基于SVM方法的风电场短期风速预测  

Short-Term Wind Speed Forecasting of Wind Farm Based on SVM Method

作　　者：彭怀午[1] 杨晓峰[1] 刘方锐[2] 

机构地区：[1]内蒙古电力勘测设计院,呼和浩特010020 [2]华中科技大学电气与电子工程学院,武汉430074

出　　处：《电网与清洁能源》2009年第7期48-52,共5页Power System and Clean Energy

基　　金：国家自然科学基金项目(50877032);国家自然科学基金重点项目(50837003)

摘　　要：针对基于支持向量机的风电场短期风速预测进行研究,选择了不同的输入向量(历史风速时间序列,历史风速和温度,历史风速、温度和风向,历史风速、温度和时间)作为输入进行误差对比分析。实测数据及分析结果表明,采用历史风度和温度的二输入模型,预测效果最佳,为风速的短期预测和发电量预测提供了较好的参考价值。In this paper, support vector machine （SVM） method is employed for the short term wind speed forcasting of wind farm. Various input vectors of SVM were generated and compared through error measures to guarantee the performance and accuracy of the chosen models. First a model with only historical wind speed data was chosen according to the traditional way. Nevertheless, the results were not sufficiently satisfactory. Therefore, three models, consisting of historical wind speed data and temperature, historical wind speed data, temperature and wind direction, historical wind speed data, temperature and time, were developed. The simplest model of two inputs with wind speed data and temperature, was the optimal for the short term wind speed forecasting. The developed model provides an alternative for short term wind speed forecasting with high pricision.

关 键 词：风电场 短期风速预测 支持向量机(SVM) 

分 类 号：TK89[动力工程及工程热物理—流体机械及工程]