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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.
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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  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 193715976
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting.
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  Data: *<searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+source+management%22">Renewable energy source management</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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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RecordInfo BibRecord:
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        Value: 10.3390/en19092080
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 2080
    Subjects:
      – SubjectFull: Wind forecasting
        Type: general
      – SubjectFull: Time-frequency analysis
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Renewable energy source management
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
    Titles:
      – TitleFull: STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting.
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          Name:
            NameFull: Ding, Tingxiao
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            NameFull: Hu, Xiaochun
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            NameFull: Chen, Yan
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
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