基于支持向量机的短期风速预测研究综述  

A Review of Short-term Wind Speed Prediction Based on Support Vector Machine

作　　者：杨茂[1] 陈新鑫[1] 张强[1] 李大勇[2] 孙涌[3] 贾云彭 

机构地区：[1]东北电力大学电气工程学院,吉林吉林132012 [2]国网吉林省电力有限公司通化供电公司,吉林通化130022 [3]国网淄博供电公司,山东淄博25500 [4]国网吉林供电公司客户服务中心计量室,吉林吉林132012

出　　处：《东北电力大学学报》2017年第4期1-7,共7页Journal of Northeast Electric Power University

基　　金：国家重点基础研究发展计划项目(973计划)(2013CB228201);吉林省产业技术研究与开发项目(2014Y124)

摘　　要：介绍了支持向量机(SVM)的理论基础,从两个方面归纳了目前国内外支持向量机算法在短期风速预测应用中的现状。首先,介绍了确定性支持向量机的预测方法:使用数据挖掘算法结合支持向量机的预测模型和对核函数参数改进优化的支持向量机预测模型。其次,介绍了结合模糊、粗糙和未确知等不确定性支持向量机的预测方法。对现有支持向量机在短期风速预测的应用现状介绍后,分析了SVM核函数的选取和参数优化对预测精度影响的问题,最后展望了基于支持向量机的短期风速预测研究的前景。This paper introduces the theoretical basis of support vector machine （ SVM）, and summarizes the present situation of support vector machine （SVM） algorithm in short-term wind speed forecasting. Firstly, the forecasting method of deterministic support vector machine is introduced. Using the data mining algorithm com- bined with the support vector machine prediction model and an improved support vector machine prediction model for parametric optimization of kernel functions. Secondly, the forecasting method of support vector ma- chine with fuzzy, rough and unascertained is introduced. After the introduction of the existing support vector machine （SVM） in short-term wind speed prediction, the selection of SVM kernel function and the influence of parameter optimization on prediction accuracy are analyzed. Finally, the prospect of short-term wind speed pre- diction based on SVM is prospected.

关 键 词：短期风速预测 支持向量机 确定性 核函数 参数优化 

分 类 号：TM614[电气工程—电力系统及自动化]