Non-parametric models for joint probabilistic distributions of wind speed and direction data

Zhuolin Hao

Abstract: Two non-parametric models, namely the non-parametric kernel density (NP-KD) and non-parametric JW (NP-JW) models, are proposed for joint probabilistic modeling of wind speed and direction distributions. In the NP-KD model, a novel bivariate kernel density function, which could consider the characteristics of both wind direction (angular) and speed (linear) data, is firstly constructed and the optimal bandwidth is selected globally through two cross-validation (CV) methods. In the NP-JW model, the univariate Gaussian and von Mises kernel density functions are, respectively, utilized to fit the wind speed and direction data. The estimated wind speed and direction distributions are used to form the joint distribution according to the JW model. Several classical parametric models, including the AG, Weibull, Rayleigh, JW-TNW and JW-FMN models, are also introduced in order for comparisons with the proposed non …