IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v328y2025ics0360544225022005.html

Probabilistic power forecasting for wind farm clusters using Moran-Graph network with posterior feedback attention mechanism

Author

Listed:
  • Qu, Zhijian
  • Hou, Xinxing
  • Huang, ShiXun
  • Li, Di
  • He, Yang
  • Meng, Yan

Abstract

Predicting wind power generation precisely is key to optimizing wind energy use. This paper proposes a novel hybrid probabilistic forecasting model for wind farm cluster power output. First, to address the complex spatiotemporal interdependencies among multiple wind farms, a Moran-Graph network is proposed to extract synergistic features across wind farm clusters. To address the high volatility and non-stationarity of wind power, a hybrid decomposition method integrating sequential Variational Mode Decomposition (SVMD) and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is proposed. The Refined Composite Multi-scale Weighted Permutation Entropy (RCMWPE) is then employed to quantify the complexity of each subsequence for optimal signal reconstruction. Finally, a Feedback Attention-based Multi-Stacking model (FAMM-Stacking) is developed, which integrates Hybrid Kernel Density Estimation (HKDE) to generate probabilistic forecasting results for wind farm cluster power output. Case studies on wind farm clusters in Northwest China demonstrate that, at identical confidence levels, the proposed model achieves superior prediction interval coverage probability (PICP) with narrower average bandwidth, outperforming benchmark models.

Suggested Citation

  • Qu, Zhijian & Hou, Xinxing & Huang, ShiXun & Li, Di & He, Yang & Meng, Yan, 2025. "Probabilistic power forecasting for wind farm clusters using Moran-Graph network with posterior feedback attention mechanism," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022005
    DOI: 10.1016/j.energy.2025.136558
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225022005
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.136558?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. Yang, Mao & Guo, Yunfeng & Huang, Tao & Zhang, Wei, 2025. "Power prediction considering NWP wind speed error tolerability: A strategy to improve the accuracy of short-term wind power prediction under wind speed offset scenarios," Applied Energy, Elsevier, vol. 377(PD).
    2. De Luca Peña, Laura Vittoria & Taelman, Sue Ellen & Bas, Bilge & Staes, Jan & Mertens, Jan & Clavreul, Julie & Préat, Nils & Dewulf, Jo, 2024. "Monetized (socio-)environmental handprint and footprint of an offshore windfarm in the Belgian Continental Shelf: An assessment of local, regional and global impacts," Applied Energy, Elsevier, vol. 353(PA).
    3. Shang, Zhihao & Chen, Yanhua & Wen, Quan & Ruan, Xiaolong, 2025. "Multi-step ahead wind speed forecasting approach coupling PSR, NNCT-based multi-model fusion and a new optimization algorithm," Renewable Energy, Elsevier, vol. 238(C).
    4. 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).
    5. 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).
    6. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    7. Wang, Fei & Chen, Peng & Zhen, Zhao & Yin, Rui & Cao, Chunmei & Zhang, Yagang & Duić, Neven, 2022. "Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method," Applied Energy, Elsevier, vol. 323(C).
    8. Xiong, Zhanhang & Yao, Jianjiang & Huang, Yongmin & Yu, Zhaoxu & Liu, Yalei, 2024. "A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition," Applied Energy, Elsevier, vol. 353(PB).
    9. Qu, Zhijian & Hou, Xinxing & Li, Jian & Hu, Wenbo, 2024. "Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation," Energy, Elsevier, vol. 290(C).
    10. Wang, Zhongliang & Zhu, Hongyu & Zhang, Dongdong & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation," Applied Energy, Elsevier, vol. 352(C).
    11. Chen, Shijian & Shang, Pengjian, 2020. "Financial time series analysis using the relation between MPE and MWPE," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    12. Qu, Zhijian & Li, Jian & Hou, Xinxing & Gui, Jianglin, 2023. "A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction," Energy, Elsevier, vol. 281(C).
    13. 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.
    14. Hu, Huanling & Wang, Lin & Zhang, Dabin & Ling, Liwen, 2023. "Rolling decomposition method in fusion with echo state network for wind speed forecasting," Renewable Energy, Elsevier, vol. 216(C).
    15. Deng, Jiewen & Xiao, Zhao & Zhao, Qiancheng & Zhan, Jun & Tao, Jie & Liu, Minghua & Song, Dongran, 2024. "Wind turbine short-term power forecasting method based on hybrid probabilistic neural network," Energy, Elsevier, vol. 313(C).
    16. 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).
    17. Hanifi, Shahram & Zare-Behtash, Hossein & Cammarano, Andrea & Lotfian, Saeid, 2023. "Offshore wind power forecasting based on WPD and optimised deep learning methods," Renewable Energy, Elsevier, vol. 218(C).
    18. Arooj, Qumrish, 2024. "FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions," Energy, Elsevier, vol. 309(C).
    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. Chen, Yunxiao & Liu, Jinfu & Yu, Daren, 2025. "Economically-driven spatiotemporal collaborative correction of high-precision wind power forecasting curves: aiming to more practical scheduling," Energy, Elsevier, vol. 337(C).
    2. Wei, Jiangxia & Zhang, Weiqiang & Zhang, Wenjie & Ren, Mifeng & Xu, Xinying & Cheng, Lan, 2025. "DBSTN: A dual-branch spatio-temporal network for wind power prediction using multi-modal 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. Yingrui Chen & Jiarong Shi, 2025. "Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest," Sustainability, MDPI, vol. 17(11), pages 1-16, May.
    2. 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).
    3. 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).
    4. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    5. Zeng, Huanze & Shi, Chenlu & Fang, Haoyu & Wu, Binrong, 2025. "Interpretable multivariate wind speed forecasting using sliding masked window-based decomposition and deep autoregressive networks," Energy, Elsevier, vol. 341(C).
    6. 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).
    7. Chen, Juntao & Fu, Xueying & Zhang, Lingli & Shen, Haoye & Wu, Jibo, 2024. "A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms," Energy, Elsevier, vol. 308(C).
    8. Qu, Zhijian & Hou, Xinxing & Li, Jian & Hu, Wenbo, 2024. "Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation," Energy, Elsevier, vol. 290(C).
    9. Li, Ke & Qin, Zheng & Mu, Yuchen & Wang, Haiyang & Bie, Qingfeng & Yin, Xianxin & Yan, Yi, 2025. "Transfer learning-based multi-energy load forecasting method for integrated energy system with zero-shot," Applied Energy, Elsevier, vol. 401(PC).
    10. Shi, Xinjie & Wang, Jianzhou & Li, Zhiwu & Zhang, Wenliang, 2025. "Efficient interpretable wind speed prediction system," Energy, Elsevier, vol. 335(C).
    11. Yingjie Liu & Mao Yang, 2025. "Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks," Energies, MDPI, vol. 18(15), pages 1-30, August.
    12. Xuejun Chen & Ying Wang & Haitao Zhang & Jianzhou Wang, 2024. "A novel hybrid forecasting model with feature selection and deep learning for wind speed research," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1682-1705, August.
    13. 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).
    14. Jiang, Weiyi & Wang, Jujie & Shu, Shuqin & He, Xuecheng, 2026. "An enhanced differential learning wind speed interval-value prediction system based on optimal collaborative interval decomposition and strategic model selection," Renewable Energy, Elsevier, vol. 256(PB).
    15. Cui, Xiwen & Yu, Xiaoyu & Niu, Haowei & Niu, Dongxiao & Liu, Da, 2025. "A novel data-driven multi-step wind power point-interval prediction framework integrating sliding window-based two-layer adaptive decomposition and multi-objective optimization for balancing prediction accuracy and stability," Applied Energy, Elsevier, vol. 397(C).
    16. Yang, Mao & Jiang, Renxian & Wang, Bo & Fang, Guozhong & Jia, Yunpeng & Fan, Fulin, 2025. "Multi-channel attention mechanism graph convolutional network considering cumulative effect and temporal causality for day-ahead wind power prediction," Energy, Elsevier, vol. 332(C).
    17. He, Jiaming & Tan, Qinliang & Lv, Hanyu, 2025. "Data-driven climate resilience assessment for distributed energy systems using diffusion transformer and polynomial expansions," Applied Energy, Elsevier, vol. 380(C).
    18. Li, HongYang & He, Shan & Yuan, JiaWang & Wang, Chao, 2025. "A wind power prediction method integrating dynamic multi-scale spatio-temporal modelling, adaptive multi-strategy local decomposition, and meta-learning ensemble model," Energy, Elsevier, vol. 340(C).
    19. Liu, Zixuan & Mo, Li & Zhang, Mi & Kang, Jiangrui & Liu, Wan & Sun, Xutong & Xiao, Wenjing, 2025. "High-dimensional scenario generation method joint-driven by multiple correlations for hydro-wind-photovoltaic," Applied Energy, Elsevier, vol. 400(C).
    20. Yu, Sheng & He, Bin & Fang, Lei, 2025. "Multi-step short-term forecasting of photovoltaic power utilizing TimesNet with enhanced feature extraction and a novel loss function," Applied Energy, Elsevier, vol. 388(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:energy:v:328:y:2025:i:c:s0360544225022005. 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/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.