标题:Research on Wind Power Climbing Output Power Prediction System Based on ISMC-PSO
作者:Wang, Hong; Wang, Zhjie; Huang, Qiyuan; Sun, Xia
通讯作者:Wang, ZJ
作者机构:[Wang, Hong; Wang, Zhjie; Huang, Qiyuan] Shanghai Dianji Univ, Dept Elect Engn, Shanghai, Peoples R China.; [Wang, Hong] Tongji Univ, Sch Econ & Man 更多
会议名称:IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
会议日期:MAY 24-26, 2019
来源:PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019)
出版年:2019
页码:1777-1781
DOI:10.1109/ITAIC.2019.8785452
关键词:Improved particle swarm optimization; Weight optimization; wind power; climbing; Combined prediction model
摘要:At present, wind farms all use a single power climbing prediction model, which has poor generalization ability and low prediction accuracy. In order to solve this problem this paper analyzes the support vector machine (SVM) and extreme learning machine two grade prediction model of the single power, through the weight for these two model selection, the establishment of a large wind power grade combination forecast model, the improved particle swarm optimization (PSO) algorithm was applied to combination the weight of two single prediction model in the prediction model of optimization, the weighting parameters were optimized by combining the advantages of two single prediction model, further improve the prediction precision of the power of climbing. Then the system is modeled and simulated, and the simulation results verify the validity of the prediction model.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071096990&doi=10.1109%2fITAIC.2019.8785452&partnerID=40&md5=ff8c8e248a399df1e09de2b6b257e6f3
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