Abstract:
Wind energy production depends not only on wind speed but also on wind direction. Thus, predicting
and estimating the wind direction for sites accurately will enhance measuring the wind energy
potential. The uncertain nature of wind direction can be presented through probability distributions
and Bayesian analysis can improve the modeling of the wind direction using the contribution of the
prior knowledge to update the empirical shreds of evidence. This must align with the nature of the
empirical evidence as to whether the data are skew or multimodal or not. So far mixtures of von
Mises within the directional statistics domain, are used for modeling wind direction to capture the
multimodality nature present in the data. In this paper, due to the skewed and multimodal patterns
of wind direction on diferent sites of the locations understudy, a mixture of multimodal skewed
von Mises is proposed for wind direction. Furthermore, a Bayesian analysis is presented to take
into account the uncertainty inherent in the proposed wind direction model. A simulation study is
conducted to evaluate the performance of the proposed Bayesian model. This proposed model is
ftted to datasets of wind direction of Marion island and two wind farms in South Africa and show
the superiority of the approach. The posterior predictive distribution is applied to forecast the wind
direction on a wind farm. It is concluded that the proposed model ofers an accurate prediction by
means of credible intervals. The mean wind direction of Marion island in 2017 obtained from 1079
observations was 5.0242 (in radian) while using our proposed method the predicted mean wind
direction and its corresponding 95% credible interval based on 100 generated samples from the
posterior predictive distribution are obtained 5.0171 and (4.7442, 5.2900). Therefore, our results
open a new approach for accurate prediction of wind direction implementing a Bayesian approach via
mixture of skew circular distributions.
