STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting.
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| Title: | STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting. |
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| Authors: | Ding, Tingxiao1 (AUTHOR), Hu, Xiaochun2 (AUTHOR) hxch@gxufe.edu.cn, Chen, Yan1,3 (AUTHOR), Liu, Rongbin1,4 (AUTHOR), Su, Jin1 (AUTHOR), Jiang, Rongxing1,2 (AUTHOR), Qin, Yiming3,4 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 9, p2080. 22p. |
| Subject Terms: | *Wind forecasting, *Time-frequency analysis, *Convolutional neural networks, *Artificial neural networks, *Renewable energy source management, *Signal processing, *Machine learning, *Renewable energy sources |
| 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. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19092080 |
