Abstract

For the wind energy assessment, it is important to know the probability density distribution of the wind speed in order to calculate the mean power of a wind. In this study wind data, for the period from January 2000 to December 2002, hourly records of speed and direction at Alexandria coast are analyzed as random monthly observations. Statistical methods are used for determination of best fit random distributions of wind speed and direction using statistical standard software that helps in evaluation of wind speed energy potential in a study area. In the present paper, different theoretical Probability Density Functions (PDF) have been tried to determine the appropriate models, including Weibull distribution and their parameters. The minimum wind power density at Alexandria found in July and August (2000-2002) were 20.41 and 21.34 w/m² and the maximum was in December and January (110.81 and 123.34 w/m²). The most important distributions of the wind speed were Weibull and Dagum and the direction followed mostly the Johnson SB model.

Keywords:

Alexandria Egypt, probability modelling, wind energy, wind power density,

References

1. Aras, H., V. Yilmaz and H.E. Çelik, 2003. Estimation of monthly wind speeds of eskisehir, Turkey. Proceeding of the 1st International Exergy, Energy and Environment Symposium. Hotel Princess, Izmir, Turkey, July 13-17.
   
2. Baran, S., D. Nemoda and A. Horanyi, 2013. Probabilistic wind speed forecasting in Hungary. Meteorol. Z., 22(3): 273-282.
   CrossRef    
3. Chang, T.P., 2010. Wind speed and power density analyses based on mixture Weibull and maximum entropy distributions. Int. Appl. Sci . Eng., 8(1): 39-46.
   
4. Lun, I.Y.F. and J.C. Lan, 2000. A study of Weibull parameters using long-term wind observations. Renew. Energ., 20: 145-153.
   CrossRef    
5. Mortensen, N.G., U.S. Said and J. Badger, 2006. Wind atlas for Egypt. Proceeding of the 3rd Middle East-North Africa Renewable Energy Conference (MENAREC' 06). Cairo, Egypt, pp: 1-12.
   
6. MRCC (Marine Research and Consultation Centre), 1993. The statistical annual report, activities of the Egyptian harbors and Suez Canal during 1992. The Arab Academy for Science and Technology and Maritime Transport, Part 2, pp: 10.
   
7. Quine, C.P., 2000. Estimation of mean wind climate and probability of strong winds for wind risk assessment. Int. J. Forest Res., 73(3): 247-258.
   CrossRef    
8. Sabra, F.A., 1979. Wave and surge forecasting along the Egyptian coast of the Mediterranean. M.Sc. Thesis, Faculty of Science, Alexandria University.
   
9. Shalaby, M.M., 1999. Waves and surge forecasting along the Egyptian coast of the Mediterranean Sea. M.Sc. Thesis, Arab Academy for Science and Technology and Maritime Transport, Alexandria, Egypt, pp: 1-123.
   
10. Toure, S., 2005. Investigations on the Eigen-coordinates method for the 2-parameter Weibull distribution of wind speed. Renew. Energ., 30: 511-521.
    CrossRef    
11. Yilmaz, V. and H.E. Çelik, 2008. A statistical approach to estimate the wind speed distribution: the case of gelibolu region. Dogus Üniv., Dergisi, 9(1): 122-132.
    
12. Zhou, W. and H.F. Yang, 2006. Wind power potential and characteristic analysis of the Pearl River Delta region, China. Renew. Energ., 31: 739-753.
    CrossRef    

Competing interests

The authors have no competing interests.

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