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.

KEYWORDS:

• Wind speed forecasting
• time-series analysis
• variational mode decomposition
• conditional heteroskedasticity
• neural network

Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant number 71671029).

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71671029].

Notes on contributors

Angel Colmenares

Angel Colmenares is a Ph.D. candidate at the School of Statistics, Dongbei University of Finance and Economics, China. He holds MSc in Random Models and a BSc in Actuarial Science from the Central University of Venezuela, where he is a Professor. His main research interests include risk analysis, demography, machine learning, statistical learning, and forecast theory.

Jianzhou Wang

Jianzhou Wang is a professor in the School of Statistics at Dongbei University of Finance and Economics, China. He holds a BSc from Northwest Normal University, China, an MSc, and a Ph.D. degree from Lanzhou University, China. He has published over 150 refereed journal papers. His research interests are wind energy forecasting, data mining, machine learning, statistical learning, and forecast theory.