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Solar Irradiance Estimation in Tropical Regions Using Recurrent Neural Networks and WRF Models

Author

Listed:
  • Fadhilah A. Suwadana

    (Department of Geography, Universitas Indonesia, Depok 16424, Indonesia)

  • Pranda M. P. Garniwa

    (Department of Geography, Universitas Indonesia, Depok 16424, Indonesia)

  • Dhavani A. Putera

    (Department of Geography, Universitas Indonesia, Depok 16424, Indonesia)

  • Dita Puspita

    (Department of Geography, Universitas Indonesia, Depok 16424, Indonesia
    Department of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea)

  • Ahmad Gufron

    (Department of Geography, Universitas Indonesia, Depok 16424, Indonesia
    Department of Mechanical Engineering, Kookmin University, Seoul 02707, Republic of Korea)

  • Indra A. Aditya

    (Perusahaan Listrik Negara Research Institute, Jakarta 12760, Indonesia)

  • Hyunjin Lee

    (Department of Mechanical Engineering, Kookmin University, Seoul 02707, Republic of Korea)

  • Iwa Garniwa

    (Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia)

Abstract

The accurate estimation of solar radiation is crucial for optimizing solar energy deployment and advancing the global energy transition. This study pioneers the development of a hybrid model combining Recurrent Neural Networks (RNNs) and the Weather Research and Forecasting (WRF) model to estimate solar radiation in tropical regions characterized by scarce and low-quality data. Using datasets from Sumedang and Jakarta across five locations in West Java, Indonesia, the RNN model achieved moderate accuracy, with R 2 values of 0.68 and 0.53 and RMSE values of 159.87 W/m 2 and 125.53 W/m 2 , respectively. Additional metrics, such as Mean Bias Error (MBE) and relative MBE (rMBE), highlight limitations due to input data constraints. Incorporating spatially resolved GHI data from the WRF model into the RNN framework significantly enhanced accuracy under both clear and cloudy conditions, accounting for the region’s complex topography. While the results are not yet comparable to best practices in data-rich regions, they demonstrate promising potential for advancing solar radiation modeling in tropical climates. This study establishes a critical foundation for future research on hybrid solar radiation estimation techniques in challenging environments, supporting the growth of renewable energy applications in the tropics.

Suggested Citation

  • Fadhilah A. Suwadana & Pranda M. P. Garniwa & Dhavani A. Putera & Dita Puspita & Ahmad Gufron & Indra A. Aditya & Hyunjin Lee & Iwa Garniwa, 2025. "Solar Irradiance Estimation in Tropical Regions Using Recurrent Neural Networks and WRF Models," Energies, MDPI, vol. 18(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:925-:d:1591493
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    References listed on IDEAS

    as
    1. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
    2. Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    3. Jayesh Thaker & Robert Höller, 2023. "Evaluation of High Resolution WRF Solar," Energies, MDPI, vol. 16(8), pages 1-13, April.
    4. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.
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