## 3 Results and Discussion
### Learning Curves and Model Features  
The learning curves derived from the five-fold cross-validation of the proposed CNN-LSTM model with an attention mechanism demonstrate robust performance and effective \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & **2022** & & **2023** & \\ \hline
**No.** & **Model** & **MAE** & **MAPE** & **MAE** & **MAPE** \\ \hline
1 & CNN & 1096 & 17.9 & 1102 & 18 \\
2 & DNN & 816 & 11.3 & 1010 & 16 \\
3 & DNN-LSTM & 216 & 3.11 & 217 & 3.13 \\
4 & CNN and & & & & \\  & LSTM—Attention & 171 & 2.2 & 194 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for the dataset of years 2022 and 2023.  
generalization for electricity demand forecasting, as illustrated in Figure 5. The implementation of early stopping, with a patience of 5 epochs, successfully halted training around 10-15 epochs, preventing overfitting and ensuring stable validation loss across all folds. The close alignment between training and validation losses, facilitated by regularization techniques such as dropout, L2 regularization, and batch normalization, underscores the model's ability to capture the inherent seasonality and trends in electricity demand without memorizing noise. In this study, we leveraged LIME (Local Interpretable Model-Agnostic Explanations) to identify the most influential input parameters affecting our model's decisions. The feature importance visualization shown in Figure 6 highlights the key variables that contributed the most to the predictions, providing a clear understanding of their impact on electricity demand forecasting. The LIME feature importance analysis for the electricity demand prediction at a randomly selected timestamp, derived from the CNN-LSTM-Attention model, reveals the primary factors shaping this single instance, as presented in Figure 6. The HDD  
Figure 5: Learning curves of the proposed model. Figure 6: LIME feature analysis. emerges as the most influential, with the HDD at a 17-day lag (HDD_t-17 \(>\) 0.40) exhibiting the greatest positive contribution (weight \(\approx\) 0.225), followed by HDD_t-3 (\(>\)0.40, \(\approx\)0.026) and HDD_t-10 (\(>\)0.40, \(\approx\)0.021). These results underscore the pivotal role of temperature-related features across various lags in driving the demand forecast for this specific case. Furthermore, prior demand values at lags of 7 and 14 days (DEMAND_t-7 \(>\) 0.40, \(\approx\)0.016; DEMAND_t-14 \(>\) 0.40, \(\approx\)0.009) positively influence the prediction, highlighting the model's dependence on historical demand trends for this timestamp. Wind speed features display opposing effects: moderate wind speed at a 10-day lag (0.25 \(<\) wind_speed_t-10 \(\leq\) 0.35, \(\approx\)0.007) boosts the predicted demand, while low wind speed at a 22-day lag (wind_speed_t-22 \(\leq\) 0.20, \(\approx-\)0.006) exerts a minor negative impact. These results highlight the model's strength in integrating diverse temporal and meteorological inputs, with the HDD and lagged demand emerging as dominant predictors. This interpretability, grounded in the feature importance analysis, enhances the model's reliability and practical value for electricity demand forecasting, demonstrating its capacity to model complex real-world dynamics. The proposed CNN-LSTM with attention model substantially enhances electricity demand forecasting accuracy compared to the baseline CNN-LSTM model, demonstrating the value of incorporating an attention mechanism to capture intricate temporal dependencies. This improvement, however, comes at a significant computational expense. Training the proposed model on a dataset of 8000 samples using Google Colab with a GPU is markedly more time-intensive than training simpler architectures. For instance, the CNN-LSTM-Attention model, with 1,630,047 parameters, required approximately 50 min to train with a batch size of 128 and 500 epochs, achieving a MAPE of 1.69%.