IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p308-d1565203.html
   My bibliography  Save this article

Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods

Author

Listed:
  • Yue Guo

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Yu Song

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Zilong Lai

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

  • Xuyang Wang

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Licheng Wang

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

  • Hui Qin

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

Abstract

In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are then combined with the meteorological sequence features extracted by the Attention-TCN module. The model leverages the strengths of the TCN, the LSTM, and the self-attention mechanism to enhance prediction accuracy and construct reliable prediction intervals. Aiming to optimize both performance and efficiency, the PSO algorithm is used for hyperparameter optimization. Ablation studies and comparisons with other models confirm the effectiveness, accuracy and robustness of the proposed model. This hybrid approach contributes to improved renewable energy integration, offering a more stable and reliable energy supply. Future work will focus on incorporating intelligent systems for autonomous risk management and real-time control of dynamic PV output fluctuations.

Suggested Citation

  • Yue Guo & Yu Song & Zilong Lai & Xuyang Wang & Licheng Wang & Hui Qin, 2025. "Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods," Energies, MDPI, vol. 18(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:308-:d:1565203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/308/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/308/
    Download Restriction: no
    ---><---

    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:2:p:308-:d:1565203. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.