Layered-Vine Copula-Based Wind Speed Prediction Using Spatial Correlation and Meteorological Influence

The accurate prediction of wind speed (WS) can facilitate the effective utilization of wind energy. However, the complex nonlinear relationships between WS and meteorology variables make WS prediction a challengeable work. Furthermore, considering significant wake effects between adjacent wind turbines, the wind regimes subjected by different turbines tend to exhibit obvious spatio-temporal coupling characteristics. This article proposes a layered-vine copula-based WS prediction framework that considers both spatial correlations between turbines and meteorological information. The first layer extracts the spatial correlations of WS and uses a D-vine structure to describe the multidimensional WS dependence of wind turbines and develop a conditional quantile regression model. The second layer determines the impact of meteorological variables on WS, whereby the key variables are selected and the C-vine regression model is established for prediction correction. Finally, the proposed method is verified using real data measured from a wind farm that the correlations between WS and its influencing factors can be well modeled and thus increase the prediction accuracy.