基于分数进制小波变换与支持向量机的短期风速预测  

Short-term wind speed forecasting based on rational-dilation wavelet transform and support vector machine

作　　者：王静璞[1] 王海江[2] 刘丽宏[1] 刘富军[3] 

机构地区：[1]北京师范大学减灾与应急管理研究院环境演变与自然灾害教育部重点实验室,北京100875 [2]河北工程大学信息与电气工程学院,河北邯郸056038 [3]河北工程大学,河北邯郸056038

出　　处：《水力发电学报》2014年第6期241-247,共7页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金项目(41271020);国家自然科学基金项目(41071188);国家自然科学基金项目(41373101)

摘　　要：本文探讨了一种基于分数进制小波变换与支持向量机(SVM)的短期风速预测模型。首先探讨了通过小波变换提取风速序列振荡特征提高传统模型预测精度的思路,进而分析了分数进制小波变换通过品质因子可调的变换模式实现的较传统小波变换更为自由精细的时频局部性能,以及在振荡信号特征提取领域的优越性;之后探讨了基于分数进制小波变换时频分解与SVM预测的风速预测模型的构建流程;实验结果表明,该模型与基于传统小波变换与SVM的预测模型以及神经网络、SVM模型相比,能够有效的提高预测精度。A new short-term wind speed forecasting model based on rational-dilation wavelet transforms and ,~upport vector machine （SVM） is presented. First, an idea of improving accuracy by extraction of the oscillatory features based on wavelet transforms is discussed. Then, this paper analyzes the superiority of rational-dilation wavelets to traditional wavelets in the power of time-frequency localization and oscillatory feature extraction. Last, we present a construction procedure of this forecasting model. Our experimental results show that the model has a better forecasting accuracy than all those of neural network, SVM, and the models based on traditional wavelet transforms and SVM.

关 键 词：风速预测 多步预测 分数进制小波变换 支持向量机 

分 类 号：TV11[水利工程—水文学及水资源]