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A Modularity-Enhanced Echo State Network for Nonlinear Wind Energy Predicting

Author

Listed:
  • Sixian Yue

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China)

  • Zhili Zhao

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China)

  • Tianyou Lai

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Jin Zhang

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

Abstract

With the rapid growth of wind power generation, accurate wind energy prediction has emerged as a critical challenge, particularly due to the highly nonlinear nature of wind speed data. This paper proposes a modularized Echo State Network (MESN) model to improve wind energy forecasting. To enhance generalization, the wind speed data is first decomposed into time series components, and Modes-cluster is employed to extract trend patterns and pre-train the ESN output layer. Furthermore, Turbines-cluster groups wind turbines based on their wind speed and energy characteristics, enabling turbines within the same category to share the ESN output matrix for prediction. An output integration module is then introduced to aggregate the predicted results, while the modular design ensures efficient task allocation across different modules. Comparative experiments with other neural network models demonstrate the effectiveness of the proposed approach, showing that the statistical RMSE of parameter error is reduced by an average factor of 2.08 compared to traditional neural network models.

Suggested Citation

  • Sixian Yue & Zhili Zhao & Tianyou Lai & Jin Zhang, 2025. "A Modularity-Enhanced Echo State Network for Nonlinear Wind Energy Predicting," Energies, MDPI, vol. 18(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1858-:d:1629523
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    References listed on IDEAS

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    1. Kelan Patel & Thomas D. Dunstan & Takafumi Nishino, 2021. "Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response," Energies, MDPI, vol. 14(19), pages 1-16, October.
    2. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    3. Qian He & Mingbin Zhao & Shujie Li & Xuefang Li & Zuoxun Wang, 2025. "Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model," Energies, MDPI, vol. 18(3), pages 1-17, January.
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