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

Na Fang, Zhengguang Liu () and Shilei Fan
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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
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
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

Energies, 2025, vol. 18, issue 6, 1-18

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.

Keywords: time series data prediction; hybrid deep learning; gated recurrent unit; CEEMDAN; VMD; secondary decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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