基于小波包变换的最小二乘支持向量机短期风速多步预测和信息粒化预测的研究  

WAVELET PACKET TRANSFORM AND THE LEAST SQUARES SUPPORT VECTOR MACHINE IN RESEARCH OF SHORT-TERM WIND SPEED MULTISTEP PREDICTION AND INFORMATION GRANULATION PREDICTION

作　　者：柳玉[1] 曾德良[2] 刘吉臻[2] 白恺[1] 宋鹏[1] 

机构地区：[1]华北电力科学研究院有限责任公司,北京100045 [2]华北电力大学新能源电力国家重点实验室,北京102206

出　　处：《太阳能学报》2014年第2期214-220,共7页Acta Energiae Solaris Sinica

基　　金：国家重点基础研究发展(973)计划(2012CB215203);国家自然科学基金(51036002)

摘　　要：讨论风电场短期风速多步预测和信息粒化预测,建模方法采用最小二乘支持向量机回归算法,数据处理方法采用小波包变换算法,在小波变换的基础上有选择地分解高频部分,可进一步提升预测精度。最后,将该文所提的建模方法应用于短期风速多步预测和信息粒化预测。大量实例分析表明,多步预测方法可得到风速预测曲线,适用于含风电场的区域能源连续调度;信息粒化方法可处理冗余数据并得到较准确的风速预测的特征数据,能准确分析不同风电场或不同风电机组的机组特性。The multistep prediction and information granulation prediction in the wind speed prediction were discussed. First, the least squares support vector machine was used for modeling method. Then, wavelet packet transform was used for data processing method. On the basis of wavelet decomposition, high frequency part was analyzed and the prediction accuracy was improved with the method. Last, the modeling method was used for multistep prediction and information granulation prediction. Many cases showed that multistep prediction is able to get the wind speed curve, which applies to regional energy scheduling including wind farms. Information granulation prediction can deal with redundant data and get more accurate characteristic value of wind speed, which applies to analyze the unit characteristics of different wind farms or different units.

关 键 词：风速预测 最小二乘支持向量机 小波包变换 多步预测 信息粒化 

分 类 号：TK8[动力工程及工程热物理—流体机械及工程]