AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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

References

 
A Ashtiani, PA Mirzaei, F Haghighat (2014). Indoor thermal condition in urban heat island: Comparison of the artificial neural network and regression methods prediction. Energy and Buildings, 76: 597-604.
Crossref
 
Z Afroz, G Shafiullah, T Urmee, G Higgins (2017). Prediction of indoor temperature in an institutional building. Energy Procedia, 142: 1860-1866.
Crossref
 
C Balaji, T Padhi (2010). A new ANN driven MCMC method for multi-parameter estimation in two-dimensional conduction with heat generation. International Journal of Heat and Mass Transfer, 53: 5440-5455.
Crossref
 
A Ben-Nakhi, MA Mahmoud, AM Mahmoud (2008). Inter-model comparison of CFD and neural network analysis of natural convection heat transfer in a partitioned enclosure. Applied Mathematical Modelling, 32:1834-1847.
Crossref
 
U Berardi, M Naldi (2017). The impact of the temperature dependent thermal conductivity of insulating materials on the effective building envelope performance. Energy and Buildings, 144: 262-275.
Crossref
 
I Budaiwi, A Abdou (2013). The impact of thermal conductivity change of moist fibrous insulation on energy performance of buildings under hot-humid conditions. Energy and Buildings, 60: 388-399.
Crossref
 
PG Cesaratto, M De Carli, S Marinetti (2011). Effect of different parameters on the in situ thermal conductance evaluation. Energy and Buildings, 43:1792-1801.
Crossref
 
PG Cesaratto, M De Carli (2013). A measuring campaign of thermal conductance in situ and possible impacts on net energy demand in buildings. Energy and Buildings, 59:29-36.
Crossref
 
Y Chen, Z Chen (2000). A neural-network-based experimental technique for determining z-transfer function coefficients of a building envelope. Building and Environment, 35:181-189.
Crossref
 
PM Ferreira, AE Ruano, S Silva, EZE Conceição (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55: 238-251.
Crossref
 
G Ficco, F Iannetta, E Ianniello, FR d’Ambrosio Alfano, M Dell’Isola (2015). U-value in situ measurement for energy diagnosis of existing buildings. Energy and Buildings, 104: 108-121.
Crossref
 
ISO (1994). ISO 8990: 1994. Thermal insulation—Determination of steady-state thermal transmission properties—Calibrated and guarded hot box. International Organization for Standardization.
 
ISO (2014). ISO 9869-1-2014. Thermal insulation. Building elements. In-situ measurement of thermal resistance and thermal transmittance. Heat flow meter method. International Organization for Standardization.
 
M Khoukhi, N Fezzioui, B Draoui, L Salah (2016). The impact of changes in thermal conductivity of polystyrene insulation material under different operating temperatures on the heat transfer through the building envelope. Applied Thermal Engineering, 105: 669-674.
Crossref
 
M Khoukhi (2018). The combined effect of heat and moisture transfer dependent thermal conductivity of polystyrene insulation material: Impact on building energy performance. Energy and Buildings, 169: 228-235.
Crossref
 
T Lu, M Viljanen (2009). Prediction of indoor temperature and relative humidity using neural network models: Model comparison. Neural Computing and Applications, 18: 345-357.
Crossref
 
Meteorological Information Center of China Meteorological Administration (2005). Meteorological Data Set for Building Thermal Environment Analysis of China. Beijing: China Architecture and Building Press. (in Chinese)
 
L Mba, P Meukam, A Kemajou (2016). Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy and Buildings, 121: 32-42.
Crossref
 
MOHURD (2009). Construction Engineering Industry Construction Standard JGJT132-2009. Energy Saving Testing Standard for Residential Buildings. Ministry of Housing and Urban Rural Development of China. (in Chinese)
 
MOHURD (2010). Construction Engineering Industry Construction Standard JGJ26-2010. Design Standards for Residential Buildings in Severe Cold and Cold Regions. Ministry of Housing and Urban Rural Development of China. (in Chinese)
 
MOHURD (2016). National Standard of China GBT50176-2016. Code for thermal design of civil buildings. Ministry of Housing and Urban Rural Development of China. (in Chinese)
 
JW Moon, SH Yoon, S Kim (2013). Development of an artificial neural network model based thermal control logic for double skin envelopes in winter. Building and Environment, 61: 149-159.
Crossref
 
JW Moon, JH Lee, Y Yoon, S Kim (2014). Determining optimum control of double skin envelope for indoor thermal environment based on artificial neural network. Energy and Buildings, 69: 175-183.
Crossref
 
JW Moon, SK Jung (2016). Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings. Applied Thermal Engineering, 103: 1135-1144.
Crossref
 
R Qin, D Yan, X Zhou, Y Jiang (2012). Research on a dynamic simulation method of atrium thermal environment based on neural network. Building and Environment, 50: 214-220.
Crossref
 
M Soleimani-Mohseni, B Thomas, P Fahlén (2006). Estimation of operative temperature in buildings using artificial neural networks. Energy and Buildings, 38: 635-640.
Crossref
 
DF Specht (1993). The general regression neural network—Rediscovered. Neural Networks, 6: 1033-1034.
Crossref
 
J Sun, T Zhu, J Wu (2006). Analysis of key input variables for solving wall heat transfer coefficient by neural network method. New Building Materials, 2006(12): 61-64. (in Chinese)
 
L Sun, C Feng, Y Cui (2017). Influence of temperature and moisture content on the thermal conductivity of building materials. Journal of Civil, Architectural & Environmental Engineering, 39(6): 123-128. (in Chinese)
 
B Thomas, M Soleimani-Mohseni (2007). Artificial neural network models for indoor temperature prediction: Investigations in two buildings. Neural Computing and Applications, 16: 81-89.
Crossref
 
SL Wong, KKW Wan, TNT Lam (2010). Artificial neural networks for energy analysis of office buildings with daylighting. Applied Energy, 87: 551-557.
Crossref
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

556

Views

10

Crossref

N/A

Web of Science

13

Scopus

1

CSCD

Altmetrics
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
Return