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The influence of variational mode decomposition on LSTM prediction accuracy - A case study with wind turbine power signals

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  • Fang, Mingkun
  • Zhang, Fangfang
  • Zhu, Di
  • Tao, Ran
  • Xiao, Ruofu

Abstract

Sustainable development of energy is regarded as a crucial solution to combat global challenges such as climate change and energy security. However, the sustainable harnessing of wind energy confronts randomness and volatility, rendering the precise prediction of wind power signals a crucial challenge in the management of power systems. To address these issues, this study employs the Variational Mode Decomposition (VMD) method and Long Short-Term Memory (LSTM) networks to comprehensively analyze the impact of the VMD method on the accuracy of wind power signal prediction using LSTM. The orthogonal experimental method is used to determine the key parameters of the VMD method. The results show that the VMD-LSTM model significantly outperforms the traditional LSTM model in time series signal prediction accuracy. Compared to the LSTM model, the VMD-LSTM model reduces the MAE, MAPE, and RMSE errors by 39.7 %, 37.5 %, and 30.9 %, respectively, and increases the R2 by 20.6 %. Among the decomposed modes, the third-order mode exhibits the largest prediction error. A method is proposed to improve prediction accuracy by excluding certain non-essential modes. This study provides valuable insights into reducing power fluctuations in the grid caused by active power prediction errors.

Suggested Citation

  • Fang, Mingkun & Zhang, Fangfang & Zhu, Di & Tao, Ran & Xiao, Ruofu, 2025. "The influence of variational mode decomposition on LSTM prediction accuracy - A case study with wind turbine power signals," Renewable Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:renene:v:245:y:2025:i:c:s0960148125005257
    DOI: 10.1016/j.renene.2025.122863
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    References listed on IDEAS

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    1. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    2. Ding, Yunfei & Chen, Zijun & Zhang, Hongwei & Wang, Xin & Guo, Ying, 2022. "A short-term wind power prediction model based on CEEMD and WOA-KELM," Renewable Energy, Elsevier, vol. 189(C), pages 188-198.
    3. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    4. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    5. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    6. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    7. Bin Li & Le Kui & Jingdong Luo & Shiyong Chen, 2021. "Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction," Advances in Mathematical Physics, Hindawi, vol. 2021, pages 1-11, December.
    8. Peng, Xiaokang & Liu, Zicheng & Jiang, Dong, 2021. "A review of multiphase energy conversion in wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.
    10. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    11. Bin Li & Le Kui & Jingdong Luo & Shiyong Chen, 2021. "Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction," Advances in Mathematical Physics, John Wiley & Sons, vol. 2021(1).
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