|Table of Contents|

College building energy consumption prediction based on GM-RBF neural network

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年01期
Page:
48-53
Research Field:
Publishing date:

Info

Title:
College building energy consumption prediction based on GM-RBF neural network
Author(s):
Zhao ChaoLin SimingXu Qiaoling
Chemistry and Chemical Engineering,Fuzhou University,Fuzhou 350108,China
Keywords:
college buildings energy consumption prediction grey theory radical basis function neural network combined models
PACS:
TU831
DOI:
-
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.

References:

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Memo

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Last Update: 2014-02-28