综合智慧能源2025，Vol.47Issue(6)：1-10,10.DOI:10.3969/j.issn.2097-0706.2025.06.001
基于多尺度卷积-残差网络的短期风电预测
Multiscale convolution-residual network for short-term wind power forecasting
摘要
Abstract
Short-term wind power forecast can provide a basis for power system scheduling and prevent power systems from severe impacts of wind power fluctuations.To improve the low prediction accuracy of existing deep learning models applied for short-term wind power prediction,a short-term wind power prediction method based on multiscale convolution-residual network is proposed to optimize the models.The proposed multiscale convolution-residual network is characterized by full range of feature extraction scales and strong stability,and the multiscale convolution part taking layers with convolutional kernel sizes of 3×3,5×5,7×7 and 9×9 is used to extracted detailed information and global information from the input data.By introducing hopping connections to the residual block,the vanishing gradient problem in the convolutional neural network is solved.The results of the simulation applying on the Natal 378-day dataset show that,the multiscale convolution-residual network can make an accurate prediction on wind power for the next 24 h,and the mean square error of the proposed network is more than 43.55%smaller than that of DarkNet19,InceptionResNetV2,InceptionV3,ResNet18,ResNet50,ShuffleNet and Xception.关键词
多尺度卷积/残差/深度学习/优化模型结构/短期风电预测Key words
multiscale convolution/residual/deep learning/model optimization/short-term wind power forecast分类
能源科技引用本文复制引用
殷林飞,仝博文,李雯吉..基于多尺度卷积-残差网络的短期风电预测[J].综合智慧能源,2025,47(6):1-10,10.基金项目
国家自然科学基金项目(52107081) （52107081）
广西自然科学基金项目(AA22068071) National Natural Science Foundation of China(52107081) （AA22068071）
Guangxi Natural Science Foundation(AA22068071) （AA22068071）
