南京工业大学学报（自然科学版）2011，Vol.33Issue(6)：67-71,5.DOI:10.3969/j.issn.1671-7627.2011.06.014
基于粗糙集和RBF神经网络的风电场短期风速预测模型
Short-term wind speed prediction for wind farms based on rough sets and RBF neural network model
摘要
Abstract
A radical basis function (RBF) neural network model combined with rough sets was used to predict short-term wind speed. Rough sets were used to reduce input feature space so that the significant factors for wind speed prediction could be found as the input variables of RBF neural network prediction model. Online rolling optimization was adopted in training RBF neural network. The latest sample was added into the training sets, thus the prediction model could catch recent changes of wind speed. The proposed method was used to predict wind speed in 1 h. Simulation results showed that the method had advantages of simplicity and high precision.关键词
风力发电/短期风速预测/粗糙集/RBF神经网络Key words
wind power generation/short-term wind speed prediction/rough sets/RBF neural network分类
信息技术与安全科学引用本文复制引用
王莉,王德明,张广明,周献中..基于粗糙集和RBF神经网络的风电场短期风速预测模型[J].南京工业大学学报（自然科学版）,2011,33(6):67-71,5.基金项目
江苏省科技厅工业科技支撑计划资助项目(BE2009166) （BE2009166）
