@Article{ee.2025.064498,
AUTHOR = {Samira Marhraoui, Basma Saad, Hassan Silkan, Said Laasri, Asmaa El Hannani},
TITLE = {Forecasting Solar Energy Production across Multiple Sites Using Deep Learning},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {7},
PAGES = {2653--2672},
URL = {http://www.techscience.com/energy/v122n7/62677},
ISSN = {1546-0118},
ABSTRACT = {Photovoltaic (PV) power forecasting is essential for balancing energy supply and demand in renewable energy systems. However, the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation. This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies: Hybrid-Si, Mono-Si, and Poly-Si, across three forecasting horizons: 1-step, 12-step, and 24-step. Among the tested models, the Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture exhibited superior performance, particularly for the 24-step horizon, achieving R<sup>2</sup> = 0.9793 and MAE = 0.0162 for the Poly-Si array, followed by Mono-Si (R<sup>2</sup> = 0.9768) and Hybrid-Si arrays (R<sup>2</sup> = 0.9769). These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies. By identifying the most suitable predictive model for each panel technology, this study contributes to optimizing PV power forecasting and improving energy management strategies.},
DOI = {10.32604/ee.2025.064498}
}