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Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level

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  • Sajid Ali

    (Energy Innovation Research Center for Wind Turbine Support Structures, Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, Jeollabuk-do, Republic of Korea)

  • Hongbae Park

    (Department of Wind Energy, The Graduate School of Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, Jeollabuk-do, Republic of Korea)

  • Adnan Aslam Noon

    (Department of Mechanical Engineering, FET, International Islamic University, Islamabad 44000, Pakistan)

  • Aamer Sharif

    (School of Engineering, Edith Cowan University, Joondalup, Perth 6027, Australia)

  • Daeyong Lee

    (Department of Wind Energy, The Graduate School of Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, Jeollabuk-do, Republic of Korea)

Abstract

The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.

Suggested Citation

  • Sajid Ali & Hongbae Park & Adnan Aslam Noon & Aamer Sharif & Daeyong Lee, 2024. "Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level," Energies, MDPI, vol. 17(9), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2173-:d:1387597
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    References listed on IDEAS

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    1. Akpan, Anthony E. & Ben, Ubong C. & Ekwok, Stephen E. & Okolie, Chukwuma J. & Epuh, Emeka E. & Julzarika, Atriyon & Othman, Abdullah & Eldosouky, Ahmed M., 2024. "Technical and performance assessments of wind turbines in low wind speed areas using numerical, metaheuristic and remote sensing procedures," Applied Energy, Elsevier, vol. 357(C).
    2. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    3. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2021. "Assessment of long-term offshore wind energy potential in the south and southeast coasts of China based on a 55-year dataset," Energy, Elsevier, vol. 224(C).
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