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A deep time–frequency augmented wind power forecasting model

Author

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  • Dong, Yunxuan
  • Zhou, Binggui
  • Zhang, Hongcai
  • Yang, Guanghua
  • Ma, Shaodan

Abstract

The high level of uncertainty in wind power generation presents a significant challenge to the safe and efficient operation of modern power systems, especially with high wind power penetration. To overcome this challenge, it is essential to develop an effective model that can capture informative features from wind power generation and provide accurate wind power forecasting. In this research, a deep time–frequency augmented wind power forecasting model is proposed. The model augments the inputs with the instantaneous frequency by utilizing the complete ensemble empirical mode decomposition method and Hilbert transform to extract non-stationary and non-linear features. The fully connected long short-term memory network is then used to learn high-order features effectively. The effectiveness of the proposed model is evaluated using the wind power data from five states of the Australian continent. Numerical results demonstrate that the proposed model achieves an average Mean Absolute Percentage Error of 3.81%, significantly outperforming benchmark methods, with improvements ranging from 1.71% to 4.03%. Comprehensive ablation Studies confirm that the CEEMD-Hilbert transform based frequency augmentation provides superior capability in capturing non-stationary features compared to traditional Fourier/wavelet and other decomposition methods, reducing the average one-step Root Mean Square Error by 28.3-47.9% compared to the strongest baseline.

Suggested Citation

  • Dong, Yunxuan & Zhou, Binggui & Zhang, Hongcai & Yang, Guanghua & Ma, Shaodan, 2026. "A deep time–frequency augmented wind power forecasting model," Renewable Energy, Elsevier, vol. 256(PA).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s0960148125012121
    DOI: 10.1016/j.renene.2025.123550
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    References listed on IDEAS

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