IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v255y2025ics0960148125015071.html

Ultra-short-term prediction of photovoltaic cluster power based on spatiotemporal convergence effect and spatiotemporal dynamic graph attention network

Author

Listed:
  • Yang, Mao
  • Jiang, Yue
  • Guo, Yunfeng
  • Su, Xin
  • Li, Yi
  • Huang, Tao

Abstract

Ultra-short-term prediction of photovoltaic (PV) power serves as a foundation for intraday power market trading and rolling generation schedules. However, localized meteorological volatility in large-scale PV clusters significantly diminishes the predictability of PV power. In this regard, this paper proposes a dynamic spatiotemporal graph attention (DGAT) network with dynamic graph structure switching for learning the spatiotemporal convergence relationship among different PV stations. And introduced an improved spatiotemporal convergence effect distance into the k-means clustering model to construct the dynamic topology of PV clusters, and proposed a graph node adaptive evaluation module to screen the nodes with the most convergence characteristics. Further, used a combined model of spatiotemporal dynamic graph attention (STDGAT) and bidirectional long short-term memory network (BILSTM) to learn the spatiotemporal convergence correlation among PV nodes and to realize the ultra-short-term power prediction, and the proposed method is applied to a PV cluster in Jilin, China. The results indicate that the proposed method achieves average reductions of 5.47 % in normalized root mean square error (NRMSE) and 5.20 % in normalized mean absolute error (NMAE) across 1st∼4hth prediction horizons compared to traditional methods.

Suggested Citation

  • Yang, Mao & Jiang, Yue & Guo, Yunfeng & Su, Xin & Li, Yi & Huang, Tao, 2025. "Ultra-short-term prediction of photovoltaic cluster power based on spatiotemporal convergence effect and spatiotemporal dynamic graph attention network," Renewable Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:renene:v:255:y:2025:i:c:s0960148125015071
    DOI: 10.1016/j.renene.2025.123843
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125015071
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123843?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. Falama, Ruben Zieba & Saidi, Abdelaziz Salah & Soulouknga, Marcel Hamda & Salah, Chokri Ben, 2023. "A techno-economic comparative study of renewable energy systems based different storage devices," Energy, Elsevier, vol. 266(C).
    3. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
    4. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    5. Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
    6. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    7. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    8. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    9. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    10. Yang, Mao & Jiang, Yue & Zhang, Wei & Li, Yi & Su, Xin, 2024. "Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints," Renewable Energy, Elsevier, vol. 237(PC).
    11. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
    12. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
    13. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    14. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    15. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    16. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    17. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    18. Markovics, Dávid & Mayer, Martin János, 2022. "Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    19. 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).
    20. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(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. Mary A, Anci Manon & K, Ramash Kumar, 2025. "Grey wolf optimized deep adaptive neural MPPT technique for high-efficiency grid integrated photovoltaic systems," Energy, Elsevier, vol. 341(C).
    2. Swapandeep Kaur & Raman Kumar & Kanwardeep Singh, 2025. "Sustainable Component-Level Prioritization of PV Panels, Batteries, and Converters for Solar Technologies in Hybrid Renewable Energy Systems Using Objective-Weighted MCDM Models," Energies, MDPI, vol. 18(20), pages 1-43, October.
    3. Li, Yanmei & Zhang, Yi & Yin, Minghao, 2026. "Physics-informed Mamba network for ultra-short-term photovoltaic power forecasting: integrating WGAN-GP augmentation and CEEMDAN-SST decomposition," Renewable Energy, Elsevier, vol. 257(C).
    4. Wang, Yuzhi & Mu, Qingguang & Bai, Zhiqing & Elmasry, Yasser & Abed Balla, Hyder H. & Alkhayyat, Ahmad & Bayhan, Zahra & Daghistani, Firas & Mahariq, Ibrahim, 2025. "Life cycle assessment, techno-economic and environmental analysis of an innovative integration air power cycle with LNG cold energy utilization in a biomass-fueled multi-generation plant: Soft computing technique and multi-objective optimization appr," Energy, Elsevier, vol. 340(C).
    5. 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.

    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. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    2. Tian, Zhirui & Liang, Bingjie, 2025. "PVMTF: End-to-end long-sequence time-series forecasting frameworks based on patch technique and information fusion coding for mid-term photovoltaic power forecasting," Applied Energy, Elsevier, vol. 396(C).
    3. 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.
    4. Wang, Tao & Xu, Ye & Qin, Yu & Wang, Xu & Zheng, Feifan & Li, Wei, 2025. "Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO," Energy, Elsevier, vol. 334(C).
    5. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    6. 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.
    7. Li, Jiaqian & Rao, Congjun & Gao, Mingyun & Xiao, Xinping & Goh, Mark, 2025. "Efficient calculation of distributed photovoltaic power generation power prediction via deep learning," Renewable Energy, Elsevier, vol. 246(C).
    8. Yang, Shaomei & Luo, Yuman, 2025. "Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models," Energy, Elsevier, vol. 316(C).
    9. Zhai, Chao & He, Xinyi & Cao, Zhixiang & Abdou-Tankari, Mahamadou & Wang, Yi & Zhang, Minghao, 2025. "Photovoltaic power forecasting based on VMD-SSA-Transformer: Multidimensional analysis of dataset length, weather mutation and forecast accuracy," Energy, Elsevier, vol. 324(C).
    10. Guo, Su & Fan, Huiying & Huang, Jing, 2025. "Ultra-short-term PV power prediction based on an improved hybrid model with sky image features and data two-dimensional purification," Energy, Elsevier, vol. 331(C).
    11. Blaifi, Sid-ali & Mellit, Adel & Taghezouit, Bilal & Moulahoum, Samir & Hafdaoui, Hichem, 2025. "A simple non-parametric model for photovoltaic output power prediction," Renewable Energy, Elsevier, vol. 240(C).
    12. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
    13. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    14. Sun, Fengpeng & Li, Longhao & Bian, Dunxin & Bian, Wenlin & Wang, Qinghong & Wang, Shuang, 2025. "Photovoltaic power prediction based on multi-scale photovoltaic power fluctuation characteristics and multi-channel LSTM prediction models," Renewable Energy, Elsevier, vol. 246(C).
    15. Zhu, Honglu & Wang, Yuhang & Wu, Ji & Zhang, Xi, 2026. "A regional distributed photovoltaic power generation forecasting method based on grid division and TCN-Bilstm," Renewable Energy, Elsevier, vol. 256(PA).
    16. Wentao Ma & Lihong Qiu & Fengyuan Sun & Sherif S. M. Ghoneim & Jiandong Duan, 2022. "PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type," Energies, MDPI, vol. 15(14), pages 1-24, July.
    17. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    18. Verdone, Alessio & Panella, Massimo & De Santis, Enrico & Rizzi, Antonello, 2025. "A review of solar and wind energy forecasting: From single-site to multi-site paradigm," Applied Energy, Elsevier, vol. 392(C).
    19. 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).
    20. Mayer, Martin János, 2022. "Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy," Applied Energy, Elsevier, vol. 323(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:renene:v:255:y:2025:i:c:s0960148125015071. 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.