Multiscale Decomposition and Hybrid Models for Wind Forecasting

Wind speed forecasting plays a critical role in ensuring optimal operation and dispatch of power systems, especially with the global expansion of wind energy. However, the inherently nonstationary and multiscale nature of wind signals presents challenges for traditional statistical and single-model artificial intelligence (AI) methods.

In a new review published in Advances in Wind Engineering, researchers from Central South University in China summarize recent progress in the application of AI to wind speed prediction.

“We offered a structured overview of how AI can contribute to forecasting systems that are not only accurate, but also stable, efficient, and practical for real-world deployment,” says Professor Hui Liu, lead author of the study.

The review was organized around a “decomposition–modeling–optimization” analytical framework. The researchers evaluated signal decomposition techniques such as empirical mode decomposition (EMD) and variational mode decomposition (VMD), analyzing their performance in isolating frequency bands for subsequent modeling. For example, the paper cites studies where VMD-based models achieved mean absolute percentage errors (MAPE) as low as 2.8% in winter forecasts—demonstrating improved stability over traditional methods.

“Following that, we examined existing intelligent fusion strategies, from simple ensemble averages to advanced architectures like stacking, boosting, and graph neural networks,” shares Liu. “These hybrid approaches are shown to enhance robustness by combining shallow models (for fast response to high-frequency fluctuations) with deep learning models (for long-term pattern extraction).”

The review emphasized the role of multi-objective optimization in balancing prediction accuracy, computational efficiency, and model interpretability. It discussed widely adopted algorithms such as NSGA-II and MOPSO, and their ability to support Pareto-optimal decision making in model configuration.

“This review lays a methodological foundation for developing intelligent, modular, and application-oriented wind forecasting systems, bridging recent AI advances with real-world engineering needs,” adds Liu.

Contact author details:

Hui Liu, Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China, csuliuhui@csu.edu.cn

Rui Yang, Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China, eumundrui@csu.edu.cn

Funder: The study is fully supported by the National Natural Science Foundation of China (Grant No. 52072412).

Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

See the article: Liu, H., Yang, R. Artificial intelligence for wind speed forecasting: A review on multi-scale decomposition and intelligent fusion strategies, Advances in Wind Engineering, Volume 2, Issue 2, 2025, 100055, https://doi.org/10.1016/j.awe.2025.100055.