IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v34y2023i5p1258-1284.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X221084078
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0958305X221084078?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. de Assis Tavares, Luiz Filipe & Shadman, Milad & de Freitas Assad, Luiz Paulo & Silva, Corbiniano & Landau, Luiz & Estefen, Segen F., 2020. "Assessment of the offshore wind technical potential for the Brazilian Southeast and South regions," Energy, Elsevier, vol. 196(C).
    3. Chen, Xinping & Foley, Aoife & Zhang, Zenghai & Wang, Kaimin & O'Driscoll, Kieran, 2020. "An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    4. Sharp, Ed & Dodds, Paul & Barrett, Mark & Spataru, Catalina, 2015. "Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information," Renewable Energy, Elsevier, vol. 77(C), pages 527-538.
    5. de Assis Tavares, Luiz Filipe & Shadman, Milad & Assad, Luiz Paulo de Freitas & Estefen, Segen F., 2022. "Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State," Energy, Elsevier, vol. 240(C).
    6. Boudia, Sidi Mohammed & Santos, João Andrade, 2019. "Assessment of large-scale wind resource features in Algeria," Energy, Elsevier, vol. 189(C).
    7. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.
    8. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    9. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    10. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    11. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    12. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys – A comparative study for the Iberian Peninsula Atlantic coast," Renewable Energy, Elsevier, vol. 102(PB), pages 433-444.
    13. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    14. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    15. Tuy, Soklin & Lee, Han Soo & Chreng, Karodine, 2022. "Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 satellite imagery, optimal sites, annual energy production and equivalent C," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    16. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    17. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    18. Ulazia, Alain & Saenz, Jon & Ibarra-Berastegui, Gabriel, 2016. "Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline," Applied Energy, Elsevier, vol. 180(C), pages 617-627.
    19. Santos, F. & Gómez-Gesteira, M. & deCastro, M. & Añel, J.A. & Carvalho, D. & Costoya, Xurxo & Dias, J.M., 2018. "On the accuracy of CORDEX RCMs to project future winds over the Iberian Peninsula and surrounding ocean," Applied Energy, Elsevier, vol. 228(C), pages 289-300.
    20. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:engenv:v:34:y:2023:i:5:p:1258-1284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.