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Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model

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  • Duarte Jacondino, William
  • Nascimento, Ana Lucia da Silva
  • Calvetti, Leonardo
  • Fisch, Gilberto
  • Augustus Assis Beneti, Cesar
  • da Paz, Sheila Radman

Abstract

Wind energy is rapidly growing industry in Brazil. Wind speed forecasting is necessary in the planning, controlling, and monitoring for the reliable and efficient operation of the wind power systems. Thus, this study focuses on the impact of different physics parameterization in forecasting wind speed in two onshore wind farms using the Weather and Research Forecasting (WRF) model. The wind farms are located in Parazinho, in the northeast of Brazil, a region with high wind resource. Hindcasts are performed for a high (i.e., July 2017) and low (i.e., April 2017) wind speed regimes using different forecast lead-times (i.e., 24–48 h). The best performing setup consists of Thompson microphysics, Bougeault-Lacarrere PBL, Betts-Miller cumulus, New Goddard Longwave/Shortwave radiation, and Pleim-Xiu Land Surface schemes. Our findings also suggest that the model forecast setting with the TKE closure scheme, namely BouLac, performed better than that setting with first-order closure ACM2. The best mean monthly error (MAE) obtained is 1.1 m s−1 for wind and 12.6% for wind power.

Suggested Citation

  • Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010896
    DOI: 10.1016/j.energy.2021.120841
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    10. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    11. Wang, Hao & Ye, Jingzhen & Huang, Linxuan & Wang, Qiang & Zhang, Haohua, 2023. "A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction," Energy, Elsevier, vol. 262(PA).
    12. Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
    13. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
    14. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    15. Yakoub, Ghali & Mathew, Sathyajith & Leal, Joao, 2023. "Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models," Energy, Elsevier, vol. 263(PD).
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    Keywords

    WRF; Onshore; Forecast; Wind power; Brazil;
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