Gaussian Process Kernels for Support Vector Regression in Wind Energy Prediction

Abstract

We consider wind energy prediction by Support Vector Regression (SVR) with generalized Gaussian Process kernels, proposing a validation–based kernel choice which will be then used in two prediction problems instead of the standard Gaussian ones. The resulting model beats a Gaussian SVR in one problem and ties in the other. Furthermore, besides the flexibility this approach offers, SVR hyper–parameterization can be also simplified.