IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1136-d1599642.html
   My bibliography  Save this article

A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

Author

Listed:
  • Jie Du

    (School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
    Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Shuaizhi Chen

    (School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
    Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Linlin Pan

    (China Electric Power Research Institute, Beijing 100192, China)

  • Yubao Liu

    (Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China
    School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

Suggested Citation

  • Jie Du & Shuaizhi Chen & Linlin Pan & Yubao Liu, 2025. "A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model," Energies, MDPI, vol. 18(5), pages 1-26, February.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1136-:d:1599642
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1136/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/5/1136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    4. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    5. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    6. 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).
    7. Chen, Xin & Ye, Xiaoling & Shi, Jian & Zhang, Yingchao & Xiong, Xiong, 2024. "A spatial transfer-based hybrid model for wind speed forecasting," Energy, Elsevier, vol. 313(C).
    8. Jiang, Zheyong & Che, Jinxing & He, Mingjun & Yuan, Fang, 2023. "A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition," Renewable Energy, Elsevier, vol. 203(C), pages 802-827.
    9. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    10. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    11. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
    12. Li, Qingyang & Wang, Guosong & Wu, Xinrong & Gao, Zhigang & Dan, Bo, 2024. "Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN," Energy, Elsevier, vol. 299(C).
    13. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
    14. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    15. Guo, Shuchang & Yang, Yi & Zhang, Feimin & Wang, Jinyan & Cheng, Yifan, 2025. "Study on bias correction method of ECMWF surface variable forecasts based on deep learning," Renewable Energy, Elsevier, vol. 239(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. 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).
    2. 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).
    3. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    4. 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).
    5. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    6. 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).
    7. 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).
    8. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    9. Joseph, Lionel P. & Deo, Ravinesh C. & Casillas-Pérez, David & Prasad, Ramendra & Raj, Nawin & Salcedo-Sanz, Sancho, 2024. "Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model," Applied Energy, Elsevier, vol. 359(C).
    10. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
    11. Zhang, Dongqin & Hu, Gang & Song, Jie & Gao, Huanxiang & Ren, Hehe & Chen, Wenli, 2024. "A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model," Energy, Elsevier, vol. 288(C).
    12. Zhang, Guowei & Zhang, Yi & Wang, Hui & Liu, Da & Cheng, Runkun & Yang, Di, 2024. "Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network," Energy, Elsevier, vol. 288(C).
    13. 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).
    14. 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).
    15. 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).
    16. 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).
    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. 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.
    19. 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).
    20. Yang, Mao & Guo, Yunfeng & Huang, Tao & Fan, Fulin & Ma, Chenglian & Fang, Guozhong, 2024. "Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods," Energy, Elsevier, vol. 312(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:gam:jeners:v:18:y:2025:i:5:p:1136-:d:1599642. 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.