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Probabilistic forecasting of the wind energy resource at the monthly to seasonal scale


  • Bastien Alonzo

    (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay; Laboratoire de Probabilités et Modéles Aléatoires, Université Paris Diderot-Paris 7)

  • Philippe Drobinski

    (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay)

  • Riwal Plougonven

    (IPSL; LMD; CNRS; Ecole Polytechnique; Université de Paris-Saclay)

  • Peter Tankov

    (CREST; ENSAE ParisTech)


We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale in France. On such long-term timescales, the variability of the surface wind speed is strongly in uenced by the atmosphere large-scale situation. Our aim is to predict the daily mean wind speed distribution at a speci c location using the information on the atmosphere large-scale situation, summarized by an index. To this end, we estimate, over 20 years of daily data, the conditional probability density function of the wind speed given the index. We next use the ECMWF seasonal forecast ensemble to predict the atmosphere large-scale situation and the index at the seasonal timescale. We show that the model is sharper than the climatology at the monthly horizon, even if it displays a strong loss of precision after 15 days. Using a statistical postprocessing method to recalibrate the ensemble forecast leads to further improvement of our probabilistic forecast, which then remains sharper than the climatology at the seasonal horizon.

Suggested Citation

  • Bastien Alonzo & Philippe Drobinski & Riwal Plougonven & Peter Tankov, 2017. "Probabilistic forecasting of the wind energy resource at the monthly to seasonal scale," Working Papers 2017-88, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2017-88

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    Cited by:

    1. Alonzo, Bastien & Tankov, Peter & Drobinski, Philippe & Plougonven, Riwal, 2020. "Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height," International Journal of Forecasting, Elsevier, vol. 36(2), pages 515-530.

    More about this item


    Wind energy; Wind speed forecasting; Seasonal forecasting; Probabilistic forecasting; Ensemble forecasts; Ensemble model output statistics;
    All these keywords.

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