Comparison of wind speed distributions: a case study for Aegean coast of Turkey

ABSTRACT

The determination of wind speed characteristics is extremely important for estimating the wind power output. In this context, Weibull distribution is one of the most commonly used statistical distributions for modeling wind speed. However, despite its widespread usage, it may not be accurate for modeling all wind regimes. For this reason, alternative distributions are utilized for modeling wind speed data. In this study, we use Rayleigh, Inverse Weibull (IW), Burr Type III, Extreme Value (EV), Gamma, Inverse Gamma (IG), Marshall–Olkin extended Lindley (MOEL), generalized Extreme Value (GEV) and exponentiated Weibull (EW) distributions as alternatives to Weibull. Our aim is to determine the distribution which provides the best fit for different wind regimes encountered in nature. In the application part, data sets are taken from stations located on Turkey’s Aegean coast and the fitting performances of the above-mentioned distributions are compared. In order to determine the appropriate wind speed distributions. Different model selection criteria are utilized.

KEYWORDS:

• Wind energy
• maximum likelihood
• wind speed distributions
• model selection criteria
• exponentiated Weibull

Acknowledgments

Certain parts of the research work represented in this manuscript are supported by Artvin Coruh University, Project No: 2016.F13.02.01, Artvin, 2017.

Additional information

Notes on contributors

Fatma Gül Akgül

Fatma Gül Akgül is an assistant professor at Artvin Coruh University, Turkey. She received her B.S. degree in Statistics and Computer Sciences from Karadeniz Technical University. She received her M.S. degree in Statistics from Karadeniz Technical University. She received her Ph. D. degree in Statistics at Ankara University.  Her main areas of interest are reliability and robust statistics.

Birdal Şenoğlu

Birdal Şenoğlu is a Professor at Ankara University, Turkey. He received his B.S. degree in Statistics from Middle East Technical University and his M.S. degree in Statistics from Iowa State University. He received his Ph. D. degree in Statistics from Middle East Technical University. His main areas of interest are experimental design and robust statistics.