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Sustainability 2018, 10(1), 84; https://doi.org/10.3390/su10010084

Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions

1,2
,
1,2,* and 1,2
1
School of Architecture, Harbin Institute of Technology, Harbin 150001, China
2
Heilongjiang Cold Region Architectural Science Key Laboratory, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 27 December 2017 / Accepted: 28 December 2017 / Published: 30 December 2017
(This article belongs to the Special Issue Achieving a Sustainable Future Using Renewable Materials in Buildings)
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Abstract

This paper aims to develop an artificial neural network (ANN) to predict the energy consumption and cost of cross laminated timber (CLT) office buildings in severe cold regions during the early stage of architectural design. Eleven variables were selected as input variables including building form and construction variables, and the values of input variables were determined by local building standards and surveys. ANNs were trained by the simulation data and Latin hypercube sampling (LHS) method was used to select training datasets for the ANN training. The best ANN was obtained by analyzing the output variables and the number of hidden layer neurons. The results showed that the ANN with multiple outputs presented better prediction performance than the ANN with single output. Moreover, the number of hidden layer neurons in ANN should be greater than five and preferably 10, and the best mean square error (MSE) value was 1.957 × 103. In addition, it was found that the time of predicting building energy consumption and cost by ANN was 80% shorter than that of traditional building energy consumption simulation and cost calculation method. View Full-Text
Keywords: cross laminated timber; artificial neural network; energy consumption; cost; office building; severe cold regions cross laminated timber; artificial neural network; energy consumption; cost; office building; severe cold regions
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Dong, Q.; Xing, K.; Zhang, H. Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions. Sustainability 2018, 10, 84.

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