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

作　　者：赵超[1] 林思铭[1] 许巧玲[1] 

机构地区：[1]福州大学化学化工学院,福建福州350108

出　　处：《南京理工大学学报》2014年第1期48-53,共6页Journal of Nanjing University of Science and Technology

基　　金：国家自然科学基金(6080402);福州大学科研基金(FZU-022335;600338;600567);高校博士点专项科研基金(20133314120004)

摘　　要：为了提高高校建筑的能耗预测精度,在比较传统灰色预测模型和神经网络预测模型优缺点的基础上,建立了灰色径向基函数(Radical basis function,RBF)神经网络能耗预测算法。该方法综合了灰色系统理论所需数据少以及神经网络自学习和自组织的优点。实例分析表明:与传统灰色理论和RBF神经网络预测模型相比较,组合模型预测值与实际值的相对误差平均降低了5.4%,为建筑节能评估和设计提供了决策依据。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 

分 类 号：TU831[建筑科学—供热、供燃气、通风及空调工程]