Open Access

ARTICLE

Forecasting Solar Energy Production across Multiple Sites Using Deep Learning

Samira Marhraoui^1,2,*, Basma Saad³, Hassan Silkan¹, Said Laasri², Asmaa El Hannani³

1 Department of Computer Science, Research in Optimization, Emerging Systems, Networks and Imaging Laboratory (LAROSERI), Faculty of Sciences, University of Chouaib Doukkali, El Jadida, 24000, Morocco
2 Laboratory of Energy Science Engineering (LabSipe), National School of Applied Sciences, University of Chouaib Doukkali, El Jadida, 24000, Morocco
3 Laboratory of Information Technologies (LTI), National School of Applied Sciences, University of Chouaib Doukkali, El Jadida, 24000, Morocco

* Corresponding Author: Samira Marhraoui. Email:

(This article belongs to the Special Issue: Modelling, Optimisation and Forecasting of Photovoltaic and Photovoltaic thermal System Energy Production)

Energy Engineering 2025, 122(7), 2653-2672. https://doi.org/10.32604/ee.2025.064498

Received 17 February 2025; Accepted 09 May 2025; Issue published 27 June 2025

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 R² = 0.9793 and MAE = 0.0162 for the Poly-Si array, followed by Mono-Si (R² = 0.9768) and Hybrid-Si arrays (R² = 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.

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Keywords

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