DocumentCode :
2936152
Title :
Building Energy Conservation Technology Project Appraisal Based on General Regression Neural Network
Author :
Zhang, Bowen ; Wang, Aifeng
Author_Institution :
Econ. & Manage. Sch., Hebei Univ. of Eng., Handan, China
Volume :
4
fYear :
2009
fDate :
26-27 Dec. 2009
Firstpage :
234
Lastpage :
237
Abstract :
As is strongly promoted by the State important strategy of ¿ energy conservation and emission reduction¿, the development of energy conservation technology of construction has been deepened in a comprehensive manner. Building energy conservation technology project appraisal is a system engineering involved many factors, single-index appraisal can´ t enforce the energy conservation project diffused more effectively. The main purpose of this study is to devise a general regression neural network (GRNN) based building energy conservation technology projection appraisal model, for this purpose 12 indicators such as FNPV and conditions system were chosen. Written software programs with MATLAB7.0 neural network toolbox to appraise building energy conservation technology project, trained and tested some Conservation system samples, get small error. This study shows the model have a higher accuracy to appraise building energy conservation technology projection.
Keywords :
construction industry; energy conservation; neural nets; pollution control; structural engineering computing; MATLAB7.0 neural network toolbox; building energy conservation technology project appraisal; construction; emission reduction; general regression neural network; system engineering; Appraisal; Buildings; Computer languages; Energy conservation; Mathematical model; Neural networks; Power engineering and energy; Software testing; Software tools; Systems engineering and theory; Building energy conservation technology; GRNN; nonlinear; technology diffusion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Management, Innovation Management and Industrial Engineering, 2009 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-0-7695-3876-1
Type :
conf
DOI :
10.1109/ICIII.2009.517
Filename :
5370520
Link To Document :
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