首页 | 本学科首页   官方微博 | 高级检索  
     

基于最小二乘支持向量机的风电场短期风速预测
引用本文:杜 颖,卢继平,李 青,邓颖玲.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15):61-66.
作者姓名:杜 颖  卢继平  李 青  邓颖玲
作者单位:输配电装备及系统安全与新技术国家重点实验室(重庆大学),甘肃洁源风电有限责任公司,中国民用航空湛江空中交通管理站
基金项目:国家重点基础研究发展计划(973计划)
摘    要:提出了一种基于最小二乘支持向量机的风电场风速预测方法。以历史风速数据、气压、温度作为输入,对风速和环境条件进行训练,建立预测模型,并且运用网格搜索法确定模型参数。算例结果表明,使用上述方法预测的风速与真实值基本一致。将本文提出方法与BP(back propagation)神经网络法的预测结果进行对比,表明前者具有更高的精度和更强的鲁棒性,因此是一种比较有价值的风速预测方法。

关 键 词:风力发电  风速预测  最小二乘支持向量机(LS-  SVM)  网格搜索  BP神经网络
收稿时间:2007-10-16

Short-Term Wind Speed Forecasting of Wind Farm Based on Least Square-Support Vector Machine
DU Ying,LU Ji-ping,LI Qing,DENG Ying-ling.Short-Term Wind Speed Forecasting of Wind Farm Based on Least Square-Support Vector Machine[J].Power System Technology,2008,32(15):61-66.
Authors:DU Ying  LU Ji-ping  LI Qing  DENG Ying-ling
Affiliation:1.State Key Laboratory of Power Transmission Equipment &; System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400044,China;2.Gansu Clean Source of Wind Power Co., Ltd., Lanzhou 730050,Gansu Province,China;3.China CIVIL Zhanjiang Air Traffic Management Station, Zhanjiang 524017,Guangdong Province,China
Abstract:A wind speed forecasting for wind farm based on least squares support vector machine (LS-SVM) is proposed. Taking historical wind speed data, atmospheric pressure and temperature as the input, the wind speed and environmental condition are trained by LS-SVM, then a forecasting model is built, and by use of grid search the parameters of the model are determined. Forecasting wind speed by the proposed method, the obtained results are basically in accordance with the values of actual wind speed. Comparing the wind speeds forecasted by the proposed method with those forecasted by BP neural network based method, it is shown that the proposed method is better than the latter in robustness and forecasting accuracy.
Keywords:wind power generation  wind speed forecasting  least squares support vector machine (LS-SVM)  grid search  BP neural network
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《电网技术》浏览原始摘要信息
点击此处可从《电网技术》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号