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Characterizing the wind speed distribution properly is essential for thesatisfactory production of potential energy in wind farms, being the mixturemodels usually employed in the description of such data. However, some mixturemodels commonly have the undesirable property of non-identifiability. In thiswork, we present an alternative distribution which is able to fit the windspeed data adequately. The new model, called Normal-Weibull-Weibull, isidentifiable and its cumulative distribution function is written as acomposition of two baseline functions. We discuss structural properties of theclass that generates the proposed model, such as the linear representation ofthe probability density function, moments and moment generating function. Weperform a Monte Carlo simulation study to investigate the behavior of themaximum likelihood estimates of the parameters. Finally, we presentapplications of the new distribution for modelling wind speed data measured infive different cities of the Northeastern Region of Brazil.

Modelling wind speed with a univariate probability distribution depending on two baseline functions