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Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Author

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  • Ammara Kanwal
  • Zia ul Rehman Tahir
  • Muhammad Asim
  • Nasir Hayat
  • Muhammad Farooq
  • Muhammad Abdullah
  • Muhammad Azhar

Abstract

The evaluation of reanalysis and analysis data (estimated data) against in-situ measured data is essential to find uncertainties before its use for wind resource assessment. The performance evaluation of four different generations reanalysis datasets (NCEP-CFSR, NCEP-DOE, NCEP-NCAR and JRA-55) and two analysis datasets (NCEP-FNL and NCEP-GFS) was done against measured data for six sites using statistical analysis. A comparison of monthly mean time-series, Weibull probability distribution function and wind rose diagram of measured and estimated data was performed. The MBE and RMSE for wind speed range from −2.18 to 2.01 m/s and 1.34 to 3.00 m/s respectively; whereas MBE and RMSE for wind direction range from −34.34° to 13.90° and 40.58° to 71.28° respectively for six sites using all datasets. NCEP-CFSR data show promising results for most of the sites with the lowest errors and better correlation coefficients. NCEP-CFSR data being the new generation reanalysis having higher spatial resolution show better results compared to other reanalyses and analyses. The reanalysis and analysis wind data can be used as alternative to measured data to assess wind energy potential.

Suggested Citation

  • Ammara Kanwal & Zia ul Rehman Tahir & Muhammad Asim & Nasir Hayat & Muhammad Farooq & Muhammad Abdullah & Muhammad Azhar, 2023. "Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment," Energy & Environment, , vol. 34(5), pages 1258-1284, August.
    • Handle: RePEc:sae:engenv:v:34:y:2023:i:5:p:1258-1284
    DOI: 10.1177/0958305X221084078
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    1. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    2. Ozgener, Leyla, 2010. "Investigation of wind energy potential of Muradiye in Manisa, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3232-3236, December.
    3. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    4. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    5. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
    6. Miao, Haozeyu & Dong, Danhong & Huang, Gang & Hu, Kaiming & Tian, Qun & Gong, Yuanfa, 2020. "Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets," Energy, Elsevier, vol. 200(C).
    7. Nagababu, Garlapati & Kachhwaha, Surendra Singh & Savsani, Vimal, 2017. "Estimation of technical and economic potential of offshore wind along the coast of India," Energy, Elsevier, vol. 138(C), pages 79-91.
    8. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    9. 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.
    10. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula," Applied Energy, Elsevier, vol. 134(C), pages 57-64.
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