## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Energy Potential Analysis Using Transformer Models  
Transformer models are useful for energy forecasting, especially when it comes to renewable energy sources, as evidenced by recent studies. In a variety of situations, these models do a better job than more conventional techniques like ARIMA and LSTM at capturing long-term dependencies in time series data. Transformers have shown success in predicting electricity consumption, multi-stream load forecasting, and power output for multiple wind farms. Their ability to handle complex correlations and dependencies among multiple data sources makes them particularly valuable for renewable energy forecasting [31; 32]. By leveraging contextual information and seasonality trends, Transformer-based models have achieved significant improvements in forecasting accuracy for both wind and solar power generation, reducing prediction errors compared to LSTM and RNN approaches [33]. These advancements highlight the potential of Transformer models in enhancing energy management and grid stability. Recent research has focused on improving long-term forecasting for multi-energy systems using Transformer-based  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Component** & **Description** & **Benefits** \\ \hline
1 & Photovoltaic (PV) Panels & Converts sunlight into electricity. & Renewable energy generation, zero emissions. \\ \hline
2 & Electrolyzers into hydrogen and oxygen. & Uses electricity to split water into hydrogen and oxygen. & Produces clean hydrogen, enables energy storage. \\ \hline
3 & Hydrogen Storage &
\begin{tabular}{p{56.9 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{4} & \multirow{2}{*}{Fuel Cells} & Converts stored hydrogen back into electricity. & Reliable power generation, supports grid stability. \\ \hline
5 & Distribution Infrastructure & \begin{tabular}{p{56.9 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{6} & Energy Management Systems & \begin{tabular}{p{56.9 pt} p{113.8 pt}} \hline \hline \end{tabular}  & \ \begin{tabular}{p{56.9 pt} p{113.8 pt}} \hline \hline \end{tabular}  &
\begin{tabular}{p{56.  
models. These models excel at capturing long-range dependencies, crucial for accurate predictions [34]. Several innovations have been proposed to address limitations in existing approaches. The PWDformer introduces a Deformable-Local aggregation mechanism to enhance time information aggregation and adapt to complex patterns [35]. Patchformer employs patch embedding to better capture local and global semantic dependencies in multivariate time series [36]. DTformer utilizes temporal top windowed attention and dual variable attention to handle extended temporal and intervariable dependencies while regulating computational complexity [37]. These models have demonstrated significant improvements in forecasting accuracy across various datasets, including energy, traffic, and weather. The advancements in Transformer-based models for time series analysis show promise in addressing the challenges of long-term multi-energy load forecasting in integrated energy systems. Transformer-based models have shown potential; for onshore wind and solar power output, the Informer model has demonstrated better prediction capabilities. Long Short-Term Memory (LSTM) models, however, have occasionally performed better than alternative strategies [38]. Higher accuracy in predicting energy generation rates and demand has been demonstrated by ensemble models utilizing neural networks and support vector machines [39]. In comparison to single models and other hybrid techniques, the H-Transformer hybrid system, which combines SARIMA with a Transformer neural network, has demonstrated better performance [40]. Furthermore, better performance and resilience in wind power prediction have been shown by the Powerformer model, a modified Transformer-based method [41]. Improvements in forecasting accuracy are critical for maintaining grid stability and integrating renewable energy sources.