摘要
基于相空间重构技术和局域预测法,提出一种风电场超短期风速预测的新方法。该方法通过优化的相空间邻域寻找预测状态点在相空间中的邻域点,并建立支持向量回归(SVR)模型。通过考察伪近邻点的比重来选取合适的邻域半径,保证了邻域点与预测状态点的高度相似性,而SVR模型则具有很强的高维非线性拟合能力。实例分析表明,该方法与其他方法相比具有较好的超短期风速预测效果。
A new approach to ultra-short-term wind speed forecasting is presented.It is based on the phase space reconstruction technique and the local prediction method,to search the neighbors in the optimal neighborhood in the phase space and to build support vector regression(SVR) models.This approach finds the optimum neighborhood by considering of proportion of false neighbors,which guarantees the high similarity between neighbors and prediction state points,and the SVR model has a good capability of nonlinear fitness.The results have demonstrated the accuracy of the proposed method in comparison with that by other prediction methods.
出处
《电力系统自动化》
EI
CSCD
北大核心
2011年第24期39-43,58,共5页
Automation of Electric Power Systems
基金
教育部新世纪优秀人才支持计划资助项目(NCET-07-0745)
浙江省自然科学基金资助项目(R107062)~~
关键词
风力发电
超短期风速预测
局域预测法
支持向量回归(SVR)
相空间重构
伪近邻点
wind power
ultra-short-term wind speed forecasting
local prediction
support vector regression(SVR)
phase space reconstruction
pseudo neighbor point
