Determination of Weibull Coefficients for Hatay Region by Polynomial Curve Fitting in Matlab

Today's ever-increasing energy demands necessitate the development of new energy sources. Renewable energy sources, in particular, have emerged as a critical source of energy for both industrialized and developing countries. Wind energy is one of the most important forms of renewable energy, however the constant variance in wind speed raises several concerns. The wind energy potential of the Hatay region was assessed in this study. The most essential factor for Hatay's selection is the region's high wind energy investments due to its wind potential, as contrasted to the actual wind potential. By using the wind data obtained from the general directorate of meteorology, the potential of the selected region in terms of wind energy has been evaluated. The coefficients of the Weibull distribution function were calculated using polynomial curve fitting in Matlab. The average wind speed of the region was estimated and using these coefficients, the average wind power of the selected region was determined. The performance of this method was evaluated using various statistical error analysis methods and the findings were compared with actual wind speed data.

Anahtar Kelimeler:

Weibull Distribution, Polynomial Curve Fitting, Statistical Test, Renewable Energy, Wind Energy.

Determination of Weibull Coefficients for Hatay Region by Polynomial Curve Fitting in Matlab

Keywords:

Weibull Distribution, Polynomial Curve Fitting, Statistical Test, Renewable Energy, Wind Energy.,

PDF

___

Ahmet Shata, S.A., & Hanitsch, R. (2006). Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt. Renewable Energy, 31, 1183–1202.
Akdağ, S.A., & Dinler A. (2009). A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management, 50, 1761-1766.
Azad, A.K., Rasul, M.G., Alam, M.M., Uddin, S.M.A., & Mondal, S.K., (2014). Analysis of Wind Energy Conversion System Using Weibull Distribution. Procedia Engineering., 90, 725–732.
Bilgili, M., & Şahin, B. (2005). The finding of weibull parameters at the determination of Wind Power density. New and Renewable Energy / Energy Management Symposium, Kayseri, 229-234.
Bilir, L., İmir, M., Devrim, Y., & Albostan A., (2015). Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function. International Journal of Hydrogen Energy, http://dx.doi.org/10.1016/j.ijhydene.2015.04.140,
Chang, T.P., (2011). Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 88, 272–282.
Capika, M, Yılmaz, A.O., & Cavusoglu, I., (2012). Present situation and potential role of renewable energy in Turkey. Renewable Energy, 46, 1-13.
Freitas de Andrade, C., Maia Neto, H. F., Costa Rocha, P. A., & Vieira da Silva, M. E. (2014). 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 Conversion and Management, 86, 801–808,
Gabbasa, M., Sopian, K., Yaakob, Z., Zonooz, M., Fudholi, A., & Asim N. (2013). Review of the energy supply status for sustainable development in the Organization of Islamic Conference. Renewable and Sustainable Energy Reviews, 28, 18–28.
Kantar, Y.M., & Usta, I., (2008). Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function. Energy Conversion and Management, 49, 962–973.
Kaplan, Y. A. (2017). Determination of the best Weibull methods for wind power assessment in the southern region of Turkey. IET Renewable Power Generation, 11, 175-182.
Kaplan, Y.A. (2015). Overview Of Wind Energy In The World And Assesment of Current Wind Energy Policies in Turkey. Renewable and Sustainable Energy Reviews, 43 C, 562-568.
Kim, J.S., Yum, B.J., (2008). Selection Between Weibull and Lognormal Distributions: A Comparative Simulation Study. Computational Statistics & Data Analysis, 53, 477-485.
Kose, R., Arif, M.O., Erbas, O., & Tugcu, A. (2004). The analysis of wind data and wind energy potential in Kutahya, Turkey. Renewable and Sustainable Energy Reviews, 8, 277–288.
Rocha, P.A.C.R., Sousa, R.C.D., Andrade, C.F.D, & Silva M.E.V.D. (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.
Yang, W.Y., Cao, W., Chung, T.S., & Morris, J. (2005). Applied Numerical Methods Using Matlab. Wiley. Yıldırım, U., Gazibey, Y., Güngör, A., (2012). Wind Energy Potential of Niğde. Journal of Niğde University, 1, 37-47.