基于特征选择的RELM风速短期预测  

Short-term wind speed forecasting based on RELM of feature selection

作　　者：王琦[1] 秦本双 

机构地区：[1]中国电力工程顾问集团东北电力设计院有限公司,吉林长春130021 [2]东北电力大学电气工程学院,吉林吉林132012

出　　处：《可再生能源》2017年第8期1215-1220,共6页Renewable Energy Resources

基　　金：国家高技术研究发展"863"计划资助项目(SS2014AA052502);国家自然科学基金资助项目(51507027)

摘　　要：准确的风速预测是风电场功率预测的基础,对大规模风电并网具有重要的价值。文章提出一种基于信息增益(IG)的正则化极限学习机(RELM)短期风速预测方法。首先采用信息增益对32维风速属性序列进行特征选择,并对其进行加权;然后将正则化系数引入极限学习机(ELM)网络,构建RELM风速预测模型;最后结合美国风能技术中心的实测数据进行仿真,与传统ELM网络、BP神经网络相比,该方法具有较高的准确性和预测精度。Accurate wind speed forecasting is the basis of wind power forecasting,which is of great value for large-scale wind power integration. A method of short-term wind speed forecasting based on the regularized learning machine（RELM） of information gain is put forward in this paper. Firstly,the32 dimensional wind speed attributes series are selected by using information gain and weighted. Then the regularization coefficient is introduced into the ELM network,and the wind speed forecasting model is established. Finally,combined with the measured data of the wind energy technology center,compared with traditional ELM neural network and BP neural network,the new method has higher accuracy and precision.

关 键 词：风速预测 信息增益 皮尔逊系数 正则化 极限学习机 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程]