American Journal of Civil Engineering and Architecture
ISSN (Print): 2328-398X ISSN (Online): 2328-3998 Website: http://www.sciepub.com/journal/ajcea Editor-in-chief: Mohammad Arif Kamal
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American Journal of Civil Engineering and Architecture. 2015, 3(3A), 1-8
DOI: 10.12691/ajcea-3-3A-1
Open AccessResearch Article

Prediction of Daylighting and Energy Performance Using Artificial Neural Network and Support Vector Machine

Sixuan Zhou1 and Dong Liu2,

1Florida State University, Tallahassee, United States

2Department of Civil and Architectural Engineering, Zhengzhou University, Zhengzhou, China

Pub. Date: August 18, 2015
(This article belongs to the Special Issue Big data analytics in Smart Buildings)

Cite this paper:
Sixuan Zhou and Dong Liu. Prediction of Daylighting and Energy Performance Using Artificial Neural Network and Support Vector Machine. American Journal of Civil Engineering and Architecture. 2015; 3(3A):1-8. doi: 10.12691/ajcea-3-3A-1

Abstract

The computerized building design has been developed to optimize building design. Machine learning techniques are explored to help predict building design performance. However, in the current building design tools, the optimization techniques have not been integrated closely with the computerized building design tool. Only a few tools add some optimization methods such as genetic algorithms. The aim of the paper is to use machine learning techniques to predict the daylighting metrics such as illuminance and thermal metrics for different combinations of window glazing transmittances, weather conditions and blind reflectance values. In this paper, three machine learning algorithms were evaluated, PCA (principal component analysis), ANN (artificial neural network), SVM (support vector machine). The PCA and forward feature selection algorithms were used to extract features or reduce the dimension of the features. Four comparisons were conducted: NN with PCA, ANN without PCA, SVM with PCA, and SVM without PCA. The results show that the NN with PCA has the best accuracy for the daylighting UDI classification problem. The ANN has an acceptable accuracy for the energy prediction problem.

Keywords:
artificial neural network support vector machine performance prediction UDI energy daylighting illuminance

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Pacific Northwest National Laboratory. 2011 Buildings energy data book., Washington, DC: U.S. DOE Energy Efficiency and Renewable Energy; 2011.
 
[2]  Tzempelikos A, Athienitis AK. “The impact of shading design and control on building cooling and lighting demand,” SOL ENERGY, 81 (3), 369-82, 2007.
 
[3]  Shen E, Hu J, Patel M. “Energy and visual comfort analysis of lighting and daylight control strategies,” BUILD ENVIRON, 78, 155-70, 2014.
 
[4]  Shen E, Patel M, Hu J. “Comparison of Energy and Visual Comfort Performance of Independent and Integrated Lighting and Daylight Controls Strategies,” in ACEEE Summer Study on Energy Efficiency in Industry, American Council for an Energy-Efficient Economy, New York, NY, USA.
 
[5]  Hviid CA, Nielsen TR, Svendsen S. “Simple tool to evaluate the impact of daylight on building energy consumption,” SOL ENERGY, 82 (9), 787-98, 2008.
 
[6]  Heschong L. Daylighting in Schools: An Investigation into the Relationship between Daylighting and Human Performance. Detailed Report., Fair Oaks, CA: Heschong Mahone Group; 1999.
 
[7]  Vine E, Lee E, Clear R, DiBartolomeo D, Selkowitz S. “Office worker response to an automated Venetian blind and electric lighting system: a pilot study,” ENERG BUILDINGS, 28 (2), 205-18, 1998.
 
[8]  Reinhart CF, Wienold J. “The daylighting dashboard - A simulation-based design analysis for daylit spaces,” BUILD ENVIRON, 46 (2), 386-96, 2011.
 
[9]  Reinhart C, Fitz A. “Findings from a survey on the current use of daylight simulations in building design,” ENERG BUILDINGS, 38 (7), 824-35, 2006.
 
[10]  Liu M, Wittchen KB, Heiselberg PK. “Development of a simplified method for intelligent glazed fa?ade design under different control strategies and verified by building simulation tool BSim,” BUILD ENVIRON, 74, 31-8, 2014.
 
[11]  Hu J, Olbina S. “An Expert System Based on OpenStudio Platform for Evaluation of Daylighting System Design,” in ASCE International Workshop on Computing in Civil Engineering, ASCE, Los Angeles, CA, 186-93.
 
[12]  Gagne JML, Andersen M, Norford LK. “An interactive expert system for daylighting design exploration,” BUILD ENVIRON, 46 (11), 2351-64, 2011.
 
[13]  Mardaljevic J. “Validation of a lighting simulation program under real sky conditions,” Lighting Research and Technology, 27 (4), 181-8, 1995.
 
[14]  Mardaljevic J. “Simulation of annual daylighting profiles for internal illuminance,” Lighting Research and Technology, 32 (3), 111-8, 2000.
 
[15]  Reinhart CF, Andersen M. “Development and validation of a Radiance model for a translucent panel,” ENERG BUILDINGS, 38 (7), 890-904, 2006.
 
[16]  Maamari F, Andersen M, de Boer J, Carroll WL, Dumortier D, Greenup P. “Experimental validation of simulation methods for bi-directional transmission properties at the daylighting performance level,” ENERG BUILDINGS, 38 (7), 849-68, 2006.
 
[17]  Reinhart CF, Herkel S. “The simulation of annual daylight illuminance distributions - a state-of-the-art comparison of six RADIANCE-based methods,” ENERG BUILDINGS, 32 (2), 167-87, 2000.
 
[18]  Loutzenhiser PG, Maxwell GM, Manz H. “An empirical validation of the daylighting algorithms and associated interactions in building energy simulation programs using various shading devices and windows,” ENERGY, 32 (10), 1855-70, 2007.
 
[19]  Andersen M, Kleindienst S, Yi L, Lee J, Bodart M, Cutler B. “An intuitive daylighting performance analysis and optimization approach,” BUILD RES INF, 36 (6), 593-607, 2008.
 
[20]  Mardaljevic J. “Spatio-temporal dynamics of solar shading for a parametrically defined roof system,” Energy & Buildings, 36 (8), 815-23, 2004.
 
[21]  Perez R, Ineichen P, Seals R, Michalsky J, Stewart R. “Modeling daylight availability and irradiance components from direct and global irradiance,” SOL ENERGY, 44 (5), 271-89, 1990.
 
[22]  Hu J, Olbina S. “Simulation-Based Model for Integrated Daylighting System Design,” J COMPUT CIVIL ENG, A4014003, 2013.
 
[23]  Li DHW. “A review of daylight illuminance determinations and energy implications,” APPL ENERG, 87 (7), 2109-18, 2010.
 
[24]  Hu J, Olbina S. “Radiance-based Model for Optimal Selection of Window Systems,” in ASCE 2012 International Conference on Computing in Civil Engineering, Clearwater Beach, FL, USA.
 
[25]  Reinhart CF, Walkenhorst O. “Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds,” ENERG BUILDINGS, 33 (7), 683-97, 2001.
 
[26]  Kazanasmaz T, Gunaydin M, Binol S. “Artificial neural networks to predict daylight illuminance in office buildings,” BUILD ENVIRON, 44 (8), 1751-7, 2009.
 
[27]  Hu J, Olbina S. “Illuminance-based slat angle selection model for automated control of split blinds,” BUILD ENVIRON, 46 (3), 786-96, 2011.
 
[28]  Olbina S, Hu J. “Daylighting and thermal performance of automated split-controlled blinds,” BUILD ENVIRON, 56 (0), 127, 2012.
 
[29]  Hu J, Patel M. “Optimized Selection and Placement of Sensors using Building Information Models (BIM),” in IES Annual Conference, Pittsburgh, PA.