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.
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Acknowledgements
With partial support from Spain’s grants TIN2016-76406-P and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAM–ADIC Chair for Data Science and Machine Learning. We thank Red Eléctrica de España for making available wind energy data and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM. We also thank the Agencia Estatal de Meteorología, AEMET, and the ECMWF for access to the MARS repository.
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de la Pompa, V., Catalina, A., Dorronsoro, J.R. (2018). Gaussian Process Kernels for Support Vector Regression in Wind Energy Prediction. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_17
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DOI: https://doi.org/10.1007/978-3-030-03496-2_17
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