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Accurate estimation of T year extreme wind speeds by considering different model selection criterions and different parameter estimation methods

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  • Tosunoğlu, Fatih

Abstract

Accurate estimation of extreme wind speeds for different return periods is necessary to avoid extensive costs or large damages. To achieve this aim, the probability distribution of the wind speed data should be well defined and its parameters should be more precisely estimated. In this study, the commonly used probability distributions, including Gamma, Generalized Extreme Value, Logistic, Lognormal, Normal and Weibull, are fitted to annual maximum wind speed data in Turkey. Parameters of the fitted distributions are estimated using method of moments (MOM), method of maximum likelihood (MLM) and method of probability weighted moments (PWMs). Based on various model selection criterions (Akaike Information Criterion, Bayesian Information criterion, Anderson-Darling, Cramér-von-Mises, and Kolmogorov–Smirnov tests), the Generalized Extreme Value and Logistic, which provided the best fit for 40% and 30% of the series, respectively, were mostly found to be the most suitable distributions. Additionally, the Lognormal, Normal and Gamma distributions showed the best fit for 15%, 10% and 5% of the series, respectively. Moreover, the MLM and PWMs provided better parameter estimations for 57% and 30% the best fitted distributions, respectively. Furthermore, wind speed quantiles with the standard errors in various return periods were estimated using the best fitted distributions.

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  • Tosunoğlu, Fatih, 2018. "Accurate estimation of T year extreme wind speeds by considering different model selection criterions and different parameter estimation methods," Energy, Elsevier, vol. 162(C), pages 813-824.
    • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:813-824
    DOI: 10.1016/j.energy.2018.08.074
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    1. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    2. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    3. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    4. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.
    5. Safari, Bonfils, 2011. "Modeling wind speed and wind power distributions in Rwanda," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 925-935, February.
    6. Carta, José A. & Velázquez, Sergio, 2011. "A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site," Energy, Elsevier, vol. 36(5), pages 2671-2685.
    7. Fatih Tosunoglu & Ibrahim Can, 2016. "Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(3), pages 1457-1477, July.
    8. IlkIlIç, Cumali & AydIn, Hüseyin & Behçet, Rasim, 2011. "The current status of wind energy in Turkey and in the world," Energy Policy, Elsevier, vol. 39(2), pages 961-967, February.
    9. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    10. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    11. Kose, Ramazan & Ozgur, M. Arif & Erbas, Oguzhan & Tugcu, Abtullah, 2004. "The analysis of wind data and wind energy potential in Kutahya, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(3), pages 277-288, June.
    12. Gökçek, Murat & Bayülken, Ahmet & Bekdemir, Şükrü, 2007. "Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey," Renewable Energy, Elsevier, vol. 32(10), pages 1739-1752.
    13. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
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