Use of finite mixture models with skew-t-normal Birnbaum-Saunders components in the analysis of wind speed: Case studies in Ontario, Canada

The probabilistic distribution of wind speed is critical information required in evaluating wind resources, designing wind farms, and mitigating the possible risks in wind power expansion. Various distributions have been used in the research literature to estimate wind speed distribution. In this paper, we propose a flexible family of mixture distributions, whose elements are convex linear combinations of the skew-t-normal Birnbaum-Saunders distributions and is suitable for modelling heavy-tailed data with a heterogeneous population. These distributions are then used to estimate the wind speed distribution. The performance of the proposed family has been compared with five mixture models of the already used distributions, using wind speed data collected at nine stations across Ontario, Canada. The results indicate that mixture models generally provide a better fit than unimodal distributions, according to the model selection criterion. Based on the obtained results, the proposed family provides highly flexible models at all selected stations. It functions better than the other considered distributions at seven of the stations, whereas it is ranked second in the remaining two stations. The proposed family can be utilized in a widespread manner to describe the wind speed in Canada, as well as other regions with similar features.