Analysis of Wind Energy Prospect for Power Generation by Three Weibull Distribution Methods

Abstract Wind energy is one of the fastest growing sectors in renewable energy. These energy resources are freely available throughout the world. It is one of the zero emission energy sources. The wind energy is used mainly for two purposes namely irrigation (water pumping) and electricity generation. The prospect of wind energy can be analyzed by different methods. Weibull distribution method is one of the widely acceptable methods for estimating wind energy. In this paper, the prospect of wind energy in Bangladesh is analyzed by Weibull distribution method. The data were collected from Meteorological Department of Bangladesh located in different areas of the country. Three different Weibull distribution methods were used to find out Weibull parameters which were verified using different widely acceptable statistical tools. Relative percentage error, chi-square error, analysis of variance etc. are the efficient statistical tools to rank the method which was used in this study. The study identified the most prospects windy site by applying efficient least square method with minimum error.

[1]  Olayinka S. Ohunakin,et al.  Assessment of wind energy potential and the economics of wind power generation in Jos, Plateau State, Nigeria , 2012 .

[2]  T. Chang.  Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[3]  Abul Kalam Azad,et al.  Wind Power: A Renewable Alternative Source of Green Energy , 2012 .

[4]  Mohammad. Rasul,et al.  Analysis of Wind Energy Conversion System Using Weibull Distribution , 2014 .

[5]  Ali Mostafaeipour,et al.  Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran , 2013 .

[6]  Frede Blaabjerg,et al.  Wind farm—A power source in future power systems , 2009 .

[7]  Radian Belu,et al.  Wind characteristics and wind energy potential in western Nevada , 2009 .

[8]  J. A. Carta,et al.  The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study , 2006 .

[9]  J. C. Lam,et al.  A study of Weibull parameters using long-term wind observations , 2000 .

[10]  Mohammad. Rasul,et al.  Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications , 2014 .

[11]  Australia,et al.  Energy Scenario: Production, Consumption and Prospect of Renewable Energy in Australia , 2014 .

[12]  E. Akpinar,et al.  A statistical analysis of wind speed data used in installation of wind energy conversion systems , 2005 .

[13]  Carla Freitas de Andrade,et al.  Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil , 2012 .

[14]  Abul Kalam Azad.  Statistical Weibull’s Distribution Analysis for Wind Power of The Two Dimensional Ridge Areas , 2013 .

[15]  Abul Kalam Azad,et al.  Wind Power for Electricity Generation in Bangladesh , 2013 .

[16]  D. Munz,et al.  Estimation procedure for the Weibull parameters used in the local approach , 1992, International Journal of Fracture.

[17]  Hao Chen,et al.  Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models , 2014 .