The Best-Fit Wind-speed Probability Distribution Functions for Winds in Libya

• Published in: 2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) pp. 1 - 6
• Main Authors: Milad, Sulaiman, Milicevic, Srdan, Katic, Vladimir A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03-06-2024
• Subjects: Best fit; Libya; PDFs; Power measurement; Probability density function; Probability distribution; Wind energy generation; Wind farms; Wind power generation; Wind speed
• DOI: 10.1109/IcETRAN62308.2024.10645193

This paper presents a comprehensive study on wind power potential using measurement data spanning one year and collected from seven key locations in Libya: Murzuq, Sabha, Tripoli, Tarhuna, Ghadames, Sirt, and Tajura. Existing literature primarily relies on analyzing the wind data in Libya using the Weibull probability distribution function (PDF). However, to broaden this research scope, the authors decided to expand their investigation by considering fourteen parametric PDFs in total. The results are validated by Kolmogorov-Smirnov (KS) Goodness-of-Fit (GoF) tests and the top best fits are ranked. The results highlight that while the Weibull distribution remains the top choice, closely followed by the Gamma, Normal, and Logistic distributions, there are variations in the performance of these distributions across the different locations in Libya. This study contributes valuable insights into understanding the wind power potential across diverse geographic regions in Libya, emphasizing the importance of considering multiple distribution functions in wind data analysis.