## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Machine Learning Techniques for Fault Detection in Solar Panels  
Because Convolutional Neural Networks (CNNs) are so good at processing images and recognizing patterns, they have become a popular method for identifying defects in solar panels. Imaging techniques such as electroluminescence imaging and infrared thermography are widely used in solar panel monitoring to identify anomalies and faults that may not be readily evident. Because CNNs can automatically learn and extract features from the data, they are especially well-suited for analyzing these images and enabling precise defect identification. With their great accuracy and efficiency, Convolutional Neural Networks (CNNs) have become a potent tool for identifying solar panel defects. Research has indicated that Convolutional Neural Networks (CNNs) are a useful tool for identifying flaws including fractures, shading, and possible induced degradation in a variety of image forms, including true color and electroluminescence images. With accuracy rates ranging from 91.1% for binary classification to 98.07% for multi-class defect detection, CNN-based models have demonstrated remarkable performance. These models perform better than manual feature detection techniques and are more robust against changes in input data. Additionally, to improve defect detection capabilities even further, CNNs can be coupled with other machine learning methods like Decision Trees and Support Vector Machines. The solar sector stands to benefit greatly from the revolutionary quality control that CNN-based fault detection systems promise to bring about. This will enhance module dependability and facilitate the rise of renewable energy sources [55; 56; 57; 58]. Automated fault identification of solar panel faults with the application of deep learning algorithms. Convolutional Neural Networks (CNNs) have demonstrated encouraging outcomes in the identification of different fault types, including as dust, bird marks, and microcracks. High accuracy in defect classification has been shown using transfer learning algorithms that use pre-trained models such as VGG16, VGG19, and AlexNet; some studies have reported accuracy rates above 90%. The application of Fully Convolutional Neural Networks (F-CNNs) has also been utilized to attain pixel-level prediction of photovoltaic backsheet degradation mechanisms. These automated inspection methods are much more efficient in maintaining solar panel performance than manual ones, and they also reduce subjectivity and human error. The accuracy of flaw identification in monocrystalline and polycrystalline solar panels has been significantly improved by the use of ensemble learn \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Application** & **Description** & **Benefits** \\ \hline \multirow{3}{*}{1} & Solar Power Generation & Predicts future solar energy output based on historical weather and irradiance data. & Improved grid integration, optimized energy storage and usage. \\ \hline \multirow{3}{*}{2} & Wind Energy Output Prediction & Forecasts wind power production by analyzing patterns in wind speed and direction. & Enhanced reliability of wind energy, better resource allocation. \\ \hline \multirow{3}{*}{3} & Energy Demand Forecasting & Estimates future energy consumption by considering factors like temperature and usage trends. & Balanced energy supply and demand, reduced risk of outages. \\ \hline \multirow{3}{*}{4} & Seasonal Trend Analysis & Captures long-term patterns in energy production and consumption. & Supports long-term planning and system optimization. \\ \hline \multirow{3}{*}{5} & Regional Energy Grid Forecasting & Scales predictions to cover larger geographical areas, such as cities or regions. & Enables efficient energy distribution and management. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Applications and benefits of transformer models in energy forecasting. ing algorithms [59; 60; 61; 62].