验,结果表明,相比其他对比算法,基于CBAM-DSC-UNet模型的时空风速预测算法的平均绝对误差下降8.4%~15.9%,精度有较大提升。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.

关 键 词：风力预测 卷积神经网络 时空数据 UNet 多风电机组 

分 类 号：TM614[电气工程—电力系统及自动化]