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WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal

Author

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  • Carvalho, D.
  • Rocha, A.
  • Gómez-Gesteira, M.
  • Silva Santos, C.

Abstract

The performance of the WRF mesoscale model in the wind simulation and wind energy estimates was assessed and evaluated under different initial and boundary forcing conditions. Due to the continuous evolution and progress in the development of reanalyses datasets, this work aims to compare an older, yet widely used, reanalysis (the NCEP-R2) with three recently released reanalyses datasets that represent the new generation of this type of data (ERA-Interim, NASA-MERRA and NCEP-CFSR). Due to its intensive use in wind energy assessment studies, the NCEP-GFS and NCEP-FNL analysis were also used to drive WRF and its results compared to those of the simulations driven by reanalyses.

Suggested Citation

  • Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    • Handle: RePEc:eee:appene:v:117:y:2014:i:c:p:116-126
    DOI: 10.1016/j.apenergy.2013.12.001
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    References listed on IDEAS

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    1. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    2. Ucar, Aynur & Balo, Figen, 2009. "Investigation of wind characteristics and assessment of wind-generation potentiality in Uludag-Bursa, Turkey," Applied Energy, Elsevier, vol. 86(3), pages 333-339, March.
    3. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    4. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    5. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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