Feature selection for support vector regression in the application of building energy prediction

H. X. Zhao (1) , F. Magoules (1)

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

When using support vector regression to predict building energy consumption, since the energy influence factors are quite abundant and complex, the features associated with the statistical model could be in large quantity. This paper focuses in feature selection for the purpose of reducing model complexity without sacrificing performance. The optimal features are selected by their feasibility of obtaining and the evaluation of two filter methods. We test the selected subset on three datasets and train support vector regression with two different kernels: radial basis function and polynomial function. Extensive experiments show that the proposed method can select valid feature subset which guarantees the model accuracy and reduces the computational time.

Mots clés: support vector regression; feature selection; building; energy consumption

Domaines: Ingénierie, finance et science [cs.CE]; Intelligence artificielle [cs.AI]; Algorithme et structure de données [cs.DS]

H. X. Zhao, F. Magoules. Feature selection for support vector regression in the application of building energy prediction. 9th IEEE International Symposium on Applied Machine Intelligence and Informatics (SAMI 2011), Jan 2011, Smolenice, Slovakia. ⟨hal-00617937⟩