南京理工大学学报（自然科学版）Issue(1)：48-53,6.
基于GM-RBF神经网络的高校建筑能耗预测
College building energy consumption prediction based on GM-RBF neural network
摘要
Abstract
To improve the accuracy of the forecasting of the college building energy consumption,this pape puts forward an estimating method of the building energy consumption according to the grey theory and radical basis function neural network ( RBFNN ) . The proposed model combines the advantages of low data demand of grey theory with the self-learning and self-organization of RBFNN. Case study indicates that compared with those of the traditional grey theory and RBFNN models,the average relative deviation between predicted and the real value can decrease 5 . 4% based on the proposed model.关键词
高校建筑/能耗预测/灰色理论/径向基函数神经网络/组合模型Key words
college buildings/energy consumption prediction/grey theory/radical basis function neural network/combined models分类
建筑与水利引用本文复制引用
赵超,林思铭,许巧玲..基于GM-RBF神经网络的高校建筑能耗预测[J].南京理工大学学报（自然科学版）,2014,(1):48-53,6.基金项目
国家自然科学基金(6080402) （6080402）
福州大学科研基金(FZU-022335,600338,600567) （FZU-022335,600338,600567）
高校博士点专项科研基金(20133314120004) （20133314120004）
