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Research Article

An artificial neural network identification method for thermal resistance of exterior walls of buildings based on numerical experiments

Lin Chen1, Changhong Zhan1( ), Guanghao Li1( ), Aimin Zhang2
School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, 66 Xidazhi Street, Harbin 150001, China
College of Civil Engineering, Northeast Forestry University, Harbin 150001, China
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Abstract

Current standards for the detection methods for the thermal resistance of exterior walls of buildings have shortcomings, such as strict conditions, high time consumption, and heavy workloads. To overcome the shortcomings of existing methods, in this study, an artificial neural network identification method was used to detect the thermal resistance of exterior walls. To enhance efficiency and reduce costs, the data required by the neural network modelling were obtained through a numerical experiment based on an unsteady heat transfer model. In this paper, the thermal resistance identification results of three neural networks—Back Propagation (BP), Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN)—were analysed and compared. The results demonstrated that the GRNN neural network had the best identification effect. Thus, the identification system for the thermal resistance of exterior walls was established using the GRNN neural network. The average test error in the training sample was 0.098%, and the average error in the anti-noise test was 4.82%. The network identification accuracy was verified by five groups of field measured data. In comparison with the conventional heat flux method, the average error was 5.82%, which proved the reliability of the proposed GRNN identification model.

Keywords

artificial neural networknumerical simulationidentificationthermal resistance

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Building Simulation
Volume 12 Issue 3,
June 2019
Pages 425-440
DOI: 10.1007/s12273-019-0524-6
Cite this article:
Chen L, Zhan C, Li G, et al. An artificial neural network identification method for thermal resistance of exterior walls of buildings based on numerical experiments. Building Simulation, 2019, 12(3): 425-440. https://doi.org/10.1007/s12273-019-0524-6

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Received: 22 August 2018
Revised: 12 December 2018
Accepted: 13 February 2019
Published: 28 March 2019
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
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