access icon free Determination of the best Weibull methods for wind power assessment in the southern region of Turkey

In this study, wind energy potential in the South of Turkey was investigated statistically by using the Turkish State Meteorological Service's hourly wind speed data between 2009 and 2013. The wind data used in this study were gathered from the Meteorology Station in Hatay and Osmaniye. In this study, different numerical methods were analysed and their performances were compared for effectiveness in determining the shape ‘k’ and scale ‘c’ parameters of the Weibull distribution function for two different regions. Six different methods: namely, graphical method, empirical method, maximum likelihood method, energy trend method, energy pattern method and moment method were used to estimate the Weibull parameters. The following statistical indicators were used for comparing the efficiency of all the methods used: the root mean square error, analysis of variance (R 2) and mean percentage error. Wind power densities were also calculated for all numerical methods used in this study. The power density is the key issue for the suitable use of wind energy. The calculated power densities for all methods used were compared with wind power density derived from measured wind data for two regions.

Inspec keywords: method of moments; mean square error methods; wind power plants; maximum likelihood estimation; Weibull distribution

Other keywords: wind power assessment; maximum likelihood method; Meteorology Station; numerical methods; Osmaniye; energy pattern method; Turkish State Meteorological Service; empirical method; energy trend method; Weibull parameters; graphical method; Weibull distribution function; wind energy; Weibull methods; wind power densities; Hatay; moment method; Turkey; mean percentage error; statistical indicators; root mean square error

Subjects: Other topics in statistics; Wind power plants; Interpolation and function approximation (numerical analysis)

References

    1. 1)
      • 19. Kım, J., -Yum, B.: ‘Selection between Weibull and lognormal distributions: a comparative simulation study’, Comput. Stat. Data Anal., 2008, 53, (2), pp. 477485.
    2. 2)
      • 7. Bassyouni, M., Gutub, S.A., Javaid, U., et al: ‘Assessment and analysis of wind power resource using Weibull parameters.’, Energy Explor. Exploit., 2015, 33, (1), pp. 105122.
    3. 3)
      • 16. Islam, M.R., Saidur, R., Rahim, N.A.: ‘Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function’, Energy, 2011, 36, (2), pp. 985992.
    4. 4)
      • 28. Talha, A., Bulut, Y.M., Yavuz, A.: ‘Comparative study of numerical methods for determining Weibull parameters for wind energy potential’, Renew. Sustain. Energy Rev., 2014, 40, pp. 820825.
    5. 5)
      • 20. Ahmet, Shata, S.A., Hanitsch, R.: ‘Evaluation of wind energy potential and electricity generation on the coast of mediterranean sea in Egypt’, Renew. Energy, 2006, 31, pp. 11831202.
    6. 6)
      • 9. Khan, J.K., Ahmed, F., Uddin, Z., et al: ‘Determination of Weibull parameter by four numerical methods and prediction of wind speed in Jiwani (Balochistan)’, J. Basic Appl. Sci., 2015, 11, pp. 6268.
    7. 7)
      • 23. Mohammadi, K., Mostafaeipour, A.: ‘Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran’, Energy Convers. Manage., 2013, 65, pp. 463470.
    8. 8)
      • 24. Basu, B., Tiwarı, D., Kundu, D., et al: ‘Is Weibull distribution the most appropriate statistical strength distribution for brittle materials?’, Ceram. Int., 2009, 35, (1), pp. 237246.
    9. 9)
      • 17. Chang, T.P.: ‘Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application’, Appl. Energy, 2011, 88, pp. 272282.
    10. 10)
      • 12. Rehman, S., Mahbub Alam, A.M., Meyer, J. P., et al: ‘Wind speed characteristics and resource assessment using Weibull parameters’, Int. J. Green Energy, 2012, 9, (8), pp. 800814.
    11. 11)
      • 26. Rehman, S., Naif, A.: ‘Wind power characteristics on the northwest coast of Saudi Arabia.’, Energy Environ., 2009, 21, (8), pp. 12571269.
    12. 12)
      • 5. GWEC (Global Wind Energy Council), Global wınd 2014 report, March, 2015.
    13. 13)
      • 1. Kaplan, Y.A.: ‘Overview of wind energy in the world and assessment of current wind energy policies in turkey’, Renew. Sustain. Energy Rev., 2015, 43 C, pp. 562568.
    14. 14)
      • 2. Kaplan, Y.A., San, I.: ‘Current situation of wind energy in the world and turkey’. Green Energy Conf.-VI (IGEC-VI), Eskisehir, Turkey, 2011.
    15. 15)
      • 13. Azad, A.K., Rasul, M.G., Yusaf, T.: ‘Statistical diagnosis of the best Weibull methods for wind power assessment for agricultural applications’, Energies, 2014, 7, pp. 30563085.
    16. 16)
      • 18. Bilgili, M., ve Şahin, B.: ‘The finding of Weibull parameters at the determination of Wind Power density’. New and Renewable Energy/Energy Management Symp., Kayseri, 2005, pp. 229234.
    17. 17)
      • 10. Rocha, P.A.C.R., Sousa, R.C.D., Andrade, C.F.D., et al: ‘Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil’, Appl. Energy, 2012, 89, pp. 395400.
    18. 18)
      • 14. Kaoga, D.K., Sergeb, D.Y., Raidandic, D., et al: ‘Performance assessment of two-parameter Weibull distribution methods for wind energy applications in the district of Maroua in Cameroon’, Int. J. Sci., Basic Appl. Res. (IJSBAR), 2014, 17, (1), pp. 3959.
    19. 19)
      • 25. Yıldırım, U., Gazibey, Y., Güngör, A.: ‘Wind energy potential of Niğde’, J. Niğde Univ., 2012, 1, (2), pp. 3747.
    20. 20)
      • 11. Freitas de Andrade, C., Maia Neto, H.F., Costa Rocha, P.A., et al: ‘An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: a new approach applied to the northeast region of Brazil’, Energy Convers. Manage., 2014, 86, (10), pp. 801808.
    21. 21)
      • 22. Morgan, E.C., Lackner, M., Vogal, R.M., et al: ‘Probability distributions of offshore wind speeds’, Energy Convers. Manage., 2011, 52, pp. 1526.
    22. 22)
      • 15. Kantar, Y.M., Usta, I.: ‘Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function’, Energy Convers. Manage., 2008, 49, pp. 962973.
    23. 23)
      • 27. Gokcek, M., Bayulken, A., Bekdemir, S.: ‘Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey’, Renew. Energy, 2007, 32, pp. 17391752.
    24. 24)
      • 8. Akdağ, S.A., Dinler, A.: ‘A new method to estimate Weibull parameters for wind energy applications’, Energy Convers. Manage., 2009, 50, pp. 17611766.
    25. 25)
      • 6. Pishgar-Komleh, S.H., Keyhani, A., Sefeedpari, P.: ‘Wind speed and power density analysis based on Waybill and Rayleigh distributions (a case study: Firouzkooh county of Iran)’, Renew. Sustain. Energy Rev., 2015, 42, pp. 313322.
    26. 26)
      • 21. Kose, R., Arif, M.O., Erbas, O., et al: ‘The analysis of wind data and wind energy potential in Kutahya, Turkey’, Renew. Sustain. Energy Rev., 2004, 8, pp. 277288.
    27. 27)
      • 3. Çapika, M., Yılmaz, A.O., Çavusoglu, I.: ‘Present situation and potential role of renewable energy in Turkey’, Renew. Energy, 2012, 46, pp. 113.
    28. 28)
      • 4. Gabbasa, M., Sopian, K., Yaakob, Z., et al: ‘Review of the energy supply status for sustainable development in the organization of Islamic conference’. Renewable and Sustainable Energy Reviews, 2013, vol. 28, pp. 1828.
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