Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

Sholahudin Sholahudin; Azimil Gani Alam; Chang In Baek; Hwataik Han

doi:10.4028/www.scientific.net/AMM.819.541

Paper Titles

Modeling of an Optimized Hybrid Energy System for Kutubdia Island, Bangladesh
p.518

Exergy Analysis and Exergoeconomic Optimization with Multiobjective Method of Unit 4 Kamojang Geothermal Power Plant
p.523

An Improved Cost Apportionment for Desalination Combined with Power Plant: An Exergetic Analyses
p.530

Experimental Investigation of Multijunction Solar Cell Using Two Axis Solar Tracker
p.536

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments
p.541

Analysis of Center Pivot Irrigation System by Experimental Method
p.549

Study of Oxidation Behavior of Thermal Spray Coating on Superalloys at 900 °C
p.553

The Road Particle Effect on Squeal Noise of Disc Braking System
p.563

Prediction of Gas Hydrate Formation Using Radial Basis Function Network and Support Vector Machines
p.569

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 819Prediction and Analysis of Building Energy...

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

Abstract:

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

You might also be interested in these eBooks

Advances in Heat Transfer, Flow Engineering and Energy Installations

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 819)

Pages:

541-545

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.819.541

Citation:

Cite this paper

Online since:

January 2016

Authors:

Sholahudin Sholahudin, Azimil Gani Alam, Chang In Baek, Hwataik Han*

Keywords:

Building, Design of Experiment (DOE), Energy, Neural Network (NN), Prediction

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] L. Perez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and Buildings 40 (3) (2008) 394-398.

DOI: 10.1016/j.enbuild.2007.03.007

Google Scholar

[2] W.G. Cai, Y. Wu, Y. Zhong, H. Ren, China building energy consumption: situation, challenges and corresponding measures, Energy Policy 37 (6) (2009) 2054-(2059).

DOI: 10.1016/j.enpol.2008.11.037

Google Scholar

[3] European Commission, Directive 2002/91/EC of the European parliament and of the council of 16th December 2002 on the energy performance of buildings, Official journal of the European Communities, L1/65-L1/71, 04 /01/(2003).

DOI: 10.1017/cbo9780511610851.032

Google Scholar

[4] Z. Yu, F. Haghigrat, B.C. M Fung, H. Yoshimo, A decision tree method for building energy demand modelling, Energy and Building, 42 (2010) 1637-1646.

DOI: 10.1016/j.enbuild.2010.04.006

Google Scholar

[5] B. Dong, C. Cao, S.E. Lee, Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings 37 (2005) 545-553.

DOI: 10.1016/j.enbuild.2004.09.009

Google Scholar

[6] T. Catalina J. Virgone, E. Blanco, Development and validation of regression models to predict monthly heating demand for residential buildings, Energy and Buildings 40 (2008) 1825-1832.

DOI: 10.1016/j.enbuild.2008.04.001

Google Scholar

[7] K.K.W. Wan D.H.W. Li, D. Liu, J.C. Lam, Future trends of building heating and cooling loads and energy consumption in different climates, Building and Environment 46 (2011) 223-234.

DOI: 10.1016/j.buildenv.2010.07.016

Google Scholar

[8] S. Schiavon, K.H. Lee, F. Bauman, T. Webster, Influence of raised floor on zone design cooling load in commercial buildings, Energy and buildings 42 (2010) 1182-1191.

DOI: 10.1016/j.enbuild.2010.02.009

Google Scholar

[9] A. Tsanas, A. Xifara, Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings (2012).

DOI: 10.1016/j.enbuild.2012.03.003

Google Scholar

[10] Krose, B., Smagt, P, An introduction to neural network, The University of Amsterdam. Eighth edition, (1996).

Google Scholar

[11] Ripley, B. D, Pattern Recognition and Neural Networks, Cambridge University Press, (1996).

Google Scholar

[12] Deng, J, Dynamic Neural Networks with Hybrid Structures for Nonlinear System Identification. Engineering Applications of Artificial Intelligence 26 (1), 2013, PP. 281–292.

DOI: 10.1016/j.engappai.2012.05.003

Google Scholar

[13] Telford, Jacqueline K, A Brief Introduction to Design of Experiments, Johns Hopkins APL Technical Digest, Vol. 27 No. 3 (2007).

Google Scholar