The Role of Deep Learning in Enhancing Solar Panel Efficiency: A Review of Models and Metrics

The rising desire to get long-term power has stepped up the utilisation of solar photovoltaic (PV) systems all over the world. Nonetheless, keeping the solar panels in optimal performance is a major concern because of the changeable environmental conditions, wear and tear of the equipment and breakdown of operations. Deep learning (DL) has become an efficient solution of offering sophistication in predicting solar power generation, fault detection, and optimization of the performance. The review will go in depth over several DL methodologies, such as, CNN, LSTM, Transformer-based models, Reinforcement Learning, GANs, and hybrid architectures of CNN-LSTM. The implementation of each model, performance metrics of MAE, RMSE, R2, and MAPE as well as weak points and advantages of each model, are provided in their relation to increasing PV efficiency. Moreover, the paper covers real-life data, real-life implementations, and comparative performance of these models in addressing critical issues in the solar energy systems. Nonetheless, despite the significant progress, there are still a number of challenges that need to be addressed, among which are the data sparsity, inability to generalize the models to different areas of interest, computational limitations, and inability to interpret the models. To resolve these problems, future directions outlined by the present paper include the creation of lightweight models of edge-AI, the use of transfer learning to achieve regional adaptability, the possibility of Explainable AI (XAI), and the combination of deep learning and IoT and digital twin technology. I would say that the given comprehensive review is an excellent source of information that can be used by researchers, engineers, and policymakers who want to use deep learning to make solar panels as efficient as possible to deliver more resilient, reliable, and sustainable energy delivery.