IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v240y2025ics0960148124022596.html
   My bibliography  Save this article

A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction

Author

Listed:
  • Geng, Donghan
  • Zhang, Yongkang
  • Zhang, Yunlong
  • Qu, Xingchuang
  • Li, Longfei

Abstract

Accurate wind energy prediction is crucial for the safety and stability of power systems. Aiming to further improve the accuracy and robustness of wind speed prediction, this paper presents a hybrid model based on capuchin search algorithm (CapSA), variational mode decomposition (VMD), residual network (ResNet), gated recurrent unit (GRU) network and attention mechanism. In which VMD optimized by CapSA with a new joint fitness function is proposed to decompose original wind speed data, mitigating the non-stationary characteristics of the wind speed sequence, a ResNet based GRU framework is constructed, which alleviating the degradation problem resulting from the deep network layers, and an attention mechanism is introduced to emphasize the impact of key information. Based on the proposed model, a series of experiments on multiple datasets are conducted. The ablation experiments verify the effectiveness of the current model and the contribution of each component. The comparison experiments with three state-of-the-art hybrid models including both decomposition and prediction modules further demonstrate its superiority and stability. Experiments on the datasets collected from typical months of two regions suggest that the proposed model can capture the regional and monthly diversities. Detailed analysis for multiple scenarios provides insight into the correlations of input characteristic and learning capabilities.

Suggested Citation

  • Geng, Donghan & Zhang, Yongkang & Zhang, Yunlong & Qu, Xingchuang & Li, Longfei, 2025. "A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction," Renewable Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:renene:v:240:y:2025:i:c:s0960148124022596
    DOI: 10.1016/j.renene.2024.122191
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124022596
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122191?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    2. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).
    3. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    4. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    5. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    6. Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
    7. Gong, Mingju & Yan, Changcheng & Xu, Wei & Zhao, Zhixuan & Li, Wenxiang & Liu, Yan & Li, Sheng, 2023. "Short-term wind power forecasting model based on temporal convolutional network and Informer," Energy, Elsevier, vol. 283(C).
    8. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
    9. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    10. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    11. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
    12. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).
    13. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    14. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    15. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    16. Ahn, EunJi & Hur, Jin, 2023. "A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques," Renewable Energy, Elsevier, vol. 212(C), pages 394-402.
    17. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    18. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    19. Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
    20. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    21. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
    22. 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).
    23. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    24. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    25. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
    26. Krishna Rayi, Vijaya & Mishra, S.P. & Naik, Jyotirmayee & Dash, P.K., 2022. "Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting," Energy, Elsevier, vol. 244(PA).
    27. Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
    28. Zhao, Jing & Guo, Yiyi & Lin, Yihua & Zhao, Zhiyuan & Guo, Zhenhai, 2024. "A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling," Energy, Elsevier, vol. 302(C).
    29. Wang, Chao & Lin, Hong & Hu, Heng & Yang, Ming & Ma, Li, 2024. "A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction," Energy, Elsevier, vol. 293(C).
    30. Zhou, Gaoyu & Hu, Guofeng & Zhang, Daxing & Zhang, Yun, 2023. "A novel algorithm system for wind power prediction based on RANSAC data screening and Seq2Seq-Attention-BiGRU model," Energy, Elsevier, vol. 283(C).
    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. Sun, Xiaoying & Liu, Haizhong, 2024. "Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S," Energy, Elsevier, vol. 305(C).
    2. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
    3. Wang, Jianguo & Yuan, Weiru & Zhang, Shude & Cheng, Shun & Han, Lincheng, 2024. "Implementing ultra-short-term wind power forecasting without information leakage through cascade decomposition and attention mechanism," Energy, Elsevier, vol. 312(C).
    4. Li, Jianfang & Jia, Li & Zhou, Chengyu, 2024. "Probability density function based adaptive ensemble learning with global convergence for wind power prediction," Energy, Elsevier, vol. 312(C).
    5. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    6. Wang, Chao & Lin, Hong & Hu, Heng & Yang, Ming & Ma, Li, 2024. "A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction," Energy, Elsevier, vol. 293(C).
    7. Cheng, Runkun & Yang, Di & Liu, Da & Zhang, Guowei, 2024. "A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting," Energy, Elsevier, vol. 308(C).
    8. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    9. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    10. Liu, Hong & Yang, Luoxiao & Zhang, Bingying & Zhang, Zijun, 2023. "A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data," Energy, Elsevier, vol. 283(C).
    11. Zhao, Jing & Guo, Yiyi & Lin, Yihua & Zhao, Zhiyuan & Guo, Zhenhai, 2024. "A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling," Energy, Elsevier, vol. 302(C).
    12. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
    13. Yang, Dongchuan & Li, Mingzhu & Guo, Ju-e & Du, Pei, 2024. "An attention-based multi-input LSTM with sliding window-based two-stage decomposition for wind speed forecasting," Applied Energy, Elsevier, vol. 375(C).
    14. Hu, Miaosen & Zheng, Guoqiang & Su, Zhonge & Kong, Lingrui & Wang, Guodong, 2024. "Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks," Energy, Elsevier, vol. 303(C).
    15. Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
    16. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    17. Wang, Chao & Lin, Hong & Yang, Ming & Fu, Xiaoling & Yuan, Yue & Wang, Zewei, 2024. "A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    18. Tang, Yugui & Yang, Kuo & Zheng, Yichu & Ma, Li & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training," Renewable Energy, Elsevier, vol. 224(C).
    19. Peng, Tian & Song, Shihao & Suo, Leiming & Wang, Yuhan & Nazir, Muhammad Shahzad & Zhang, Chu, 2024. "Research and application of a novel graph convolutional RVFL and evolutionary equilibrium optimizer algorithm considering spatial factors in ultra-short-term solar power prediction," Energy, Elsevier, vol. 308(C).
    20. Bashir, Tasarruf & Wang, Huifang & Tahir, Mustafa & Zhang, Yixiang, 2025. "Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models," Renewable Energy, Elsevier, vol. 239(C).

    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:eee:renene:v:240:y:2025:i:c:s0960148124022596. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.