Issue |
EPJ Web Conf.
Volume 326, 2025
International Conference on Functional Materials and Renewable Energies: COFMER’05 5th Edition
|
|
---|---|---|
Article Number | 05002 | |
Number of page(s) | 4 | |
Section | Smart Energy systems: Storage, Management, Integration | |
DOI | https://doi.org/10.1051/epjconf/202532605002 | |
Published online | 21 May 2025 |
https://doi.org/10.1051/epjconf/202532605002
A comparative study of deep learning approaches for real-time solar irradiance forecasting
1 Faculty of sciences, Ibn Tofail University, Kenitra, Morocco
2 Laboratory of Economic Sciences and Public Policies, Ibn Tofail University, Kenitra, Morocco
* Corresponding author: sara.fennane@uit.ac.ma
Published online: 21 May 2025
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.