A comparative study of deep learning approaches for real-time solar irradiance forecasting
Accurate forecasting of Global Horizontal Irradiance (GHI) is critical for enhancing both grid stability and the efficiency of solar energy systems. A comparative assessment of several deep learning models is presented in this study for real-time GHI forecasting, specifically Long Short-Term Memory...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05002.pdf |
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| author | Fennane Sara Kacimi Houda Mabchour Hamza ALtalqi Fatehi El Massaoudi El Mahdi Echchelh Adil |
| author_facet | Fennane Sara Kacimi Houda Mabchour Hamza ALtalqi Fatehi El Massaoudi El Mahdi Echchelh Adil |
| author_sort | Fennane Sara |
| collection | DOAJ |
| description | Accurate forecasting of Global Horizontal Irradiance (GHI) is critical for enhancing both grid stability and the efficiency of solar energy systems. A comparative assessment of several deep learning models is presented in this study for real-time GHI forecasting, specifically Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and a hybrid LSTM-GRU architecture. Approach performance is evaluated using standard metrics, including MAE, RMSE, and the R². Findings indicate that while GRUs are computationally efficient, they struggle to maintain long-term temporal dependencies. In contrast, LSTMs effectively capture these dependencies, resulting in improved forecasting accuracy. Notably, the hybrid LSTM-GRU model outperforms the individual architectures, achieving the lowest MAE (12.931), RMSE (21.825), and the highest R² (0.996), thereby demonstrating superior predictive performance. These results highlight the potential of the hybrid model in real-time solar energy applications, improving forecast reliability and grid stability. This study advances solar irradiance forecasting methodologies, thereby facilitating the integration of renewable energy sources and improving the effectiveness and reliability of grid operations. |
| format | Article |
| id | doaj-art-c240ade0040541579fe5294cc2a00b4e |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-c240ade0040541579fe5294cc2a00b4e2025-08-20T03:53:51ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013260500210.1051/epjconf/202532605002epjconf_cofmer2025_05002A comparative study of deep learning approaches for real-time solar irradiance forecastingFennane Sara0Kacimi Houda1Mabchour Hamza2ALtalqi Fatehi3El Massaoudi El Mahdi4Echchelh Adil5Faculty of sciences, Ibn Tofail UniversityFaculty of sciences, Ibn Tofail UniversityFaculty of sciences, Ibn Tofail UniversityFaculty of sciences, Ibn Tofail UniversityFaculty of sciences, Ibn Tofail UniversityFaculty of sciences, Ibn Tofail UniversityAccurate forecasting of Global Horizontal Irradiance (GHI) is critical for enhancing both grid stability and the efficiency of solar energy systems. A comparative assessment of several deep learning models is presented in this study for real-time GHI forecasting, specifically Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and a hybrid LSTM-GRU architecture. Approach performance is evaluated using standard metrics, including MAE, RMSE, and the R². Findings indicate that while GRUs are computationally efficient, they struggle to maintain long-term temporal dependencies. In contrast, LSTMs effectively capture these dependencies, resulting in improved forecasting accuracy. Notably, the hybrid LSTM-GRU model outperforms the individual architectures, achieving the lowest MAE (12.931), RMSE (21.825), and the highest R² (0.996), thereby demonstrating superior predictive performance. These results highlight the potential of the hybrid model in real-time solar energy applications, improving forecast reliability and grid stability. This study advances solar irradiance forecasting methodologies, thereby facilitating the integration of renewable energy sources and improving the effectiveness and reliability of grid operations.https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05002.pdf |
| spellingShingle | Fennane Sara Kacimi Houda Mabchour Hamza ALtalqi Fatehi El Massaoudi El Mahdi Echchelh Adil A comparative study of deep learning approaches for real-time solar irradiance forecasting EPJ Web of Conferences |
| title | A comparative study of deep learning approaches for real-time solar irradiance forecasting |
| title_full | A comparative study of deep learning approaches for real-time solar irradiance forecasting |
| title_fullStr | A comparative study of deep learning approaches for real-time solar irradiance forecasting |
| title_full_unstemmed | A comparative study of deep learning approaches for real-time solar irradiance forecasting |
| title_short | A comparative study of deep learning approaches for real-time solar irradiance forecasting |
| title_sort | comparative study of deep learning approaches for real time solar irradiance forecasting |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05002.pdf |
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