TY  - JOUR
A1  - Khouili, Oussama
A1  - Hanine, Mohamed
A1  - Louzazni, Mohamed
A1  - López Flores, Miguel Ángel
A1  - García Villena, Eduardo
A1  - Ashraf, Imran
ID  - uninimx17794
TI  - Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review
UR  - http://doi.org/10.1016/j.esr.2025.101735
SN  - 2211467X
VL  - 59
KW  - Deep learning; PV power forecasting; Solar radiation forecasting; Systematic review
N2  - Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
JF  - Energy Strategy Reviews
Y1  - 2025/05//
AV  - public
ER  -