Abstract
This paper appraises the accuracy of methods for calculating wind power density (WPD), by comparing measurement values to the shape and scale parameters of the Weibull distribution (WD). For the estimation of WD parameters, the Graphical method (GP), Empirical method of Justus (EMJ), Empirical method of Lysen (EML), Energy pattern factor method (EPF), and Maximum likelihood method (ML) are used. The accuracy of each method was evaluated via multiple metrics: Mean absolute bias error (MABE), Mean absolute percentage error (MAPE), Root mean square error (RMSE), Relative root mean square error (RRMSE), Correlation coefficient (R), and Index of agreement (IA). The study’s objective is to select the most suitable methods to evaluate the WD parameters (k and c) for calculating WDP in four meteorological stations located in Junin-Peru: Comas, Huasahuasi, Junin, and Yantac. According to the statistical index results, the ML, EMJ, and EML methods are the most accurate for each station, however, it is important to note that the methods do not perform equally well in all stations, presumably the graphical conditions and external factors play a major role.
Highlights
Renewable power capacity is increasing at a faster annual rate than all fossil fuels combined
The objective is to find the most convenient method to estimate the accuracy of the Weibull distribution (WD), with statistical indicators being used for the performance evaluation of the methods
Station Method more fully; the three methods reveal the same tendencies for both parameters; overall, we found that the ML, Empirical method of Justus (EMJ), and Empirical method of Lysen (EML) methods have the greatest accuracy for wind density power (WDP) calculation
Summary
Renewable power capacity is increasing at a faster annual rate than all fossil fuels combined. In 2016, green energy comprised nearly 62% of the total power-generating capacity in the world, and more and more countries are using this technology [1]. A standard approach for describing velocity is via the WD, a continuous probability distribution involving the two parameters of shape and scale [3] [4] [5] [6] [7]. The WD is used across multiple research areas concerned with wind velocity, extensive literature [3]-[15] used this type of distribution
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