Assessing wind energy potential in southeast Brazil: fitting Weibull, Gumbel, and GEV distributions to wind speed data

Abstract Fitting statistical distribution of wind speed allows for a better understanding of the wind potential of a location, which is essential when deciding where to locate new wind farm installations and sometimes for the design of tall buildings and super-structures. The objective of this study is to fit three probability distribution functions (PDFs), such as Generalized Extreme Value (GEV), Gumbel (GUM) and Weibull (W) to average wind speed data from the cities of São Paulo and Rio de Janeiro, located in the southeast region of Brazil. Another objective was to calculate the expected average wind speeds for these cities, for the different return period (10, 20, 30, 40, 50 and 100 years). The assessment and appropriateness of the chosen PDFs were evaluated using statistical measures such as the Kolmogorov–Smirnov test (KS), the Ljung-Box test, and mean absolute percentage errors (MAPE). Regarding the evaluation of the accuracy of wind speed predictions in Rio de Janeiro, the Weibull distribution showed the best performance. In São Paulo, the Gumbel and Weibull distributions showed the same performance. The results presented in this article can help in understanding the wind potential of southeastern Brazil and serves as a source of wind energy projects.