IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v200y2025ip1s0960077925009889.html

A novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit

Author

Listed:
  • Jin, Ji
  • Peng, Tao
  • Wang, Dongwei

Abstract

Wind power is characterized by prominent economic benefits and broad application prospects, but the variability of wind speed can influence the efficiency and reliability of wind power generation. To enhance the accuracy of wind speed prediction, a novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit neural network is proposed. Fractal wavelet transform is studied to decompose wind series into more stable subseries. The analysis on the fractal characteristics of wind series ensures the satisfactory decomposition effect of fractal wavelet transform. Then every subseries is modeled and predicted by the gated recurrent unit model, the quantile loss function is incorporated in the training process to enhance the prediction performance of gated recurrent unit model. The predicted subseries is reconstructed to obtain final predicted results for original wind series. Moreover, shapley additive explanations method is introduced to identify the marginal contribution of each subseries, thereby addressing the explainability of the proposed model. The performance of the proposed model is evaluated by analyzing four groups of wind datasets from the real wind farm. The proposed model outperforms three hybrid models in terms of prediction accuracy, demonstrating a remarkable capability of capturing the variation pattern of wind speed series.

Suggested Citation

  • Jin, Ji & Peng, Tao & Wang, Dongwei, 2025. "A novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009889
    DOI: 10.1016/j.chaos.2025.116975
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925009889
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2025.116975?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Lu, Hongkun & Gao, Xiaoxia & Yu, Jinxiao & Zhao, Qiansheng & Zhu, Xiaoxun & Ma, Wanli & Cao, Jingyuan & Wang, Yu, 2025. "Analysis and prediction of incoming wind speed for turbines in complex wind farm: Accounting for meteorological factors and spatiotemporal characteristics of wind farm," Applied Energy, Elsevier, vol. 381(C).
    2. 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.
    3. 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.
    4. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    5. Jin, Ji & Tian, Jinyu & Yu, Min & Wu, Yong & Tang, Yuanyan, 2024. "A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    6. Cao, Wangbin & Wang, Guangxing & Liang, Xiaolin & Hu, Zhengwei, 2024. "A STAM-LSTM model for wind power prediction with feature selection," Energy, Elsevier, vol. 296(C).
    7. 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).
    8. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
    9. 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).
    10. 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).
    11. 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).
    12. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    13. Bommidi, Bala Saibabu & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function," Energy, Elsevier, vol. 265(C).
    14. Zhao, Ning & Su, Yi & Dai, Xianxing & Jia, Shaomin & Wang, Xuewei, 2024. "A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction," Applied Energy, Elsevier, vol. 369(C).
    15. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    16. Cong, Feiyun & Wu, Rong & Zhong, Wei & Lin, Xiaojie, 2024. "A transferable federated learning approach for wind power prediction based on active privacy clustering and knowledge merge," Energy, Elsevier, vol. 313(C).
    17. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    18. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    19. 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).
    20. Białek, Jakub & Bujalski, Wojciech & Wojdan, Konrad & Guzek, Michał & Kurek, Teresa, 2022. "Dataset level explanation of heat demand forecasting ANN with SHAP," Energy, Elsevier, vol. 261(PA).
    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. Mi, Lihua & Han, Yan & Long, Lizhi & Chen, Hui & Cai, C.S., 2025. "A physics-informed temporal convolutional network-temporal fusion transformer hybrid model for probabilistic wind speed predictions with quantile regression," Energy, Elsevier, vol. 326(C).
    2. 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).
    3. 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).
    4. Ullah, Sajid & Chen, Xi & Han, Han & Wu, Junhao & Dong, Jinghan & Liu, Ruiqing & Ding, Weijie & Liu, Min & Li, Qingli & Qi, Honggang & Huang, Yonggui & Yu, Philip Lh, 2025. "A novel hybrid ensemble approach for wind speed forecasting with dual-stage decomposition strategy using optimized GRU and transformer models," Energy, Elsevier, vol. 329(C).
    5. Chen, Congcong & Chai, Lin & Wang, Qingling, 2025. "A wind power ultra-short-term ensemble forecast framework considering wind speed correction and scenario classification," Energy, Elsevier, vol. 336(C).
    6. Zeng, Huanze & Wu, Binrong & Fang, Haoyu & Lin, Jiacheng, 2025. "Interpretable wind speed forecasting through two-stage decomposition with comprehensive relative importance analysis," Applied Energy, Elsevier, vol. 392(C).
    7. Wang, Yun & Duan, Xiaocong & Zhang, Fan & Wu, Guang & Zou, Runmin & Wan, Jie & Hu, Qinghua, 2025. "MFFDM-WLS: A multi-granularity feature-based coherent forecasting method for temporal hierarchical wind speed time series," Applied Energy, Elsevier, vol. 400(C).
    8. Li, Lue & Long, Jun & Yuan, Meilan, 2025. "Novel wind speed ensemble forecasting system based on the critic weighing principle of fuzzy information granulation and reverse mixed-frequency modeling," Energy, Elsevier, vol. 330(C).
    9. Huawei, Mei & Qingyuan, Zhu & Wangbin, Cao, 2025. "A TSFLinear model for wind power prediction with feature decomposition-clustering," Renewable Energy, Elsevier, vol. 248(C).
    10. 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).
    11. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    12. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    13. 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).
    14. 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).
    15. Leng, Zhiyuan & Chen, Lu & Yi, Bin & Liu, Fanqian & Xie, Tao & Mei, Ziyi, 2025. "Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition," Energy, Elsevier, vol. 322(C).
    16. 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).
    17. Jiang, Wenjun & Liu, Bo & Liang, Yang & Gao, Huanxiang & Lin, Pengfei & Zhang, Dongqin & Hu, Gang, 2024. "Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables," Applied Energy, Elsevier, vol. 353(PB).
    18. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
    19. 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).
    20. Shengcai Zhang & Changsheng Zhu & Xiuting Guo, 2024. "Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model," Energies, MDPI, vol. 17(9), pages 1-22, April.

    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:eee:chsofr:v:200:y:2025:i:p1:s0960077925009889. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.