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Application of bias corrections to improve hub-height ensemble wind forecasts over the Tehachapi Wind Resource Area

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  • Chen, Shu-Hua
  • Yang, Shu-Chih
  • Chen, Chih-Ying
  • van Dam, C.P.
  • Cooperman, Aubryn
  • Shiu, Henry
  • MacDonald, Clinton
  • Zack, John

Abstract

This study demonstrates improvements in ensemble wind forecasts at hub height due to bias correction strategies and their impact on wind energy forecasts at the Alta II wind farm in southern California. The ensemble consists of twenty members that differ in physics schemes used. Ensemble wind forecasts are produced for three months. Hub-height sodar wind observations are used to evaluate forecast performance. Time-dependent bias correction (TBC) and probability bias correction (PBC) are proposed to calibrate hub-height ensemble wind forecasts.

Suggested Citation

  • Chen, Shu-Hua & Yang, Shu-Chih & Chen, Chih-Ying & van Dam, C.P. & Cooperman, Aubryn & Shiu, Henry & MacDonald, Clinton & Zack, John, 2019. "Application of bias corrections to improve hub-height ensemble wind forecasts over the Tehachapi Wind Resource Area," Renewable Energy, Elsevier, vol. 140(C), pages 281-291.
    • Handle: RePEc:eee:renene:v:140:y:2019:i:c:p:281-291
    DOI: 10.1016/j.renene.2019.03.043
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    References listed on IDEAS

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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. Deo, Ravinesh C. & Ahmed, A.A. Masrur & Casillas-Pérez, David & Pourmousavi, S. Ali & Segal, Gary & Yu, Yanshan & Salcedo-Sanz, Sancho, 2023. "Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression," Renewable Energy, Elsevier, vol. 203(C), pages 113-130.
    3. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.

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