Probability Distributions of Wind Speed in the Campo Grande, Ms, Brazil

Abstract

The objective of this work was to evaluate the adjustment of data of average wind velocity to Campo Grande, State of Mato Grosso do Sul, to the distributions of probability of: Weibull (W2), Ralyeigh (RAY), Log-Logistic (LL), Inverse Gaussian (IG), Normal (N), Gamma (G), Generalized extreme value (GEV), Extreme value (EV), Lognormal (LN), Logistic (L), Burr (BR) and Rician (R). Four statistical criteria, coefficient of determination (R2), mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are considered as judgment criteria to evaluate the adequacy of probability density functions. As a result, Weibull, Rayleigh, generalized extreme value, extreme value, and Rician distributions accurately perform data. These distributions can be used as an alternative distribution that adequately describes wind speed data in Campo Grande. The weaker settings are obtained by the normal, Burr, logistic, log-logistic, and inverse Gaussian distributions.

Evaluating the Global Forecast System (GFS) for energy management over Minas Gerais State (Brazil) against in-situ observations

“…An accurate and reliable assessment of wind resources plays an important role in the effective use of wind energy (Shamshirband et al, 2016). Given the variety of studies carried out that confirm the efficiency of the Weibull distribution (Shoaib et al, 2017;Katinas et al, 2018;Souza et al, 2019) in wind studies, it can be concluded that the results presented show that wind intensity data, provide relevant general information on wind variability.…”

Goodness-of-Fit of Wind Speed for Probability Distribution in Central Western Brazil

“…The practice of fitting probability distribution models to data, especially velocity data, has been reported in the literature { Pobočíková, 2017; Souza et al, 2019a and b [28,37,38]. In these studies, different probability distribution models were fitted to data series in observations from the Midwest region.…”

“…Souza et al 2019 [38] evaluated the fit of hourly average wind speed data for Campo Grande, State of Mato Grosso do Sul, to the probability distributions of: Weibull (W2), Ralyeigh (RAY), Log-Logistics (LL), Gaussian Inverse (IG), Normal (N), Range (G), Extremely Generated (GV), Extreme (EV), Lognormal (LN), Logistic (L), Burr (BR) and Rician (R). Four statistical criteria, coefficient of determination (R2), mean square error (RMSE), mean absolute error (MAE) and mean absolute error (MAPE) were considered as judgment criteria to assess the adequacy of probability density functions.…”