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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
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    References listed on IDEAS

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    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).
    Full references (including those not matched with items on IDEAS)

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