Wind Resource Evaluation in Libya: A Comparative Study of Ten Numerical Methods for the Estimation of Weibull Parameters using Multiple Datasets


  • Youssef Kassem Department of Mechanical Engineering, Energy, Environment, and Water Research Center, Science, Technology, Engineering Education Application and Research Center, Near East University, Cyprus
  • Huseyin Camur Department of Mechanical Engineering, Science, Technology, Engineering Education Application and Research Center, Near East University, Cyprus
  • Almonsef Alhadi Salem Mosbah High and Intermediate Institute of Agricultural Technology, Gheran, Libya
Volume: 14 | Issue: 2 | Pages: 13388-13397 | April 2024 |


This study examines Libya's pursuit of sustainable wind energy solutions, using nine sites with mast measurements before the 2011 civil war and six gridded datasets, including CFSR, ERA5, EAR5-Ag, MERRA2, EAR5-Land, and TerraClimate. Employing the Weibull distribution function with ten methods, the empirical method of Justus proved to be optimal for calculating Weibull parameters across datasets. Al Bayda and Darnah exhibit substantial wind power potential (116.80-123.00 W/m²) based on MERRA2 data, making them ideal for large-scale wind turbine deployment. Furthermore, the results showed that wind power density was estimated below 100 W/m² for all selected locations according to CFSR, ERA5, EAR5-Ag, EAR5-Land, and TerraClimate. This study emphasizes the need for new mast measurements to refine dataset selection, which is crucial for accurate assessments and large wind farm planning. Consequently, this study provides key insights into optimizing wind energy utilization in diverse Libyan regions, addressing both the potential and the challenges in sustainable energy development.


Libya, wind energy potential, Weibull distribution, measured data, gridded datasets, numerical estimation method


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How to Cite

Y. Kassem, H. Camur, and A. A. S. Mosbah, “Wind Resource Evaluation in Libya: A Comparative Study of Ten Numerical Methods for the Estimation of Weibull Parameters using Multiple Datasets”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13388–13397, Apr. 2024.


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