Statistical distribution and energy estimation of the wind speed at Saint Martin’s Island, Bangladesh


  • Khandaker Dahirul Islam School of Renewable Energy (SCORE), Maejo University, Chiang Mai 50290
  • Tanate Chaichana School of Renewable Energy (SCORE), Maejo University, Chiang Mai 50290
  • Natthawud Dussadee School of Renewable Energy (SCORE), Maejo University, Chiang Mai 50290
  • Akarin Intaniwet School of Renewable Energy (SCORE), Maejo University, Chiang Mai 50290


Wind speed, statistical probability distribution function, wind energy, performance measurement


This paper describes some statistical probability distribution functions (pdf) which have been used to measure wind power and energy potential of Saint Martin’s Island situated at 13 kilometers apart from the southern-most tip of Bangladesh. One-year measured wind speed data at 60 meters above ground level (agl) are analyzed in the research with some well-known statistical probability density functions (pdf) namely, Normal distribution, Weibull distribution, Gamma distribution, and Rayleigh distribution. The best distribution technique among the four for the wind speed data of the Island has been measured, and the Root Mean Square Error (RMSE) and Mean Bias Error (MBE) are the methods that have been employed throughout the study in order to select the best distribution function. Results for the one-year time-series wind speed data (October 2014-October 2015) recorded every minute of Saint Martin’s Island as per RMSE and MBE performance test revealed that, Weibull probability distribution, whose RMSE and MBE values have been measured to be 1.43 and 0.72 respectively proved to be the best distribution function for the wind power estimation and analysis. Rayleigh distribution follows Weibull pdf in this research. The shape (k) and scale parameters (c) for Weibull and Rayleigh pdf have been calculated using Empirical Method (EM) for the research which are 1.70 and 4.90 m/s respectively for Weibull pdf, where the scale parameter for Rayleigh pdf whose defined shape parameter value is 2, is to be measured as 4.92 m/s. Annual Energy Production (AEP) for Saint Martin’s Island has been measured as 5997 kWh using frequency distribution (histogram).


US Dept. of Energy, Wind powering America; September 2009.

Bonfils Safari, “Renewable and Sustainable Energy Reviews,” 15 (2011) 925–935,

Eugene C. Morgan a, Matthew Lackner, Richard M. Vogel and Laurie G. Baise, “Probability distributions for offshore wind speeds,” Energy Conversion and Management, 52 (2011) 15–26.

Musgrave PJ, “Wind energy conversion: recent progress and future prospects,” Sol Wind Technology, 4(1) (1987) 37–49.

Joselin Herbert GM, Iniyan S, Sreevalsan E and Rajapandian S. “A review of wind energy technologies,” Renew Sustain Energy, 11 (2007) 1117–45.

Thirugnanasambandam M, Iniyan S and Ranko G, “A review of solar thermal technologies,” Renew Sustain Energy, 14(1) 2010 312–22.

Liu L-q, Wang Z-x, Zhang H-q and Xue Y-c, “Solar energy development in China – a review,” Renew Sustain Energy, 14(1) 2010 301–11.

Manwell JF, McGowan JG and Rogers AL, “Wind energy explained: theory, design and application” (2002).

Lackner MA, Rogers AL and Manwell JF, “Uncertainty analysis in MCP-based wind resource assessment and energy production estimation,” In: Journal of solar energy engineering – transactions of the ASME: AIAA 45th aerospace sciences meeting and exhibit, 130 (2008).

Harris RI, “Generalised pareto methods for wind extremes. useful tool or mathematical mirage,” J Wind Eng Ind Aerodyn, 93(5) (2005) 341–60. doi:10.1016/j.jweia.2005.02.004.

Harris RI, “Errors in gev analysis of wind epoch maxima from Weibull parents,” Wind Struct, 9(3) (2006) 179–91.

Ramirez P and Carta JA. “Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study,” Energy Convers Manage 2005;46(15–16):2419–38

Celik AN, “A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey,” Renew Energy, 29(4) 2004 593–604.

Carta JA, Ramirez P and Velazquez S, “Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation,” Energy Convers Manage, 49(10) (2008) 2647–2655.

Justus CG and Mikhail A, “Height variation of wind speed and wind distribution statistics,” Geophys Res Lett, 3 (1976) 261–264.

Gokcek M, Bayulken A and Bekdemir S, “Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey,” Renew Energy, 32 (2007) 1739–1752.

Ahmed, S., “Investigation and analysis of Wind Pumping system for Irrigation in Bangladesh”, M.Sc Thesis, BUET, Dhaka, (2002).

Safari B and Gasore J, “A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda,” Renew Energy, (2010), doi:10.1016/j.renene.2010.04.032.

D.K. Kidmo, R. Danwe, N. Djongyang and S.Y. Doka, “Performance Assessment of Two-parameter Weibull Distribution Methods for Wind Energy Applications in the District of Maroua in Cameroon,” International Journal of Sciences: Basic and Applied Research, 17(1) (2014) 39–59.

A.K. Azad, M.G. Rasul and T. Yusaf, “Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications”, Energies, 7(5) (2014) 3056–3085.

G. Al Zohbi, P. Hendrick et P. Bouillard, “Evaluation du Potentiel d’Energie Eolienne au Liban’,” Revue des Energies Renouvelables, 17(1) (2014) 83 – 96.

J. A. Davies and D. C. Mckav, “Evaluation of Selected Models for Estimating Solar Radiation on Horizontal Surfaces,” Solar Energy, 43(3) (1989) 153-168.

E.O. Falayi, J.O. Adepitan, and A.B. Rabiu, “Empirical Models for the Correlation of Global Solar Radiation with Meteorological Data for Iseyin, Nigeria,” International Journal of Physical Sciences, 3 (9) (2008) 210-216 Available online at ISSN 1992 - 1950 © 2008 Academic Journals.

Sathyajit Mathew, “Wind Energy – Fundamentals, Resource Analysis and Economics,” Sringer-Verlag Berlin, Heidelburg – (2006), ISBN – 13: 978-3-540-30905-5.




How to Cite

Islam, K. D., Chaichana, T., Dussadee, N., & Intaniwet, A. (2017). Statistical distribution and energy estimation of the wind speed at Saint Martin’s Island, Bangladesh. Journal of Renewable Energy and Smart Grid Technology, 12(1), 77–88. Retrieved from