- Hacettepe Journal of Mathematics and Statistics
- Vol: 52 Issue: 3
- Modelling wind speed with a univariate probability distribution depending on two baseline functions
Modelling wind speed with a univariate probability distribution depending on two baseline functions
Authors : Fábio Silveira, Frank Gomes-silva, Cícero Brito, Jader Jale, Felipe Gusmão, Silvio Xavier-júnior, João Rocha
Pages : 808-827
Doi:10.15672/hujms.976348
View : 120 | Download : 162
Publication Date : 2023-05-30
Article Type : Research Article
Abstract :Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.Keywords : Goodness-of-fit, identifiability, L-BFGS-B algorithm, maximum likelihood, Monte Carlo simulation