Author manuscript, published in "Proceedings of the IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2011), Smolenice: Slovakia (2011)"
Abstract—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...
| Other Authors: | |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2011
|
| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.393.6031 http://hal.inria.fr/docs/00/61/79/37/PDF/paper.pdf |
| Summary: | Abstract—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 modelcomplexitywithoutsacrificingperformance.Theoptimal 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.Extensiveexperimentsshowthattheproposedmethod can select valid feature subset which guarantees the model accuracy and reduces the computational time. Keywords-support vector regression; feature selection; building; energy consumption I. |
|---|