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

Fadhilah A. Suwadana, Pranda M. P. Garniwa, Dhavani A. Putera, Dita Puspita, Ahmad Gufron, Indra A. Aditya, Hyunjin Lee and Iwa Garniwa ()
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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
Ahmad Gufron: Department of Geography, Universitas Indonesia, Depok 16424, Indonesia
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

Energies, 2025, vol. 18, issue 4, 1-17

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.

Keywords: solar energy; recurrent neural network; weather and research forecast; tropical regions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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