IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924024863.html

Short-term power prediction method of wind farm cluster based on deep spatiotemporal correlation mining

Author

Listed:
  • Wang, Da
  • Yang, Mao
  • Zhang, Wei
  • Ma, Chenglian
  • Su, Xin

Abstract

This paper proposed a short-term power prediction method based on spatiotemporal correlation mining for wind farm clusters. Firstly, a quantitative metric for spatial correlation is established, which takes into account both wind speed and direction. Based on this metric, a graph structure that includes virtual nodes is constructed to represent the spatial correlation between wind farms, with the virtual nodes adding extra useful information to the input data. Then, we employ the graph attention network to extract the spatial features of the wind farm cluster, and then construct a bidirectional recurrent residual network to extract temporal features, introducing multi-task learning algorithms to optimize the network output. Lastly, an evaluation index for the false prediction component is proposed, which assesses the erroneous predictions caused by the accumulation of positive and negative errors, offering a reference for the development of power generation plans. Experimental analysis was conducted using data from 21 wind farm clusters in China, and the short-term prediction accuracy achieved was 89.69 %, which validated the effectiveness of the proposed model.

Suggested Citation

  • Wang, Da & Yang, Mao & Zhang, Wei & Ma, Chenglian & Su, Xin, 2025. "Short-term power prediction method of wind farm cluster based on deep spatiotemporal correlation mining," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024863
    DOI: 10.1016/j.apenergy.2024.125102
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924024863
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125102?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. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
    2. Amirhosein Torabi & Sayyed Ali Kiaian Mousavy & Vahideh Dashti & Mohammadhossein Saeedi & Nasser Yousefi, 2019. "A New Prediction Model Based on Cascade NN for Wind Power Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1219-1243, March.
    3. Meka, Rajitha & Alaeddini, Adel & Bhaganagar, Kiran, 2021. "A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables," Energy, Elsevier, vol. 221(C).
    4. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    5. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).
    6. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    7. Yang, Mao & Wang, Da & Zhang, Wei & Yv, Xinnan, 2024. "A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network," Energy, Elsevier, vol. 310(C).
    8. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
    9. Zhang, Dongdong & Chen, Baian & Zhu, Hongyu & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model," Energy, Elsevier, vol. 285(C).
    10. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    11. Messner, Jakob W. & Pinson, Pierre, 2019. "Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1485-1498.
    12. Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
    13. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).
    14. Bo Wang & Tiancheng Wang & Mao Yang & Chao Han & Dawei Huang & Dake Gu, 2023. "Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation," Energies, MDPI, vol. 16(6), pages 1-16, March.
    15. 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).
    16. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
    17. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Li & Gao, Jinhan & Li, Yunchao & Wang, Da, 2025. "A method for ultra-short-term wind power forecasting of large-scale wind farms based on adaptive spatiotemporal graph convolution," Renewable Energy, Elsevier, vol. 249(C).
    2. Yan Yan & Yan Zhou, 2025. "Temporal-Alignment Cluster Identification and Relevance-Driven Feature Refinement for Ultra-Short-Term Wind Power Forecasting," Energies, MDPI, vol. 18(17), pages 1-19, August.
    3. Li, Pei-hang & Jia, Rong & Cao, Ge & Ming, Bo & Guo, Yi & Wang, Song-kai & Li, Wei, 2025. "A novel perspective for equivalent aggregation of wind farm: Measuring the dynamic similarity between output time-series," Applied Energy, Elsevier, vol. 392(C).
    4. Xiao, Liexi & Wang, Yu & Meng, Anbo & Tan, Zhenglin & Chen, Shuxuan & Song, Shihao & Yin, Hao & Luo, Jianqiang, 2025. "Power prediction methods for offshore wind farm clusters: interpretable ASTGCN based on wind speed delay perception and spatial feature fusion," Energy, Elsevier, vol. 341(C).

    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. Yang, Mao & Jiang, Yuxi & Xu, Chuanyu & Wang, Bo & Wang, Zhao & Su, Xin, 2025. "Day-ahead wind farm cluster power prediction based on trend categorization and spatial information integration model," Applied Energy, Elsevier, vol. 388(C).
    2. Wu, Xinning & Zhan, Haolin & Hu, Jianming & Wang, Ying, 2025. "Non-stationary GNNCrossformer: Transformer with graph information for non-stationary multivariate Spatio-Temporal wind power data forecasting," Applied Energy, Elsevier, vol. 377(PB).
    3. 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).
    4. Yang, Mao & Wang, Da & Zhang, Wei & Yv, Xinnan, 2024. "A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network," Energy, Elsevier, vol. 310(C).
    5. Cao, Yuzhe & Huang, Xuefei & Liu, Jing & Cai, Defu & Ding, Yuemin & Lu, Renzhi, 2025. "DDRGS2S: A novel spatiotemporal correlation-based deep learning model for wind power prediction," Energy, Elsevier, vol. 338(C).
    6. Wang, Li & Gao, Jinhan & Li, Yunchao & Wang, Da, 2025. "A method for ultra-short-term wind power forecasting of large-scale wind farms based on adaptive spatiotemporal graph convolution," Renewable Energy, Elsevier, vol. 249(C).
    7. Zhao, Yongning & Zhao, Yuan & Liao, Haohan & Pan, Shiji & Zheng, Yingying, 2025. "Interpreting LASSO regression model by feature space matching analysis for spatio-temporal correlation based wind power forecasting," Applied Energy, Elsevier, vol. 380(C).
    8. Yang, Mao & Guo, Yunfeng & Fan, Fulin & Huang, Tao, 2024. "Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering," Energy, Elsevier, vol. 302(C).
    9. 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).
    10. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    11. Yang, Mao & Huang, Yutong & Guo, Yunfeng & Zhang, Wei & Wang, Bo, 2024. "Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism," Energy, Elsevier, vol. 290(C).
    12. Yang, Mao & Wang, Da & Zhang, Wei, 2024. "A novel ultra short-term wind power prediction model based on double model coordination switching mechanism," Energy, Elsevier, vol. 289(C).
    13. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
    14. 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).
    15. 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).
    16. 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).
    17. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    18. Liu, Huizhou & Huang, Juntao & Hu, Jinqiu & Zhang, Junfeng & Huang, Mengxing, 2025. "Enhancing wind power prediction accuracy: A novel method integrating seasonal temporal factors and advanced spatio-temporal feature extraction," Energy, Elsevier, vol. 336(C).
    19. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    20. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).

    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:appene:v:380:y:2025:i:c:s0306261924024863. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.