Comparison of the performance of different wind speed distribution models applied to onshore and offshore wind speed data in the Northeast Brazil

Abstract

This paper aims to evaluate the performance presented by different distribution models when applied to onshore and offshore wind speed data and determine which distribution has the best fit in each location. The onshore data were measured at two stations located in the Northeast Brazil and the offshore data were measured by two ocean buoys located in the South Atlantic. Five probability distributions were used to model wind speed, namely: Weibull, Nakagami, extended generalized Lindley, generalized gamma and generalized extreme value. The probability distribution parameters were estimated using maximum likelihood, modified maximum likelihood and the method of multi-objective moments. In addition, three goodness of fit tests were used to select the distribution model that best fits a region’s wind speed data, namely: Kolmogorov–Smirnov test, Deviation of Skewness and Kurtosis and the Akaike Information Criterion. The results of these tests were aggregated into a single performance metric called “total error”. In both onshore locations, the extended generalized Lindley and generalized gamma were, in general, superior to the other distributions. In the offshore wind data from the ocean buoys, the extended generalized Lindley, generalized gamma and Weibull showed the best fit in adjusting the histogram. The generalized extreme value distribution presented the worst fit for all four locations. According to the results, there is no single distribution model that is more appropriate to fit a set of wind speed data, and it is necessary to carry out a study to know which of the available distributions is the most appropriate for any particular dataset.