IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5767-d1523808.html
   My bibliography  Save this article

TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction

Author

Listed:
  • Zhi Rao

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
    Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Zaimin Yang

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Xiongping Yang

    (China Southern Power Grid, Guangzhou 510623, China)

  • Jiaming Li

    (China Southern Power Grid, Guangzhou 510623, China)

  • Wenchuan Meng

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Zhichu Wei

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

Abstract

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.

Suggested Citation

  • Zhi Rao & Zaimin Yang & Xiongping Yang & Jiaming Li & Wenchuan Meng & Zhichu Wei, 2024. "TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction," Energies, MDPI, vol. 17(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5767-:d:1523808
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5767/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5767/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deveci, Muhammet & Pamucar, Dragan & Oguz, Elif, 2022. "Floating photovoltaic site selection using fuzzy rough numbers based LAAW and RAFSI model," Applied Energy, Elsevier, vol. 324(C).
    2. Liu, Bingchun & Huo, Xiankai, 2024. "Prediction of Photovoltaic power generation and analyzing of carbon emission reduction capacity in China," Renewable Energy, Elsevier, vol. 222(C).
    3. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    4. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    5. Yin, Linfei & Zhou, Hang, 2024. "Modal decomposition integrated model for ultra-supercritical coal-fired power plant reheater tube temperature multi-step prediction," Energy, Elsevier, vol. 292(C).
    6. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    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. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting," Applied Energy, Elsevier, vol. 364(C).
    2. Dong, Shiqian & Di, Yanqiang & Gao, Yafeng & Long, He & Fan, Zhixuan & Guan, Jingxuan & Han, Lijun & Wang, Yingming, 2025. "Multiple operational strategies investigations of the PV/T collectors based on 3 days ahead hourly radiation prediction," Applied Energy, Elsevier, vol. 377(PA).
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    5. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
    6. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    7. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
    8. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    9. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
    10. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    11. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
    12. Bashir, Tasarruf & Wang, Huifang & Tahir, Mustafa & Zhang, Yixiang, 2025. "Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models," Renewable Energy, Elsevier, vol. 239(C).
    13. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    14. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    15. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    16. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    17. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    18. Haspolat, Emre & Cicek, Doga Derman & Gokmener, Serkan & Melek, Abiddin Berhan & Deveci, Muhammet & Oguz, Elif, 2024. "Site selection of floating photovoltaic systems on hydropower reservoirs using fuzzy sine trigonometric decision-making model: Turkey as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
    19. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).
    20. 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).

    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:17:y:2024:i:22:p:5767-:d:1523808. 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.