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Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection

Abstract : We study the prediction of short term wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for those quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. For those time scales, outputs of numerical weather prediction models are usually overlooked even though they should provide valuable information on higher scales dynamics. In this work, we combine those outputs with local observations using machine learning. So as to make the results usable for practitioners, we focus on simple and well known methods which can handle a high volume of data. We study first variable selection through two simple techniques, a linear one and a nonlinear one. Then we exploit those results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the indirect one (directly predict wind power).
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03673674
Contributor : Dimitri Bouche Connect in order to contact the contributor
Submitted on : Friday, May 20, 2022 - 10:47:21 AM
Last modification on : Tuesday, May 24, 2022 - 3:38:46 AM

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  • HAL Id : hal-03673674, version 1
  • ARXIV : 2204.09362

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Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc, Riwal Plougonven, Marianne Clausel, et al.. Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection. 2022. ⟨hal-03673674⟩

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