电力系统自动化2012，Vol.36Issue(5)：24-28,5.
基于经验模式分解和混沌相空间重构的风电功率短期预测
Wind Power Short-term Forecasting Based on Empirical Mode Decomposition and Chaotic Phase Space Reconstruction
摘要
Abstract
It is very important to forecast short-term wind farm output for the security and stability of the power grid.Due to its non-steady and non-periodic characteristic,the wind power time series is decomposed into the random component and trend component by using the empirical mode decomposition（EMD） theory.Chaotic prediction is made for the random components and trend components using neural network with radical basis function and using least squares support vector machine,respectively,thus the final consequence can be obtained by combining the prediction result of each component.The power output of a wind farm in Yunnan is used as the case study for the model proposed.The outcome shows that the prediction model has high accuracy compared with the traditional artificial neural prediction model and provides a reference for the wind power forecasting.关键词
风力发电/功率预测/经验模式分解/相空间重构/最小二乘支持向量机/径向基函数Key words
wind power generation/power prediction/empirical mode decomposition（EMD）/phase space reconstruction/least squares support vector machine/radial basis function分类
信息技术与安全科学引用本文复制引用
张宜阳,卢继平,孟洋洋,严欢,李辉..基于经验模式分解和混沌相空间重构的风电功率短期预测[J].电力系统自动化,2012,36(5):24-28,5.基金项目
重庆市科委科技计划攻关项目 （）
输配电装备及系统安全与新技术国家重点实验室自主研究项目（2007DA10512710101）~~ （）
