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Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Author

Listed:
  • Jonghoon Jin

    (Department of Mechanical Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
    These authors contributed equally to this work.)

  • Yuzhang Che

    (Department of Mechanical Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
    College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China)

  • Jiafeng Zheng

    (College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China
    These authors contributed equally to this work.)

  • Feng Xiao

    (Department of Mechanical Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
    These authors contributed equally to this work.)

Abstract

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model.

Suggested Citation

  • Jonghoon Jin & Yuzhang Che & Jiafeng Zheng & Feng Xiao, 2019. "Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan," Energies, MDPI, vol. 12(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1505-:d:224714
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    References listed on IDEAS

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    1. Xsitaaz T. Chadee & Naresh R. Seegobin & Ricardo M. Clarke, 2017. "Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago," Energies, MDPI, vol. 10(7), pages 1-23, July.
    2. Brandon Storm & Sukanta Basu, 2010. "The WRF Model Forecast-Derived Low-Level Wind Shear Climatology over the United States Great Plains," Energies, MDPI, vol. 3(2), pages 1-19, February.
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    Cited by:

    1. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    2. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).

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