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
    ---><---

    References listed on IDEAS

    as
    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
    3. Tao, Kejun & Zhao, Jinghao & Tao, Ye & Qi, Qingqing & Tian, Yajun, 2024. "Operational day-ahead photovoltaic power forecasting based on transformer variant," Applied Energy, Elsevier, vol. 373(C).
    4. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
    5. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Wang, Xinyu & Ma, Wenping, 2024. "A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 295(C).
    7. Wu Xu & Dongyang Li & Wenjing Dai & Qingchang Wu, 2024. "Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition," Energies, MDPI, vol. 17(12), pages 1-16, June.
    8. Xiaoying Ren & Yongqian Liu & Fei Zhang & Lingfeng Li, 2024. "A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection," Energies, MDPI, vol. 17(16), pages 1-25, August.
    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. 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).
    2. 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).
    3. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    4. 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).
    5. Liu, Wencheng & Mao, Zhizhong, 2024. "Short-term photovoltaic power forecasting with feature extraction and attention mechanisms," Renewable Energy, Elsevier, vol. 226(C).
    6. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    7. Zhang, Zongbin & Huang, Xiaoqiao & Li, Chengli & Cheng, Feiyan & Tai, Yonghang, 2025. "CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting," Energy, Elsevier, vol. 320(C).
    8. Hao, Jianhua & Liu, Fangai & Zhang, Weiwei, 2024. "Multi-scale RWKV with 2-dimensional temporal convolutional network for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 309(C).
    9. Tang, Huadu & Kang, Fei & Li, Xinyu & Sun, Yong, 2025. "Short-term photovoltaic power prediction model based on feature construction and improved transformer," Energy, Elsevier, vol. 320(C).
    10. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
    11. 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).
    12. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    13. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    14. Zang, Haixiang & Li, Wenan & Cheng, Lilin & Liu, Jingxuan & Wei, Zhinong & Sun, Guoqiang, 2025. "Short-term multi-site solar irradiance prediction with dynamic-graph-convolution-based spatial-temporal correlation capturing," Renewable Energy, Elsevier, vol. 246(C).
    15. Margarete Afonso de Sousa Guilhon Araujo & Soraida Aguilar & Reinaldo Castro Souza & Fernando Luiz Cyrino Oliveira, 2024. "Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data," Energies, MDPI, vol. 17(20), pages 1-25, October.
    16. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    17. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    18. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    19. Huxley, O.T. & Taylor, J. & Everard, A. & Briggs, J. & Tilley, K. & Harwood, J. & Buckley, A., 2022. "The uncertainties involved in measuring national solar photovoltaic electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    20. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.

    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: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.

    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.