review · Energy Strategy Reviews
Accurate forecasting of solar and photovoltaic power is critical for grid integration, energy storage management, and overall system efficiency. Deep learning models effectively handle the complex, non-linear relationships present in solar generation data. A systematic review of 26 core research studies reveals that Long Short-Term Memory networks are the most widely applied architecture, appearing in nearly a third of the assessed literature, closely followed by Convolutional Neural Networks. For data preparation, Wavelet Transform serves as the predominant data decomposition technique, while Pearson Correlation is the preferred method for feature selection. Environmental variables, particularly ambient temperature, atmospheric pressure, and humidity, represent the most frequent input features for these predictive systems. Addressing persistent challenges requires developing more interpretable, robust architectures and incorporating multi-source data to raise forecast reliability.
Solar energy production fluctuates with weather conditions, making it challenging to balance power grids and manage battery storage effectively. Synthesising how advanced artificial intelligence predicts solar output helps engineers and grid planners choose the best algorithms. Improving forecast accuracy directly supports the reliable, large-scale adoption of renewable energy into electricity networks without compromising grid stability.
This research informs the design of predictive analytics for commercial grid management software, renewable energy trading, and solar farm operations. The prospective users are grid operators, energy utilities, and solar asset managers aiming to minimise supply imbalances. Because the findings stem from a systematic literature review rather than a deployed prototype, the underlying concepts remain at an early, analytical stage that requires practical translation into operational software.
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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. • Review of DL approaches on solar and photovoltaic power forecasting. • Data preprocessing techniques and feature engineering approach for DL algorithms. • Prominent features employed for DL-based solar forecasting. • Obstacles in DL-based solar forecasting.
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DOI: 10.1016/j.esr.2025.101735
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