作　　者： ; ; ; ; (陈天恩）;

机构地区： 华南理工大学电力学院

出　　处： 《电网技术》 2011年第5期177-182,共6页

摘　　要： 运用小波包变换和支持向量回归相结合的方法对提前1～6h的每10min风速预测进行研究。首先针对风速非平稳、非线性的特点,利用小波包变换将原始风速序列分解成一系列不同变动频率的子序列,再分别对这些子序列用支持向量回归法进行预测,最后将各自输出结果叠加得到最终的预测风速。选择某风电场2组具有不同特点的实测数据作为应用案例,结果表明,通过小波包变换更能把握风速变化规律,支持向量回归法具备较强的学习能力,小波包支持向量回归法优于现有的一些预测方法。 By use of the combination of wavelet package transform with support vector regression （SVR）, the one hour to six hour-ahead wind speed forecasting of each ten minute is researched. Firstly, in view of the non-stationary and nonlinear features of wind speed, the original wind speed sequence is decomposed into a series of sub-sequences; then these sub-sequences are respectively forecasted by SVR; finally, respective outputs are superposed to obtain final forecasted wind speed. Taking two sets of measured data possessing different characteristics in a certain wind farm as application cases, the application results show that by use of wavelet package transform the variation of wind speed can be grasped better; due to the better learning ability of SVR, the combination of wavelet package transform with SVR is superior to existing wind speed forecasting methods.

关 键 词： 小波包变换 支持向量回归 风速预测

领　　域： [电气工程]