## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Emerging Trends in Renewable Energy Monitoring  
Monitoring and fault detection for renewable energy is being revolutionized by the merging of AI and IoT (AIoT) technologies. Real-time data collecting from several renewable energy sources is made possible by IoT devices, and AI algorithms can examine this data to identify anomalies and schedule maintenance. The Isolation Forest algorithm, for instance, has shown effectiveness in identifying faults in renewable energy systems. AI-driven predictive maintenance minimizes downtime, extends equipment lifespan, and optimizes energy output by adapting to changing environmental conditions. These developments help to increase the effectiveness, dependability, and affordability of renewable energy installations. As artificial intelligence (AI) technology develops, it is anticipated that its influence on renewable energy will intensify, hastening the shift to greener energy sources [89, 90, 91]. Insights, anomaly identification, and fault diagnosis for hybrid energy \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{3}{*}{1} & \multirow{3}{*}{Deeper Architecture} & Comprises 50 layers, enabling it to capture complex patterns and features. & Enhanced ability to detect subtle and complex faults in solar panels. \\ \cline{2-4}  & & Utilizes shortcut connections to prevent vanishing gradients. & Improves learning efficiency in deep networks. \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Feature Extraction} & Excels at identifying minute differences in image data. & High accuracy in fault detection across varied fault types. \\ \cline{2-4}  & & Suitable for large-scale solar installations with diverse fault detection needs. & Ensures reliable performance in extensive solar monitoring systems. \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Computational Requirements} & Requires significant computational resources and longer training times. & Delivers superior accuracy and reduces false positives. \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ResNet-50 in solar panel fault detection. systems are provided by real-time monitoring systems that integrate AI and IoT, supporting sustainability and effective energy management [92]. The combination of artificial intelligence (AI) with the Internet of Things (IoT) for intelligent monitoring and predictive maintenance across multiple domains, including smart grids, has been the focus of recent study. IoT sensor data abnormalities are being found using machine learning techniques like autoencoders, one-class SVM, and isolation forests in order to develop proactive maintenance plans [93]. With temperature detection accuracy reaching 98.7%, real-time monitoring paired with AI-based predictive analytics has demonstrated notable gains in the early detection of problematic trends [94]. Large-scale IoT data processing requires artificial intelligence (AI) in order to support both post-event and real-time analysis [95]. Machine learning algorithms are being utilized in renewable energy systems to optimize smart grids, estimate energy supply and demand, and increase energy efficiency. These developments are expected to improve grid sustainability, lower operating costs, and improve asset management [96]. The integration of AI and IoT enhances fault detection capabilities in industrial systems. The precision and accuracy of fault identification and localization can be increased by training deep learning models to identify certain fault signs in IoT sensor data [97]. Machine learning techniques, combined with IIoT architectures, enable real-time data collection and processing for fault detection, prediction, and prevention [98]. Energy-aware intelligent fault detection schemes using optimized deep learning mechanisms can significantly improve fault detection accuracy while reducing false alarm rates and energy consumption in IoT-enabled wireless sensor networks [99].