A Systematic Review of Advances in Deep Learning Architectures for Efficient and Sustainable Photovoltaic Solar Tracking: Research Challenges and Future Directions

Ali Alhazmi (), Kholoud Maswadi and Christopher Ifeanyi Eke
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Ali Alhazmi: Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia
Kholoud Maswadi: Department of Management Information Systems, College of Business, Jazan University, Jazan 45142, Saudi Arabia
Christopher Ifeanyi Eke: Department of Computer Science, Faculty of Computing, Federal University of Lafia, P.M.B 146, Lafia 950101, Nigeria

Sustainability, 2025, vol. 17, issue 21, 1-44

Abstract: The swift advancement of renewable energy technology has highlighted the need for effective photovoltaic (PV) solar energy tracking systems. Deep learning (DL) has surfaced as a promising method to improve the precision and efficacy of photovoltaic (PV) solar tracking by utilising complicated patterns in meteorological and PV system data. This systematic literature review (SLR) seeks to offer a thorough examination of the progress in deep learning architectures for photovoltaic solar energy tracking over the last decade (2016–2025). The review was structured around four research questions (RQs) aimed at identifying prevalent deep learning architectures, datasets, performance metrics, and issues within the context of deep learning-based PV solar tracking systems. The present research utilised SLR methodology to analyse 64 high-quality publications from reputed academic databases like IEEE Xplore, Science Direct, Springer, and MDPI. The results indicated that deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, are extensively employed to improve the accuracy and efficiency of photovoltaic solar tracking systems. Widely utilised datasets comprised meteorological data, photovoltaic system data, time series data, temperature data, and image data. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), were employed to assess model efficacy. Identified significant challenges encompass inadequate data quality, restricted availability, high computing complexity, and issues in model generalisation. Future research should concentrate on enhancing data quality and accessibility, creating generalised models, minimising computational complexity, and integrating deep learning with real-time photovoltaic systems. Resolving these challenges would facilitate advancements in efficient, reliable, and sustainable photovoltaic solar tracking systems, hence promoting the wider adoption of renewable energy technology. This review emphasises the capability of deep learning to transform photovoltaic solar tracking and stresses the necessity for interdisciplinary collaboration to address current limitations.

Keywords: deep learning; photovoltaic solar energy tracking; convolutional neural networks (CNNs); long short-term memory (LSTM); transformers; hybrid models; performance metrics; datasets; renewable energy systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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