Wind Speed Modelling Using Inverse Weibull Distrubition: A Case Study for Bilecik, Turkey

Emrah Dokur, Mehmet Kurban, Salim Ceyhan


Wind speed modelling plays a critical role in wind related engineering studies. Frequency distribution of wind speed can be displayed different distributions such as Gamma, lognormal, Rayleigh and Weibull.Weibull distribution is used to model of  many regions of the world wind speed in recent year. In this paper, wind speed potential analysis realized using Inverse Weibull Distribution (IWD) for Bilecik, Turkey. Maximum likelihood method for parameter estimation   used for wind speed modelling analysis. All analysis is carried out by Matrix Laboratory (MATLAB) programming language. Monthly and yearly wind speeds are modeled by Inverse Weibull distribution. Accuracy of the modelling is evaluated in terms of Root Mean Square Error (RMSE) 


Wind Speed; Modelling; Weibull Distrubition; Renewable Energy; Inverse Weibull Distrubition.

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Akda?, S. A., & Dinler, A. (2009). A new method to estimate Weibull parameters for wind energy applications. Energy conversion and management, 50(7), 1761-1766.

Akgül, F. G., ?eno?lu, B., & Arslan, T. (2016). An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Conversion and Management, 114, 234-240.

Bardsley, W. E. (1980). Note on the use of the inverse Gaussian distribution for wind energy applications. Journal of Applied Meteorology, 19(9), 1126-1130.

Carta, J. A., Ramirez, P., & Velazquez, S. (2009). A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renewable and Sustainable Energy Reviews, 13(5), 933-955.

Garcia, A., Torres, J. L., Prieto, E., & De Francisco, A. (1998). Fitting wind speed distributions: a case study. Solar Energy, 62(2), 139-144.

Guttman, N. B., Hosking, J. R. M., & Wallis, J. R. (1993). Regional precipitation quantile values for the continental United States computed from L-moments. Journal of Climate, 6(12), 2326-2340.

Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for estimating wind speed frequency distributions. Journal of applied meteorology, 17(3), 350-353.

Jaramillo, O. A., & Borja, M. A. (2004). Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case. Renewable Energy, 29(10), 1613-1630.

Justus, C. G., Hargraves, W. R., & Yalcin, A. (1976). Nationwide assessment of potential output from wind-powered generators.

Kaminsky, F. C. (1977). Four probability densities/log-normal, gamma, Weibull, and Rayleigh/and their application to modelling average hourly wind speed. In International Solar Energy Society, Annual Meeting (Vol. 1, pp. 19-6).

Kiss, P., & Jánosi, I. M. (2008). Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe. Energy Conversion and Management, 49(8), 2142-2151.

Kurban, M., Kantar, Y. M., & Hocao?lu, F. O. (2006). Rüzgar Enerjisi Potansiyelinin Ara?t?r?lmas?nda Weibull Ve Rayle?gh Da??l?m?n?n Kullan?lmas?. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(1), 14-21.

Kurban, M., Hocao?lu, F. O., & Kantar, Y. M. (2011). Rüzgar Enerjisi Potansiyelinin Tahmininde Kullan?lan ?ki Farkl? ?statistiksel Da??l?m?n Kar??la?t?rmal? Analizi. Pamukkale University Journal Of Engineering Sciences, 13(1).

Luna, R. E., & Church, H. W. (1974). Estimation of long-term concentrations using a “universal” wind speed distribution. Journal of Applied Meteorology, 13(8), 910-916.

Lysen, E. H. (1982). Introduction to wind energy. Consultancy services wind energy developing countries (CWD).

Morgan, E. C., Lackner, M., Vogel, R. M., & Baise, L. G. (2011). Probability distributions for offshore wind speeds. Energy Conversion and Management, 52(1), 15-26.

Pishgar-Komleh, S. H., Keyhani, A., & Sefeedpari, P. (2015). Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews, 42, 313-322.

Rosen, K., Van Buskirk, R., & Garbesi, K. (1999). Wind energy potential of coastal Eritrea: an analysis of sparse wind data. Solar energy, 66(3), 201-213.

Stedinger, J. R. (1980). Fitting log normal distributions to hydrologic data. Water Resources Research, 16(3), 481-490.

Stevens, M. J. M., & Smulders, P. T. (1979). The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind engineering, 3, 132-145.

Sherlock, R. H. (1951). Analyzing winds for frequency and duration. Meteorological Monographs, 4, 72-79

Takle, E. S., & Brown, J. M. (1978). Note on the use of Weibull statistics to characterize wind-speed data. Journal of Applied Meteorology, 17(4), 556-559.

Van der Auwera, L., De Meyer, F., & Malet, L. M. (1980). The use of the Weibull three-parameter model for estimating mean wind power densities. Journal of Applied Meteorology, 19(7), 819-825.

Vogel, R. M., McMahon, T. A., & Chiew, F. H. (1993). Floodflow frequency model selection in Australia. Journal of Hydrology, 146, 421-449.


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