现代电力2026，Vol.43Issue(1)：1-9,9.DOI:10.19725/j.cnki.1007-2322.2023.0381
风速重构聚类的元启发双向记忆预测方法
Meta-inspired Bidirectional Memory Prediction Method for Wind Speed Reconstruction Clustering
摘要
Abstract
The accurate prediction of wind speed is important for the integration of large-scale wind power and its safe operation.The complementary ensemble empirical mode decomposition with adaptive noise is employed to decompose the wind speed sequence into several modal components,while the fast correlation filtering is utilized to optimize these modal components and reduce the dimensionality to reconstruct the sample set.The Gaussian kernel distance is utilized to measure the sample spacing,and an initial value is selected to improve the k-medoids clustering,so as to improve both the clustering accuracy and stability of the high-dimensional sample space.The meta-heuristic optimization module is embedded into the bidirectional long short-term memory network to construct the meta-heuristic bidirectional memory network.The typical set training samples are input to optimize the built-in parameters,while the typical set test samples are input to optimize the structural parameters.Finally,the wind speed prediction value is generated.The wind field in Northeast China is taken as the research object,and the accuracy and generalization ability of the prediction model are verified.关键词
风速预测/模态分解重构/改进K-medoids聚类/元启发双向记忆网络Key words
wind speed prediction/mode decomposition reconstruction/improved K-medoids clustering/meta-inspired bidirectional memory network分类
信息技术与安全科学引用本文复制引用
史晓航,潘超,王超,李载源..风速重构聚类的元启发双向记忆预测方法[J].现代电力,2026,43(1):1-9,9.基金项目
国家重点研发计划项目(2022YFB2404000).National Key R&D Program of China(2022YFB2404000). （2022YFB2404000）
