Mixture bivariate distribution of wind speed and air density for wind energy assessment

Offshore Wind Energy Assessment with a Clustering Approach to Mixture Model Parameter Estimation

“…In this study, a hub height of 120 m was selected as representative of standard offshore wind turbine configurations [58]. Algorithms previously outlined in Yang et al [29] were applied to derive hourly wind speeds and air densities at the designated hub height using ERA5 dataset. To attenuate the effect of short-term dependencies in the time To provide a thorough analysis of wind energy characteristics across the selected six locations, statistical analyses of both wind speed and air density were conducted.…”

“…Locations L1 to L4 exhibit relatively symmetric air density distributions, as reflected by their lower skewness values, whereas L5 and L6 are marked by higher skewness, signifying a tendency toward lower air density values. To yield a more rigorous examination of the complex interrelationships between wind speed and air density, a mixture Gaussian copula model, as defined in Equation ( 12) and recommended by Yang et al [29], was adopted to characterize the bivariate structure. Specifically, the marginal distributions incorporate a mixture Weibull distribution for wind speed and a mixture lognormal distribution for air density.…”

“…The joint probability distributions of wind speed and air density, derived from both the EM algorithm and the clustering algorithm, were then employed to analyze wind energy potential. Conventionally, wind energy resource evaluations predominantly emphasize metrics such as wind power density and the power output of wind turbines [29,51]. Notably, these metrics are intrinsically associated with the specific wind turbine model under consideration.…”

“…Their adaptability proves advantageous in addressing the patterns in wind and wave data, making them well-suited for predictive analysis. This remarkable versatility is of utmost significance in applications such as wind power generation, where precise wind speed forecasts are indispensable for optimizing energy production efficiency [28,29]. Consequently, mixture distribution models have emerged as a potent method in the pursuit of accurate and efficient utilization of renewable energy sources.…”

“…Please note that although air density is often assumed to remain constant in many previous studies, the value of this variable could be affected by many factors, such as altitude, temperature, pressure, and humidity. Research indicates that it is not reasonable to assume air density as constant when forecasting wind energy resources [29]. Therefore, this study aims to evaluate wind energy resources by establishing a bivariate probability distribution for wind speed and air density.…”