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

Sixuan Zhou¹ and Dong Liu^2,

¹Florida State University, Tallahassee, United States

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

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

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