Abstract
Wind power forecasting (WPF), as a significant research topic within renewable energy, plays a crucial role in enhancing the security, stability, and economic operation of power grids. However, mid-term forecasting faces a persistent dilemma: achieving high predictive accuracy often comes at the cost of computational efficiency. Existing Transformer-based architectures struggle with this trade-off: traditional temporal attention mechanisms suffer from computational redundancy and weak inter-variable coupling, while recent transposed architectures, despite improving speed, inherently compromise the capture of local temporal dynamics and domain-specific periodic characteristics. To overcome these limitations, this paper proposes Fast-Powerformer. Built upon the Reformer backbone, the model reconstructs the feature extraction paradigm through three complementary strategies: (1) an Input Transposition Mechanism that optimizes multivariate coupling modeling while reducing sequence complexity; (2) a lightweight temporal embedding module that compensates for the intrinsic deficiency of transposed architectures in capturing local sequential features; and (3) a Frequency Enhanced Channel Attention Mechanism (FECAM) that exploits spectral information to characterize the physical periodic patterns of wind power. Experimental results on multiple real-world wind farm datasets demonstrate that Fast-Powerformer achieves the best overall performance among compared methods. The model successfully balances superior accuracy with reduced resource consumption, highlighting its significant practical potential for resource-constrained scenarios.
Similar content being viewed by others
Data availability
All data used in the experiments of this study are derived from the open-source dataset provided by the State Grid Corporation of China. The dataset is publicly available at: https://www.nature.com/articles/s41597-022-01696-6.
References
Liu, H. & Chen, C. Data processing strategies in wind energy forecasting models and applications: A comprehensive review. Applied Energy 249, 392–408. https://doi.org/10.1016/j.apenergy.2019.04.188 (2019).
Wang, J., Zhang, W., Wang, J., Han, T. & Kong, L. A novel hybrid approach for wind speed prediction. Information Sciences 273, 304–318. https://doi.org/10.1016/j.ins.2014.02.159 (2014).
Lin, Z., Liu, X. & Collu, M. Wind power prediction based on high-frequency scada data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems 118, 105835. https://doi.org/10.1016/j.ijepes.2020.105835 (2020).
Haque, A. U. et al. A new strategy for predicting short-term wind speed using soft computing models. Renewable and Sustainable Energy Reviews 16, 4563–4573. https://doi.org/10.1016/j.rser.2012.05.042 (2012).
Wang, J., Song, Y., Liu, F. & Hou, R. Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models. Renewable and Sustainable Energy Reviews 60, 960–981. https://doi.org/10.1016/j.rser.2016.01.114 (2016).
AbdElkader, A. G., ZainEldin, H. & Saafan, M. M. Optimizing wind power forecasting with rnn-lstm models through grid search cross-validation. Sustainable Computing: Informatics and Systems 45, 101054 (2025).
Ge, C. et al. Middle-term wind power forecasting method based on long-span nwp and microscale terrain fusion correction. Renewable Energy 240, 122123. https://doi.org/10.1016/j.renene.2024.122123 (2025).
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intelligent Systems and their Applications 13, 18–28. https://doi.org/10.1109/5254.708428 (1998).
Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).
Friedman, J. H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29, 1189–1232 (2001).
Guo, Z., Zhao, J., Zhang, W. & Wang, J. A corrected hybrid approach for wind speed prediction in hexi corridor of china. Energy 36, 1668–1679. https://doi.org/10.1016/j.energy.2010.12.063 (2011).
Shen, W., Jiang, N. & Li, N. An emd-rf based short-term wind power forecasting method. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 283–288, https://doi.org/10.1109/DDCLS.2018.8515901 (2018).
Cai, L., Gu, J., Ma, J. & Jin, Z. Probabilistic wind power forecasting approach via instance-based transfer learning embedded gradient boosting decision trees. energies ; 12(1): 159e76 (2019).
Charytoniuk, W. & Chen, M. S. Very short-term load forecasting using artificial neural networks. IEEE Transactions on Power Systems 15, 263–268 (2002).
Cui, D. et al. Enhancing short-term electricity forecasting with advanced machine learning techniques. Journal of Electrical Engineering & Technology 1–41 (2025).
Singh, S., Bhatti, T. S. & Kothari, D. P. Wind power estimation using artificial neural network. Journal of Energy Engineering 133, 46–52 (2007).
Wilms, H., Cupelli, M., Monti, A. & Gross, T. Exploiting spatio-temporal dependencies for rnn-based wind power forecasts. In 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 921–926 (IEEE, 2019).
Zhang, J. et al. Wind power generation prediction based on lstm. In Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence, 85–89 (2019).
Ding, M. et al. A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting. Neurocomputing 365, 54–61. https://doi.org/10.1016/j.neucom.2019.07.058 (2019).
Wang, D., Cui, X. & Niu, D. Wind power forecasting based on lstm improved by emd-pca-rf. Sustainability 14, 7307 (2022).
Abedinia, O. et al. Wind power forecasting enhancement utilizing adaptive quantile function and cnn-lstm: A probabilistic approach. IEEE Transactions on Industry Applications 60, 12 (2024).
Wang, D. et al. Enhancing wind power forecasting accuracy through lstm with adaptive wind speed calibration (c-lstm). Scientific Reports 15, 5352 (2025).
Mo, Y. et al. Fdnet: Frequency filter enhanced dual lstm network for wind power forecasting. Energy 312, 133514. https://doi.org/10.1016/j.energy.2024.133514 (2024).
Vaswani, A. et al. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).
Huang, S., Yan, C. & Qu, Y. Deep learning model-transformer based wind power forecasting approach. Frontiers in Energy Research 10, 1055683 (2023).
Zhou, H. et al. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence 12, 11106–11115 (2021).
Kitaev, N., Kaiser, Ł. & Levskaya, A. Reformer: The efficient transformer. arXiv Preprint arXiv:2001.04451 (2020).
Wu, H., Xu, J., Wang, J. & Long, M. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems 34, 22419–22430 (2021).
Liu, Y. et al. itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023).
Jiang, M. et al. Fecam: Frequency enhanced channel attention mechanism for time series forecasting. Advanced Engineering Informatics 58, 102158. https://doi.org/10.1016/j.aei.2023.102158 (2023).
Chen, Y. & Xu, J. Solar and wind power data from the chinese state grid renewable energy generation forecasting competition. Scientific Data 9, 577 (2022).
Funding
This work was supported by the National Natural Science Foundation of China under Grant 62373290.
Author information
Authors and Affiliations
Contributions
Mingyi Zhu was responsible for the model design, all experiments, and the majority of the manuscript writing. Zhaoxing Li assisted with parts of the writing and conducted literature review. Qiao Lin and Li Ding provided supervision, guidance, and direction throughout the research and writing process.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Zhu, M., Li, Z., Lin, Q. et al. Fast-powerformer achieves accurate and memory-efficient mid-term wind power forecasting. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36777-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-36777-8
