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Mixture EMOS model for calibrating ensemble forecasts of wind speed

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  • S. Baran
  • S. Lerch

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  • S. Baran & S. Lerch, 2016. "Mixture EMOS model for calibrating ensemble forecasts of wind speed," Environmetrics, John Wiley & Sons, Ltd., vol. 27(2), pages 116-130, March.
    • Handle: RePEc:wly:envmet:v:27:y:2016:i:2:p:116-130
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    Cited by:

    1. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Sándor Baran & Patrícia Szokol & Marianna Szabó, 2021. "Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    4. Yang, Dazhi & Kleissl, Jan, 2023. "Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1640-1654.
    5. Baran, Sándor & Lerch, Sebastian, 2018. "Combining predictive distributions for the statistical post-processing of ensemble forecasts," International Journal of Forecasting, Elsevier, vol. 34(3), pages 477-496.
    6. Michaël Zamo & Liliane Bel & Olivier Mestre, 2021. "Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 202-225, January.

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