Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition

Abstract

With the steady integration of wind energy into electricity networks, precise wind speed forecasting is an essential element in the administration and management of power systems. However, wind energy forecasting research has focused increasingly on short-term forecasting, leaving aside the challenging horizons of medium- and long-term predictions. Therefore, this study proposes a wind speed forecasting methodology based on two types of ensembles, which addresses the nonlinearity and chaotic behavior of wind speed using decomposition-based models. With the results of the first ensemble of 90 ARMA-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) models, the second ensemble is established based on three types of neural networks and learning functions. Finally, we propose the application of variational mode decomposition (VMD) before or after the first ensemble. The experimental outcomes lead us to divide the prediction horizons into two broad groups, those where VMD inclusion did and did not improve the ensemble results. These horizons are classified as short-term (3, 4, and 5 steps) and mid- and long-term forecast horizons (6, 12, 24, and 48 steps), where the best performance arises with the VMD application after the first ensemble. The research contributes to the existing literature studying a wide variety of innovation distribution and optimization methods that can be implemented with GARCH-type models. Simultaneously, the VMD application is proposed in a novel way not seen in the literature by applying it to the predictions already made by other models, in this case, in ensembles of GARCH-type models.