Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6911
Title: Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools
Authors: Badawi, Ahmed 
Yusoff, Siti Hajar 
Zyoud, Alhareth 
Khan, Sheroz 
Hashim, Aisha 
Uyaroğlu, Yılmaz 
Ismail, Mahmoud 
Keywords: Winds - Speed - Measurement;Average wind speed;Wind power;Cumulative distribution;Probability distribution function;Numerical analysis;Statistical tools;Weibull distribution;Parameter estimation.;Wind energy
Issue Date: Jun-2021
Publisher: institute of advanced engineering and science
Journal: International Journal of Power Electronics and Drive Systems (IJPEDS) 
Abstract: This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X2 ), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy
URI: http://hdl.handle.net/20.500.11889/6911
DOI: 10.11591/ijpeds.v12.i2.pp1114-1130
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