Prediction of Building Energy Consumption at Early Design Stage Based on Artificial Neural Network

Abstract:

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In this study, the main objective is to predict buildings heating and cooling energy consumption benefitting from 18 building envelope performance parameters by using artificial neural network. A backpropagation neural network has been preferred and the data have been presented to network by being normalized. 7 Cases application study was carried out with conventional methods. The building energy simulation software DeST was used for the calculations of energy consumption and ANN toolbox of MATLAB was used for predictions. As a conclusion, when the calculated values compared with the outputs of the network, it is proven that ANN gives satisfactory results successful prediction rate of over 97% and will be helpful for designers in designing period of buildings.

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Edited by:

Yanwen Wu

Pages:

580-585

DOI:

10.4028/www.scientific.net/AMR.108-111.580

Citation:

J. Yao "Prediction of Building Energy Consumption at Early Design Stage Based on Artificial Neural Network", Advanced Materials Research, Vols. 108-111, pp. 580-585, 2010

Online since:

May 2010

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Price:

$35.00

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