Comparison of wind speed distributions: a case study for Aegean coast of Turkey

References

• Ahmed, A. S. 2010. Wind energy as a potential generation source are Ras Benas, Egypt. Renew Sustain Energy Rev 4:2167–73. doi:10.1016/j.rser.2010.03.006.
   Google Scholar
• Akdag, S. A., and O. Guler. 2015. A novel energy pattern factor method for wind speed distribution parameter estimation. Energy Convers Manage 106:1124–33.
   Web of Science ®Google Scholar
• Akdag, S. A., and O. Guler. 2018. Alternative moment method for wind energy potential and turbine energy output estimation. Renew Energy 120:69–77.
   Web of Science ®Google Scholar
• Akgul, F. G., B. Senoglu, and T. Arslan. 2016. An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Convers Manage 114:234–40. doi:10.1016/j.enconman.2016.02.026.
   Web of Science ®Google Scholar
• Alavi, O., K. Mohammadi, and A. Mostafaeipour. 2016a. Evaluating the suitability of wind speed distribution models: A case of study of east and southeast parts of Iran. Energy Convers Manage 119:101–08.
   Web of Science ®Google Scholar
• Alavi, O., A. Sedaghat, and A. Mostafaeipour. 2016b. Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for Kerman, Iran. Energy Convers Manage 120:51–61. doi:10.1016/j.enconman.2016.04.078.
   Web of Science ®Google Scholar
• Altunkaynak, A., T. Erdik, I. Dabanlı, and Z. Sen. 2012. Theoretical derivation of wind power probability distribution function and applications. Applied Energy 92:809–14. doi:10.1016/j.apenergy.2011.08.038.
   Web of Science ®Google Scholar
• Arslan, T., S. Acitas, and B. Senoglu. 2017. Generalized Lindley and power Lindley distributions for modeling the wind speed data. Energy Convers Manage 152:300–11.
   Web of Science ®Google Scholar
• Bilir, L., M. Imır, Y. Devrim, and A. Albostan. 2015. An investigation on wind energy potential and small scale wind turbine performance at Incek region – Ankara, Turkey. Energy Convers Manage 103:910–23. doi:10.1016/j.enconman.2015.07.017.
   Web of Science ®Google Scholar
• Bracale, A., G. Carpinelli, and P. De Falco. 2017. A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization. Renew Energy 113:1366–77. doi:10.1016/j.renene.2017.07.012.
   Web of Science ®Google Scholar
• Brano, V. L., A. Orioli, G. Ciulla, and S. Culotta. 2011. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renew Energy 36:1026–39. doi:10.1016/j.renene.2010.09.009.
   Web of Science ®Google Scholar
• Burr, I. W. 1942. Cumulative frequency functions. Annals of Mathematical Statistics 13:215–32. doi:10.1214/aoms/1177731607.
   Google Scholar
• Carta, J. A., P. Ramirez, and S. Velazquez. 2009. A review of wind speed probability distributions used in wind energy analysis case study in the Canary Islands. Renew Sustain Energy Rev 13:933–55. doi:10.1016/j.rser.2008.05.005.
   Web of Science ®Google Scholar
• Chang, T. P. 2011. Estimation of wind energy potential using different probability density functions. Applied Energy 88:1848–56. doi:10.1016/j.apenergy.2010.11.010.
   Web of Science ®Google Scholar
• Cheng, E., and C. Yeung. 2002. Generalized extreme gust wind speeds distributions. Journal of Wind Engineering and Industrial Aerodynamics 90:1657–69. doi:10.1016/S0167-6105(02)00277-5.
   Web of Science ®Google Scholar
• Chiodo, E., and P. De Falco. 2016. Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation. Electric Power Systems Research 141:549–61. doi:10.1016/j.epsr.2016.08.028.
   Web of Science ®Google Scholar
• Gani, A., K. Mohammadi, S. Shamshirband, T. A. Altameem, D. Petkovic, and S. Ch. 2016. A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm. Environmental Progress & Sustainable Energy 35 (3):867–75.
   Web of Science ®Google Scholar
• Ghitany, M. E., D. K. Al-Mutairi, F. A. Al-Awadhi, and M. M. Al-Burais. 2012. Marshall-Olkin extended Lindley distribution and its application. International Journal of Applied Mathematics 25 (5):709–21.
   Google Scholar
• Gugliani, G. K., A. Sarkar, S. Mandal, and V. Agrawal. 2017. Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study. International Journal of Green Energy 14 (9):737–53.
   Web of Science ®Google Scholar
• Haralambos, S. B., G. Mihalakakou, S. Rehman, and L. M. Al-Hadhrami. 2016. Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function. International Journal of Green Energy 13 (7):703–14. doi:10.1080/15435075.2014.896258.
   Web of Science ®Google Scholar
• Hepbasli, A., and O. Ozgener. 2004. A review on the development of wind energy in Turkey. Renew Sustain Energy Rev 8:257–76. doi:10.1016/j.rser.2003.10.006.
   Web of Science ®Google Scholar
• Ilkilic, C. 2012. Wind energy and assessment of wind energy potential in Turkey. Renew Sustain Energy Rev 16:1165–73. doi:10.1016/j.rser.2011.11.021.
   Web of Science ®Google Scholar
• Ilkilic, C., and M. Nursoy. 2010. The potential of wind energy as an alternative source in Turkey. Energy Sources, Part A 32:450–59. doi:10.1080/15567030802612226.
   Google Scholar
• Jowder, F. A. L. 2009. Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain. Applied Energy 86:538–45. doi:10.1016/j.apenergy.2008.08.006.
   Web of Science ®Google Scholar
• Jung, C., and D. Schindler. 2017. Global comparison of the goodness-of-fit of wind speed distributions. Energy Convers Manage 133:216–34. doi:10.1016/j.enconman.2016.12.006.
   Web of Science ®Google Scholar
• Kantar, Y. M., and I. Usta. 2015. Analysis of the upper-truncated Weibull distribution for wind speed. Energy Convers Manage 96:81–88.
   Web of Science ®Google Scholar
• Khahro, S. F., K. Tabbassum, A. M. Soomro, L. Dong, and X. Liao. 2014. Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan. Energy Convers Manage 78:956–67. doi:10.1016/j.enconman.2013.06.062.
   Web of Science ®Google Scholar
• Masseran, N. 2015. Evaluating wind power density models and their statistical properties. Energy 84:533–41.
   Web of Science ®Google Scholar
• Masseran, N., A. M. Razali, K. Ibrahim, A. Zaharim, and K. Sopian. 2013. The probability distribution model of wind speed over East Malaysia. Research Journal of Applied Sciences, Engineering and Technology 6 (10):1774–79. doi:10.19026/rjaset.6.3902.
   Google Scholar
• Mielke, P. W. 1973. Another family of distributions for describing and analyzing precipitation data. Journal of Applied Meteorology 12:275–80.
   Google Scholar
• Mohammadi, K., O. Alavi, and J. G. McGowan. 2017. Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review. Energy Convers Manage 143:109–22. doi:10.1016/j.enconman.2017.03.083.
   Web of Science ®Google Scholar
• Morgan, E. C., M. Lackner, R. M. Vogel, and L. G. Baise. 2011. Probability distributions for offshore wind speeds. Energy Convers Manage 52:15–26.
   Web of Science ®Google Scholar
• Mudholkar, G. S., and D. K. Srivastava. 1993. Exponentiated Weibull family for analyzing bathtub failure rate data. IEEE Transactions on Reliability 42 (2):299–302. doi:10.1109/24.229504.
   Web of Science ®Google Scholar
• Oliveira, M. M. F., N. F. F. Ebecken, J. L. F. Oliveira, and F. Gilleland. 2011. Generalized extreme wind speed distributions in South America over the Atlantic Ocean region. Theor Appl Climatol 104:377–85. doi:10.1007/s00704-010-0350-3.
   Web of Science ®Google Scholar
• Ouarda, T. B. M. J., C. Charron, and F. Chebana. 2016. Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study. Energy Convers Manage 124:247–65.
   Web of Science ®Google Scholar
• Ouarda, T. B. M. J., C. Charron, J.-Y. Shin, P. R. Marpu, A. H. Al-Mandoos, M. H. Al-Tamimi, H. Ghedira, and T. N. Al Hosary. 2015. Probability distributions of wind speed in the UAE. Energy Convers Manage 93:414–34. doi:10.1016/j.enconman.2015.01.036.
   Web of Science ®Google Scholar
• Ozay, C., and M. S. Celiktas. 2016. Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region. Energy Convers Manage 121:49–54.
   Web of Science ®Google Scholar
• Ozerdem, B., and M. Turkeli. 2003. An investigation of wind characteristics on the campus of Izmir Institute of Technology, Turkey. Renewable Energy 28:1013–27.
   Web of Science ®Google Scholar
• Oztopal, A., A. D. Sahin, N. Akgun, and Z. Sen. 2000. On the regional wind energy potential of Turkey. Energy 25:189–200.
   Web of Science ®Google Scholar
• Rocha, P. A. C., R. C. de Sousa, C. F. de Andrade, and M. E. V. Da Silva. 2012. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy 89:395–400. doi:10.1016/j.apenergy.2011.08.003.
   Web of Science ®Google Scholar
• Senel, B., M. Senel, and L. Bilir. 2014. Role of wind power in the energy policy of Turkey. Energy Technology & Policy 1:123–30.
   Google Scholar
• Shamshirband, S., J. Iqbal, D. Petkovic, and M. A. Mirhashemi. 2014. Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology. Advances in Engineering Software 76:148–53.
   Web of Science ®Google Scholar
• Shu, Z. R., Q. S. Li, and P. W. Chan. 2015. Statistical analysis of wind characteristics and wind energy potential in Hong Kong. Energy Convers Manage 101:644–57. doi:10.1016/j.enconman.2015.05.070.
   Web of Science ®Google Scholar
• Ucar, A., and F. Balo. 2009. Evaluation of wind energy potential and electricity generation at six locations in Turkey. Applied Energy 86:1864–72.
   Web of Science ®Google Scholar
• Usta, I. 2016. An innovative estimation method regarding Weibull parameters for wind energy applications. Energy 106:301–14. doi:10.1016/j.energy.2016.03.068.
   Web of Science ®Google Scholar
• Xiao, Y. Q., Q. S. Li, Z. N. Li, Y. W. Chow, and G. Q. Li. 2006. Probability distributions of extreme wind speed and its occurrence interval. Engineering Structures 28:1173–81.
   Web of Science ®Google Scholar