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SVRs and Uncertainty Estimates in Wind Energy Prediction

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9095)

Abstract

While Support Vector Regression, SVR, is one of the algorithms of choice in modeling problems, construction of its error intervals seems to have received less attention. On the other hand, general noise cost functions for SVR have been recently proposed. Taking this into account, this paper describes a direct approach to build error intervals for different choices of residual distributions. We also discuss how to fit these noise models and estimate their parameters, proceeding then to give a comparison between intervals obtained using this method. under different ways to estimate SVR parameters as well as the intervals obtained by employing a full SVR Bayesian framework. The proposed approach is shown on a synthetic problem to provide better accuracy when models fitted coincide with the noise injected into the problem. Finally, we apply it to wind energy forecasting, exploiting predicted energy magnitudes to define intervals with different widths.

Keywords

  • Support Vector Regression
  • Noise Model
  • Uncertainty Estimate
  • Uncertainty Interval
  • Noise Distribution

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Jesús Prada .

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Prada, J., Dorronsoro, J.R. (2015). SVRs and Uncertainty Estimates in Wind Energy Prediction. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-19222-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19221-5

  • Online ISBN: 978-3-319-19222-2

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