## 4 Experimental Results
### Results of Subset1  
The results of the performance metrics obtained as a result of training the three models using the Subset1 dataset are presented in Table 3. It can be seen from the table that the RMSE and MAPE results of all the models are quite low, in other words, the prediction results and the actual values are very close to each other. Among these models, Model2, which is based on the LSTM architecture, has the lowest error. Figure 9 shows the one-month-ahead prediction results of Model1, Model2, and Model3, respectively, for test data in the Subset1 dataset. In this figure, the blue and orange lines indicate the actual load values and the predicted values, respectively. By analyzing the figure, it can be inferred that Model2 provides the best approximation of the actual load value using the Subset1 test data. In the study, LIME values are explained using RecurrentTabularExplainer. The local explanation of the LIME method is made on the scaled data, as is the actual model's prediction. Figure 10 shows the LIME results of Model1, Model2, and Model3, respectively, using the Subset1 dataset. As can be seen from the graphs, the output of the LIME method \begin{table}
\begin{tabular}{c c c} \hline \hline
**Subset1** & **RMSE** & **MAPE** \\ \hline Model1 & 0.078 & 0.081 \\ \hline Model2 & 0.074 & 0.077 \\ \hline Model3 & 0.086 & 0.093 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Subset1. Figure 9: Forecasting results of the models based on subset1: (**a**) Model1 results; (**b**) Model2 results; (**c**) Model3 results. consists of three stages. Accordingly, the progress bar on the left in Figure 10 shows the changing range of the resulting estimated 'SYSLoad' value. While the bar chart in the middle part of the figure shows the features that contribute positively and negatively to the prediction. The features in the bar chart in the middle of the figure are determined by LIME, and these features are selected among the features that have the most positive and negative impact on the prediction for the relevant sample, the table on the far right of the figure gives the list of the actual feature values. In this list on the right side of the figure for all models, feature values colored in orange indicate that they have a positive impact on the prediction value, while feature values colored in blue have a negative impact. The XAI results in that figure are from the fifth test instance for Model1, and from the second test instance for Model2 and Model3. Looking at the LIME results evaluated for the randomly selected instance for Model1 in the figure, we see that the prediction of the 'SYSLoad' value made by this model for the fifth sample is 0.8, and the min-max range varies between 0.76 and 0.83. For Model1, the lagged values of the 'DryBulb' feature for 5, 6, 7, 8, 10, 11, 12, and 13 days prior had a negative effect on the prediction value by obtaining a value less than 0.67, while the value of the \"Humidity\" feature for the current and one day lagged contributed positively by obtaining a value more than 0.74. This means that a 'Humidity' value greater than 0.74 had a positive effect on the prediction result, but a 'DryBulb' value greater than 0.67 had a negative effect on it. These values are different for each other feature as seen in the bar chart in the middle of the figure, and what determines this is the application of the LIME approach to the model. For Model1, the feature values in the list on the far right of the figure enable the model to produce an XAI prediction score of 0.80. In short, summarizing these results for Model1, the more lagged values of the 'DryBulb' feature have a negative impact on the model's prediction, but the more recent lagged values of the 'Humidity' feature make a positive contribution to the prediction. From the figure for Model2, we can see that the predicted 'SYSLoad' value for the second sample is 0.78, and the min-max range varies between 0.74 and 0.82.