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Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model

Author

Listed:
  • Na Fang

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Zhengguang Liu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

  • Shilei Fan

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
    Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China)

Abstract

In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness.

Suggested Citation

  • Na Fang & Zhengguang Liu & Shilei Fan, 2025. "Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model," Energies, MDPI, vol. 18(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1465-:d:1613885
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    References listed on IDEAS

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    1. Jingtao Huang & Jin Qin & Shuzhong Song, 2023. "A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk," Energies, MDPI, vol. 16(24), pages 1-18, December.
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    Cited by:

    1. Ioannis Laios & Dimitrios Zafirakis & Konstantinos Moustris, 2025. "From Data-Rich to Data-Scarce: Spatiotemporal Evaluation of a Hybrid Wavelet-Enhanced Deep Learning Model for Day-Ahead Wind Power Forecasting Across Greece," Energies, MDPI, vol. 18(21), pages 1-18, October.

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