STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting

Ding, Tingxiao; Hu, Xiaochun; Chen, Yan; Liu, Rongbin; Su, Jin; Jiang, Rongxing; Qin, Yiming

doi:10.3390/en19092080

Open AccessArticle

STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting

by

Tingxiao Ding

¹,

Xiaochun Hu

^2,*

,

Yan Chen

^1,3

,

Rongbin Liu

¹,

Jin Su

¹,

Rongxing Jiang

¹ and

Yiming Qin

⁴

¹

School of Computer and Electronic Information Science, Guangxi University, Nanning 530000, China

²

College of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning 530000, China

³

Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center, Nanning 530000, China

⁴

Dispatch and Control Center, Guangxi Power Grid, Nanning 530000, China

^*

Author to whom correspondence should be addressed.

Energies 2026, 19(9), 2080; https://doi.org/10.3390/en19092080

Submission received: 24 February 2026 / Revised: 4 April 2026 / Accepted: 22 April 2026 / Published: 25 April 2026

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Abstract

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management.

Keywords: wind power forecasting; time-frequency analysis; deep learning; discrete cosine transform; long-term prediction; renewable energy

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MDPI and ACS Style

Ding, T.; Hu, X.; Chen, Y.; Liu, R.; Su, J.; Jiang, R.; Qin, Y. STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting. Energies 2026, 19, 2080. https://doi.org/10.3390/en19092080

AMA Style

Ding T, Hu X, Chen Y, Liu R, Su J, Jiang R, Qin Y. STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting. Energies. 2026; 19(9):2080. https://doi.org/10.3390/en19092080

Chicago/Turabian Style

Ding, Tingxiao, Xiaochun Hu, Yan Chen, Rongbin Liu, Jin Su, Rongxing Jiang, and Yiming Qin. 2026. "STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting" Energies 19, no. 9: 2080. https://doi.org/10.3390/en19092080

APA Style

Ding, T., Hu, X., Chen, Y., Liu, R., Su, J., Jiang, R., & Qin, Y. (2026). STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting. Energies, 19(9), 2080. https://doi.org/10.3390/en19092080

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STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting

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