## 4 Results
### Model Training and Evaluation  
The training and evaluation of the main model were conducted on a dataset consisting of meteorological features and corresponding energy production values. The results from the training process, alongside the final test performance, are critical in understanding the model's predictive capabilities and its potential limitations. The training process of the model spanned 2000 epochs, during which the loss function was monitored and minimized using the Adam optimizer. The loss, represented by the mean squared error (MSE), started at a relatively higher value and showed a consistent decline as the training progressed. Specifically, in Figure 6 the training versus validation loss over the training of the model is presented. The training and validation loss curves demonstrate the model's effective learning and generalization over time. Initially, both the training and validation losses are high, indicating that the model starts with significant prediction errors. However, within the first 20 epochs, there is a rapid decrease in both losses, reflecting the model's quick improvement in learning patterns from the data. As training progresses, both losses stabilize at a low value, near 0.02-0.03, suggesting that the model is minimizing errors effectively. The close alignment between the training and validation losses throughout the training process indicates good generalization with minimal signs of overfitting. Small fluctuations observed after epoch 400 are typical during the fine-tuning phase and do not impact the overall performance significantly. These trends suggest that the model is both accurate and robust, performing well on both the training and validation sets. To comprehensively evaluate the model's performance, several metrics were calculated on the test data, including MAE, RMSE, and MAPE. These metrics provide insights into the model's predictive accuracy and the nature of the errors. In Table 3 the evaluation results for the testing phase are presented. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RMSE** & **MAE** & **MAPE** \\ \hline
102.09 & 88.72 & 17.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluated metrics for the main prediction model. Figure 6: Training vs validation loss curve. The MAE was calculated to be approximately 88.72 MWh. This metric provides an average magnitude of the errors in the predictions, without considering their direction. The RMSE was found to be 102.09 MWh. This metric is more sensitive to larger errors due to the squaring of differences before averaging. The higher RMSE compared with MAE indicates that there are some larger deviations in the model's predictions, which could be due to outliers or to periods where the model fails to capture sudden changes in energy production. The MAPE was 17.5%, which is especially useful for understanding the model's performance relative to the scale of the data. The model was able to predict the output but lacked accuracy in the prediction of abrupt changes and outliers. The comparison of predicted versus actual energy production values, as visualized in the provided graph, highlights several key points, as follows:  
* Alignment and Divergence: While the predicted values generally align with the actual energy production trends, there are clear instances of both underestimation and overestimation. These discrepancies could be attributed to the model's sensitivity to specific meteorological conditions. * Pattern Recognition: The model appears to capture the broader patterns of energy production, likely driven by recurring meteorological conditions. However, the presence of significant errors in certain periods suggests that the model might not fully account for all relevant factors influencing energy production, such as sudden weather changes or other external variables. The overall performance of the main model indicates its potential for energy production prediction (Figure 7).