참고문헌
- Akdag, S. and Guler, O. (2009), "Calculation of wind energy potential and economic analysis by using Weibull distribution-a case study from Turkey. Part 1: Determination of Weibull Parameters", Energy sources part B-economics planning and policy, 4, 1-8. https://doi.org/10.1080/15567240802532841.
- Akdag, S.A., Bagiorgas, H.S. and Mihalakakou, G. (2010), "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean", Appl. Energy, 87(8), 2566-2573. https://doi.org/10.1016/j.apenergy.2010.02.033.
- Akpinar, S. and Akpinar, E.K. (2007), "Wind energy analysis based on maximum entropy principle (MEP)-type distribution function", Energ. Convers. Manage., 48(4), 1140-1149. https://doi.org/10.1016/j.enconman.2006.10.004.
- Ali, S., Lee, S.M. and Jang, C.M. (2018), "Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island - Incheon, South Korea", Renew. Energ., 123, 652-663. https://doi.org/10.1016/j.renene.2018.02.087.
- Alizadeh, M., Cordeiro, G.M., Nascimento, A.D.C., Lima, M.C.S. and Ortega, E.M.M. (2017), "Odd-Burr generalized family of distributions with some applications", J. Stat. Comput. Sim., 87(2), 367-389. https://doi.org/10.1080/00949655.2016.1209200.
- Altun, G., Alizadeh, M. Altun, E. and Ozel, G. (2017), "Odd Burr Lindley distribution with properties and applications", Hacettepe J. Math. Stat., 46(2), 255-276.
- Alzaatreh, A., Lee, C. and Famoye, F. (2013), "A new method for generating families of continuous distributions", Metron, 71, 63-79. https://doi.org/10.1007/s40300-013-0007-y
- Arik, I. (2018), "New distribution families and their statistical properties for survival analysis", Ph.D. Dissertation; Anadolu University, Eskisehir, Turkey.
- Arik, I. and Kantar, Y.M. (2019), "New Odd Burr-Rayleigh distribution: theory and applications", Far East J. Theor. Stat., 55(1), 53-82. https://doi.org/10.17654/TS055010053
- Chalamcharla, S.C.V. and Doraiswamy, I.D. (2016), "Mathematical modeling of wind power estimation using multiple parameter Weibull distribution", Wind Struct., 23(4), 351-366. https://doi.org/10.12989/was.2016.23.4.351.
- Chang T.P. (2011), "Estimation of wind energy potential using different probability density functions", Appl. Energ., 88(5), 1848-1856. https://doi.org/10.1016/j.apenergy.2010.11.010.
- Hu, B., Li, Y., Yang H. and Wang H. (2017), "Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems", J. Modern Power Syst. Clean Energy, 5(2), 220-227. https://doi.org/10.1007/s40565-015-0172-5
- Kantar, Y.M. and Usta, I. (2008), "Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function", Energ. Convers. Manage., 49(5), 962-973. https://doi.org/10.1016/j.enconman.2007.10.008.
- Kantar, Y.M. and Senoglu, B. (2008), "A comparative study for the location and scale parameters of the Weibull distribution with given shape parameter", Comput. Geosci., 34(12), 1900-1909. https://doi.org/10.1016/j.cageo.2008.04.004.
- Kantar, Y.M. and Usta, I. (2015), "Analysis of the upper-truncated Weibull distribution for wind speed", Energ. Convers. Manage., 96, 81-88. https://doi.org/10.1016/j.enconman.2015.02.063.
- Kantar, Y.M., Usta, I., Arik, I. and Yenilmez, I. (2018), "Wind speed analysis using the Extended Generalized Lindley Distribution", Renew. Energ., 118, 1024-1030. https://doi.org/10.1016/j.renene.2017.09.053.
- Mohammadi, K., Alavi, O. and McGowan, J.G. (2017), "Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review", Energ. Convers. Manage, 143, 109-122. https://doi.org/10.1016/j.enconman.2017.03.083.
- Morgan, C.E., Lackner, M., Vogel, M.R. and Baise G.L. (2011), "Probability distributions for offshore wind speeds", Energ. Convers. Manage., 52(1), 15-26. https://doi.org/10.1016/j.enconman.2010.06.015.
- Philippopoulos, K., Deligiorgi, D. and Karvounis, G. (2012), "Wind speed distribution modeling in the Greater Area of Chania", Greece, Int. J. Green Energy, 9(2), 174-193. https://doi.org/10.1080/15435075.2011.622020.
- Safari, B. and Gasore, J. (2010), "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda", Renew Energ., 35(12), 2874-2880. https://doi.org/10.1016/j.renene.2010.04.032.
- Sedghi, M., Hannani, S.K. and Boroushaki, M. (2015), "Estimation of weibull parameters for wind energy application in Iran's cities", Wind Struct., 21(2), 203-221. http://dx.doi.org/10.12989/was.2015.21.2.203
- Seshaiah, C.V. and Sukkiramathi, K. (2016), "A mathematical model to estimate the wind power using three parameter Weibull distribution", Wind Struct., 22(4), 393-408. https://doi.org/10.12989/was.2016.22.4.393.
- Soholi, V., Gupta, S. and Nema, R. (2016), "A comparative analysis of wind speed probability distributions for wind power assessment of four sites", Turk. J. Elec. Eng. & Comp. Sci., 24, 4724-4735. https://doi.org/10.3906/elk-1412-207
- Soulouknga, M.H., Doka, S.Y., Revanna, N., Djongyang, N. and Kofane, T.C. (2018), "Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution", Renew. Energ., 121, 1-8. https://doi.org/10.1016/j.renene.2018.01.002.
- Soukissian, T. (2013), "Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution", Appl. Energ., 111, 982-1000. https://doi.org/10.1016/j.apenergy.2013.06.050.
- Usta, I. and Kantar, Y.M. (2012), "Analysis of some flexible families of distributions for estimation of wind speed distributions", Appl. Energ., 89(1), 355-367. https://doi.org/10.1016/j.apenergy.2011.07.045.
- Usta, I. and Kantar Y.M. (2016), "Wind power potential estimation by using different statistical distributions", DEU Muh. Fak. Fen ve Muh. Dergisi, 18(3), 362-380. https://doi.org/10.21205/deufmd.2016185407
- Usta, I., Arik, I., Kantar, Y.M. and Yenilmez, I. (2018), "A new estimation approach based on moments for estimating Weibull parameters in wind power applications", Energ. Convers. Manage., 164, 570-578. https://doi.org/10.1016/j.enconman.2018.03.033.
- WASA (2018), Wind Atlas for South Africa; Department: Energy Republic of South Africa, South Africa, http://wasadata.csir.co.za/wasa1/WASAData.
- Zhou, J., Erdem, E., Li G. and Shi J. (2010), "Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites", Energ. Convers. Manage., 51(7), 1449-1458. https://doi.org/10.1016/j.enconman.2010.01.020.