## 4 Results and Discussion  
### Energy Potential Analysis  
The three main metrics used in this section to assess the Transformer model's prediction of the university's solar hydrogen energy potential are Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Error 2 (MAE2). Forecasts of energy production that are accurate are necessary to maximize energy storage, resource management, and the overall effectiveness of renewable energy systems. The university's solar hydrogen output was predicted using the Transformer model, a potent machine learning method renowned for its capacity to spot intricate patterns in time series data. The average difference between the model's predictions and the actual energy output, as indicated by the MAE result (Figure 1) of 1685.04, is around 1685 units. The average prediction error (MAPE), which may be simply interpreted, is a simple metric that gives information about the overall accuracy of the model. Figure 1: Mean Absolute Error (MAE) result. The average of the squared discrepancies between the expected and actual values is represented by the MSE result (Figure 2), which is 4,361,833.37. Larger errors are given more weight in this metric, since it squares the errors before averaging. Even if the majority of predictions are quite near to the actual values, there are certain cases with sizable mistakes, which raise the overall MSE, as indicated by the relatively high MSE. The MAE2 result (Figure 3) of 1685.038, which is a variation of the MAE, underscores the importance of minimizing errors. This result, closely aligning with the original MAE, indicates a consistent average deviation of around 1685 units, even when the error is squared. These metrics form a comprehensive evaluation of the Transformer model's accuracy. The relatively low MAE and MAE2 suggest the model is generally effective, while the higher MSE points to areas where larger errors might need to be addressed, especially when extreme values are involved. This analysis feeds directly into the broader narrative of optimizing renewable energy systems by improving prediction accuracy, leading to more efficient energy management. The detailed evaluation using these metrics directly informs both the Model Performance and Error Analysis sections. By understanding how the Transformer model performs in predicting solar hydrogen energy production, we can identify its strengths and weaknesses, ultimately guiding future improvements and refinements to enhance its real-world application. The Transformer model's ability to forecast the university's solar hydrogen energy potential is evaluated visually in the graph in Figure 4 (Model Performance). The model's predictions (represented by the orange line) are compared against the actual measured values (blue line) over a substantial period, revealing key insights into its efficacy. Overall, the model demonstrates a commendable ability to track the fluctuations and trends in solar hydrogen production. The close alignment of the predicted and real values in many instances suggests that the Transformer model is effective at learning and generalizing the patterns of energy generation, which are influenced by factors such as sunlight availability, seasonal variations, and perhaps the operational efficiency of the solar-hydrogen systems. This alignment implies that the model can serve as a reliable tool for forecasting the university's energy potential, aiding in the strategic planning of energy use and storage. Figure 3: Mean Squared Error 2 (MSE2) result. Figure 2: Mean Squared Error (MSE) result. Despite the Transformer model's generally strong performance, the graph also uncovers several areas where its predictions fall short, highlighting some limitations and sources of error (Error Analysis). Notably, there are significant discrepancies between the predicted and actual values during certain periods, particularly around the 1800 to 2100 mark on the X-axis.