Copula‐based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

Abstract

Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong.

Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation

“…It is mainly used to model the joint distribution function of random variables, which separates the marginal distribution from dependence and makes the model more flexible. There are various types of copula functions, and the common ones include Gaussian copula [41], t-distribution copula [42], Clayton copula [43], and Frank copula [44]. Each type of copula corresponds to a different dependence structure, so choosing an appropriate copula function is crucial for accurately describing the dependence among variables.…”

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment

“…Li et al [31] demonstrated that the copula approach is superior in binary distributions adjustment of wind speed and direction, as well as in predicting extreme wind speeds, through a comparison of its performance with that of conventional methods. Huang et al [32] evaluated the directional wind energy potential in Hong Kong based on various copula functions.…”