Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review
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 relationship...
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| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25000987 |
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| author | Oussama Khouili Mohamed Hanine Mohamed Louzazni Miguel Angel López Flores Eduardo García Villena Imran Ashraf |
| author_facet | Oussama Khouili Mohamed Hanine Mohamed Louzazni Miguel Angel López Flores Eduardo García Villena Imran Ashraf |
| author_sort | Oussama Khouili |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2008cf42f6b481d8ecc332f9dce74d9 |
| institution | DOAJ |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-b2008cf42f6b481d8ecc332f9dce74d92025-08-20T03:10:41ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910173510.1016/j.esr.2025.101735Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic reviewOussama Khouili0Mohamed Hanine1Mohamed Louzazni2Miguel Angel López Flores3Eduardo García Villena4Imran Ashraf5LTI Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, 24000, MoroccoLTI Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, 24000, MoroccoScience Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, MoroccoUniversidad Europea del Atlántico, Isabel Torres 21, 39011, Santander, Spain; Universidad Internacional Iberoamericana Campeche, 24560, Mexico; Instituto Politécnico Nacional, UPIICSA, Ciudad de México, MexicoUniversidad Europea del Atlántico, Isabel Torres 21, 39011, Santander, Spain; Universidad Internacional Iberoamericana Arecibo, Puerto Rico, 00613, USA; Universidade Internacional do Cuanza, Cuito Bié, AngolaInformation and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2211467X25000987Deep learningPV power forecastingSolar radiation forecastingSystematic review |
| spellingShingle | Oussama Khouili Mohamed Hanine Mohamed Louzazni Miguel Angel López Flores Eduardo García Villena Imran Ashraf Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review Energy Strategy Reviews Deep learning PV power forecasting Solar radiation forecasting Systematic review |
| title | Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review |
| title_full | Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review |
| title_fullStr | Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review |
| title_full_unstemmed | Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review |
| title_short | Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review |
| title_sort | evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting a systematic review |
| topic | Deep learning PV power forecasting Solar radiation forecasting Systematic review |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25000987 |
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