## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Machine Learning Techniques for Fault Detection in Solar Panels
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Aspect** & **Description** & **Strengths** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Image Processing} & \begin{tabular}{c} Analyzes solar panel images \\ to detect defects like cracks \\ or hotspots. \\ \end{tabular} & \begin{tabular}{c} High accuracy in fault \\ identification and \\ classification. \\ \end{tabular} \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Pattern Recognition} & \begin{tabular}{c} Learns hierarchical features \\ to recognize subtle \\ degradation patterns. \\ \end{tabular} & \begin{tabular}{c} Effective in early detection of \\ potential issues. \\ \end{tabular} \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Scalability} & \begin{tabular}{c} Handles large volumes of \\ image data efficiently. \\ \end{tabular} & \begin{tabular}{c} Ideal for large-scale solar \\ installations with automated \\ monitoring. \\ \end{tabular} \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Versatility} & \begin{tabular}{c} Can be combined with other \\ data sources for enhanced \\ fault detection. \\ \end{tabular} & \begin{tabular}{c} Allows for a comprehensive \\ assessment of solar panel \\ health. \\ \end{tabular} \\ \hline \multirow{2}{*}{5} & \begin{tabular}{c} Computational \\ Requirements \\ \end{tabular} & \
\begin{tabular}{c} Requires substantial \\ computational resources for \\ training. \\ \end{tabular} &
\begin{tabular}{c} Delivers rapid and accurate \\ fault detection once trained. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CNN applications in solar panel fault detection. into ways to improve ResNet performance. For example, [78] proposed ResNetX, which adds a \"fold depth\" dimension and increases network disorder. Additionally, [79] showed that by using contemporary training techniques on the original ResNet-50 architecture, remarkable results can be achieved, including 80.4% top-1 accuracy on ImageNet without the need for additional data or distillation. ResNet-50's architecture demonstrates scalability across various applications and hardware implementations. It can be efficiently accelerated on FPGAs, achieving high throughput and low latency for complex image recognition tasks [80]. The model's scalability extends to large-scale distributed training on supercomputers, where it achieves over 90% scaling efficiency using up to 104 K \(\times\)86 cores, reducing training time to just 28 min. Additionally, the introduction of the Collapsed Ensemble technique allows ResNet-50 to achieve accuracy comparable to ResNet-152 while maintaining its original topology, further highlighting its scalability and potential for improved performance [81,82]. ResNet-50's versatility is further demonstrated by its application in Wi-Fi fingerprint-based indoor localization systems for several buildings and floors, which exhibit near-state-of-the-art performance with lower complexity and energy consumption [83]. Table 4 outlines the key elements of ResNet-50 in solar panel fault detection, highlighting its deep architecture, advanced feature extraction abilities, and scalability. The model's capacity to accurately detect and classify intricate faults makes it a vital component of sophisticated solar monitoring systems. Although ResNet-50 requires more computational resources and longer training periods, its enhancements in fault detection accuracy and system reliability make it an invaluable tool for sustaining the performance and efficiency of solar energy systems [84, 85, 86, 87, 88].