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A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition

Author

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  • Yuan Sun

    (School of Mechanical Engineering, Shanghai Dianji University, No. 300, Shuihua Road, Pudong New Area District, Shanghai 201306, China)

  • Shiyang Zhang

    (School of Mechanical Engineering, Shanghai Dianji University, No. 300, Shuihua Road, Pudong New Area District, Shanghai 201306, China)

Abstract

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Suggested Citation

  • Yuan Sun & Shiyang Zhang, 2024. "A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data D," Energies, MDPI, vol. 17(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2923-:d:1414658
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    References listed on IDEAS

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    1. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    2. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
    3. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    4. Hu, Shuai & Xiang, Yue & Zhang, Hongcai & Xie, Shanyi & Li, Jianhua & Gu, Chenghong & Sun, Wei & Liu, Junyong, 2021. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, Elsevier, vol. 293(C).
    5. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
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