eprintid: 17794 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/77/94 datestamp: 2025-05-19 23:30:16 lastmod: 2025-05-19 23:30:17 status_changed: 2025-05-19 23:30:16 type: article metadata_visibility: show creators_name: Khouili, Oussama creators_name: Hanine, Mohamed creators_name: Louzazni, Mohamed creators_name: López Flores, Miguel Ángel creators_name: García Villena, Eduardo creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: miguelangel.lopez@uneatlantico.es creators_id: eduardo.garcia@uneatlantico.es creators_id: title: Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Deep learning; PV power forecasting; Solar radiation forecasting; Systematic review abstract: 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. date: 2025-05 publication: Energy Strategy Reviews volume: 59 pagerange: 101735 id_number: doi:10.1016/j.esr.2025.101735 refereed: TRUE issn: 2211467X official_url: http://doi.org/10.1016/j.esr.2025.101735 access: open language: en citation: Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Universidad Internacional do Cuanza > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/unic=5Fproduccion=5Fcientifica.html> Abierto Inglés 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. metadata Khouili, Oussama; Hanine, Mohamed; Louzazni, Mohamed; López Flores, Miguel Ángel; García Villena, Eduardo y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, miguelangel.lopez@uneatlantico.es, eduardo.garcia@uneatlantico.es, SIN ESPECIFICAR <http://repositorio.unini.edu.mx/id/eprint/17794/1/s41598-025-95836-8.pdf> (2025) Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review. Energy Strategy Reviews, 59. p. 101735. ISSN 2211467X document_url: http://repositorio.unini.edu.mx/id/eprint/17794/1/s41598-025-95836-8.pdf