Reassessing The Accuracy of Weibull Distribution for Wind Speed Modeling and Energy Assessment in Libya

Khaled Abdusamad; Muammer Alus

doi:10.66411/jer.v41i1.121

Authors

Khaled Abdusamad Libyan Academy for Postgraduate Studies Author
Muammer Alus University of Elmergib Author

DOI:

https://doi.org/10.66411/jer.v41i1.121

Keywords:

Wind power density, Weibull distribution, Kolmogorov–Smirnov test, Probability distributions, Wind speed modeling

Abstract

Accurate wind speed modeling is essential for reliable wind resource assessment and wind farm planning. Although the Weibull distribution is widely used in wind energy studies, its accuracy may vary depending on regional climatic characteristics. This study evaluates the suitability of the Weibull distribution for modeling wind speed data in Libya and compares its performance with several alternative probability distributions. The analysis is based on 7,305 daily wind speed observations obtained from the NASA POWER database for the period 2004–2023 at a reference height of 50 m above Sea level for five locations in Libya: Tripoli, Ajdabiyah, Alkufra, Awbari, and Darnah. The mean wind speeds range from 6.419 m/s in Tripoli to 7.270 m/s in Darnah. Statistical analysis was performed using EasyFit and MATLAB, while the Kolmogorov–Smirnov (K–S) test was applied to evaluate the goodness of fit between observed and theoretical distributions. The results show that the Weibull distribution does not provide the best statistical representation for any of the studied locations. Instead, Johnson SB, Dagum (4P), and Burr distributions demonstrated superior performance. The estimated wind power density ranged from 52.57 W/m² in Tripoli to 78.97 W/m² in Darnah, highlighting the importance of flexible multi-parameter distributions for improving wind resource assessment and supporting more reliable wind farm planning in Libya.

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