| 基于EMD与LS-SVM的风电场短期风速预测 |
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| 引用本文： | 王晓兰,李辉. 基于EMD与LS-SVM的风电场短期风速预测[J]. 计算机工程与设计, 2010, 31(10) |
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| 作者姓名： | 王晓兰 李辉 |
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| 作者单位： | 兰州理工大学,电气工程与信息工程学院,甘肃,兰州,730050 |
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| 摘 要： | 为了提高风电场风速短期预测的精度,提出了将经验模式分解与数据挖掘方法相结合对风速时间序列进行建模预测.对风速时间序列进行经验模式分解,使之分解为若干不同频带的本征模式分量.对不同频带的平稳分量建立相应的最小二乘支持向量机预测模型,将各模型的预测值等权求和得到最终预测值.仿真实验结果表明,风电场短期风速预测的MAPE为1.507%,提高了此类预测的精度,表明了该方法的有效性.
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| 关 键 词： | 风速时间序列 数据挖掘 经验模式分解 本征模式分量 最小二乘支持向量机 |
Short-term wind speed forecasting of wind farm based on EMD and LS-SVM |
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| WANG Xiao-lan,LI Hui. Short-term wind speed forecasting of wind farm based on EMD and LS-SVM[J]. Computer Engineering and Design, 2010, 31(10) |
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| Authors: | WANG Xiao-lan LI Hui |
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| Affiliation: | WANG Xiao-lan,LI Hui(College of Electrical Engineering , Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China) |
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| Abstract: | In order to improve the forecast precision,a forecasting method based on empirical mode decomposition(EMD) and data mining method is proposed.The wind speed time series is decomposed into several intrinsic mode functions(IMF) and the trend term.The different least square support vector machine(LS-SVM) models to forecast each IMF are built up.These forecasting results of each IMF are combined to obtain the final forecasting result.The simulation experiment shows the value of the MAPE is 1.507% about wind spe... |
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| Keywords: | wind speed time series data mining empirical mode decomposition(EMD) intrinsic mode function(IMF) least square support vector machine(LS-SVM) |
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