基于优化近邻传播聚类的CMN风速预测  

Wind Speed Forecasting of CMN Based on Optimized Affinity Propagation Clustering

作　　者：韩绍禹 徐鹏程 蒋迪遥 潘超[2] 李润宇 HAN Shaoyu;XU Pengcheng;JIANG Diyao;PAN Chao;LI Runyu(Siping Power Supply Company,State Grid Jilin Electric Power Company,Siping 136000,Jilin,China;Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education(Northeast Electric Power University),Jilin 132012,Jilin,China)

机构地区：[1]国网吉林省电力有限公司四平供电公司,吉林四平136000 [2]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林吉林132012

出　　处：《电网与清洁能源》2022年第8期110-120,共11页Power System and Clean Energy

基　　金：国家重点研发计划重点专项资助项目(2016YFB0900104)。

摘　　要：提出一种基于快速相关性约简和近邻传播聚类的卷积记忆网络短期风速预测模型。计算各风速序列及其属性序列的相关程度信息熵,运用快速相关性滤波算法进行属性约简,以降低属性维度及删除冗余属性;针对风速属性矩阵样本,采用压缩-激励模块(squeeze-and-excitation networks,SENet)构建属性表征序列,以该序列间距为样本相似度,利用近邻传播聚类实现样本集优选重构;构建卷积记忆网络,利用其挖掘深层特征及短期预测。通过对实际风场风速进行预测,对比实测数据,结果表明,该方法在风速属性数据的优选方面具有较大优势,通过保留关联紧密的属性信息,提高了预测精度。This paper proposes a short-term wind speed prediction model based on fast correlation reduction based on convolutional memory network. Firstly,the information entropy of the correlation degree of each wind speed series and its attribute series is calculated,and the fast correlation filtering algorithm is used for attribute reduction to reduce the attribute dimension and delete redundant attributes. For wind speed attribute matrix samples,Squeeze-and-Excitation Networks are used to construct attribute representation sequences. The sequence spacing is used as sample similarity to realize sample set optimization and reconstruction by Affinity Propagation Clustering. Secondly,a convolutional memory network is built and used to mine deep features and short-term predictions. Finally,by predicting the wind speed of the actual wind field and comparing the measured data,the accuracy and effectiveness of the prediction model are verified. The results show that the method proposed in this paper has a great advantage in the optimization of wind speed attribute data. By retaining closely related attribute information,the prediction accuracy is improved.

关 键 词：风速 短期预测 快速相关性滤波 近邻传播聚类 卷积记忆网络 

分 类 号：TM721[电气工程—电力系统及自动化]