Evaluation of Weibull parameters by different methods for farms

Abstract

One of the most prevalent clean and sustainable forms of energy produced worldwide is the power created from wind runoff. Wind turbines ought to be installed in areas with favorable circumstances to transform mechanical wind energy into electricity. Finding appropriate ways to predict the energy produced by a wind farm using the Weibull distribution is the main goal of this work. Theoretical techniques have been applied to calculate Weibull selected characteristics using experimental data gathered at the campus of Universidade Federal de Mato Grosso do Sul (UFMS) in Brazil. These data were gathered 10 meters above the surface. The effectiveness of four statistical techniques that are frequently used in the energy industry are compared: the standard energy factor method; the least squares regression method; the moment method; and the mean standard deviation method in estimating Weibull parameters. The root mean square error, Chi-square error, Kolmogorov-Smirnov test, and coefficient of determination are used to contrast the statistical methodologies. The results demonstrated that the least squares regression approach performs less well than other methods. The standard energy factor approach, the moment method, and the mean standard deviation method are the most effective techniques when modifying Weibull distribution curves for the assessment of wind speed data. The data analysis confirms that these three strategies are fully applicable if the wind speed distribution closely matches the Weibull distribution.