IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i7p1858-d1629523.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/7/1858/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/7/1858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    2. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    3. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    4. Liang, Tao & Zhao, Qing & Lv, Qingzhao & Sun, Hexu, 2021. "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, Elsevier, vol. 230(C).
    5. Wang, Ying & Li, Hongmin & Jahanger, Atif & Li, Qiwei & Wang, Biao & Balsalobre-Lorente, Daniel, 2024. "A novel ensemble electricity load forecasting system based on a decomposition-selection-optimization strategy," Energy, Elsevier, vol. 312(C).
    6. Yao, Xianshuang & Guo, Kangshuai & Lei, Jianqi & Li, Xuanyu, 2024. "Fully connected multi-reservoir echo state networks for wind power prediction," Energy, Elsevier, vol. 312(C).
    7. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    8. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.
    9. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    10. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    11. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    12. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
    13. Qingliang Zhao & Xiaobin Feng & Liwen Zhang & Yiduo Wang, 2023. "Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising," Mathematics, MDPI, vol. 11(19), pages 1-16, October.
    14. Tayeb Brahimi, 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia," Energies, MDPI, vol. 12(24), pages 1-16, December.
    15. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    16. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    17. Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
    18. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    19. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    20. Piotr Bórawski & Aneta Bełdycka-Bórawska & Lisa Holden & Tomasz Rokicki, 2022. "The Role of Renewable Energy Sources in Electricity Production in Poland and the Background of Energy Policy of the European Union at the Beginning of the COVID-19 Crisis," Energies, MDPI, vol. 15(22), pages 1-17, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1858-:d:1629523. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.