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Practices and Considerations in Wind Data Processing for Accurate and Efficient Wind Farm Energy Calculation

Author

Listed:
  • Angel Gaspar Gonzalez-Rodriguez

    (Department of Electronic Engineering and Automation, University of Jaen, 23071 Jaen, Spain)

  • Jose Manuel Riega-Medina

    (Facultad de Ciencias, Universidad Nacional de Ingeniería, Rimac 15333, Peru
    Current address: Higher Polytechnic School, University of Jaen, Campus las Lagunillas s/n, 23071 Jaen, Spain.
    These authors contributed equally to this work.)

  • Ildefonso Ruano-Ruano

    (Department of Telecommunications Engineering, University of Jaen, 23071 Jaen, Spain
    These authors contributed equally to this work.)

  • Jose Vicente Muñoz-Diez

    (Department of Electronic Engineering and Automation, University of Jaen, 23071 Jaen, Spain
    These authors contributed equally to this work.)

Abstract

An accurate estimation of future wind conditions is essential for calculating the annual energy produced by a wind farm. This estimation should be based on historical wind data collected over several years at the site location. However, research articles often rely on data grouped into 12 sectors. This article examines five methods to improve the speed and accuracy in the use of wind data. First, it studies the effect of inadequate Weibull parameter calculation based on historical data showing that purely mathematical fitting methods (the traditional ones) are not valid. Then, the error introduced by wind speed discretization is evaluated showing that the traditional binning of 1 m/s is not always the best choice. Next, the effect of using symmetric wind roses is examined, demonstrating that it is possible to reduce computation time by half for layouts exhibiting point symmetry, with negligible error for other layouts. After that, the effect of abrupt wind condition distributions caused by sectorization, which can alter results when searching for optimal configurations, is analyzed proposing continuous interpolation of wind data to improve result consistency. Finally, an alternative to the wind rose is proposed to provide a quick assessment of the highest-quality wind directions.

Suggested Citation

  • Angel Gaspar Gonzalez-Rodriguez & Jose Manuel Riega-Medina & Ildefonso Ruano-Ruano & Jose Vicente Muñoz-Diez, 2025. "Practices and Considerations in Wind Data Processing for Accurate and Efficient Wind Farm Energy Calculation," Energies, MDPI, vol. 18(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3402-:d:1689470
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    References listed on IDEAS

    as
    1. Ju Feng & Wen Zhong Shen, 2015. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction," Energies, MDPI, vol. 8(4), pages 1-18, April.
    2. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    3. Raichle, Brian W. & Carson, W. Richard, 2009. "Wind resource assessment of the Southern Appalachian Ridges in the Southeastern United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1104-1110, June.
    4. Navarro Diaz, Gonzalo P. & Saulo, A. Celeste & Otero, Alejandro D., 2021. "Full wind rose wind farm simulation including wake and terrain effects for energy yield assessment," Energy, Elsevier, vol. 237(C).
    5. Angel G. Gonzalez-Rodriguez & Javier Serrano-González & Manuel Burgos-Payán & Jesús Manuel Riquelme-Santos, 2021. "Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources," Energies, MDPI, vol. 14(10), pages 1-20, May.
    6. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    7. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    8. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.
    9. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
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