## 4 Experiments
### Experimental Results  
We compared the proposed LSTM-SCN model with other baseline models, and Table 5 presents the performance metrics of each model on the forecasting set. The LSTM-SCN model demonstrated the best performance across all metrics, with an RMSE of 56.970, an MAE of 43.033, and a MAPE of 0.492%. The RMSE and MAPE values of the LSTM-SCN model are reduced by 6.016 and 0.053%, respectively, compared to the second-best performing CNN-LSTM model. Additionally, the MAE of the LSTM-SCN model is decreased by 8.846 compared to the second-best performing GRU model. These results demonstrate the superior performance of the LSTM-SCN model compared to the others. In the field of electricity load forecasting, while most models achieve good forecasting performance in low-volatility regions, their true performance is often revealed in high  
Figure 8: Feature acquisition using the sliding window method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** \\ \hline LSTM [42] & 93.797 & 72.175 & 0.819\% \\ BiLSTM [17] & 124.220 & 100.635 & 1.179\% \\ GRU [43] & 69.620 & 51.879 & 0.593\% \\ SCN & 97.626 & 74.071 & 0.847\% \\ CNN-LSTM [5] & 62.986 & 57.829 & 0.545\% \\ LSTM-RVFL [29] & 187.695 & 167.114 & 1.866\% \\ LSTM-SCN (our) & 56.970 & 43.033 & 0.492\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for each model on the forecasting set. volatility regions. To fully evaluate LSTM-SCN's performance, we selected five high-volatility periods from the last month of the test set, each with 40 data points. Figure 9 and Table 6 detail the selection of these regions and their data ranges, respectively. This detailed analysis enables us to more accurately assess the model's applicability and accuracy in real-world scenarios with high volatility. Table 7 displays the performance metrics for each model in the five selected high-volatility regions. The analysis results indicate that the LSTM-SCN model demonstrates optimal performance across all five high-volatility regions. In Region 1, we compare the LSTM-SCN model to the second-best performing model. The LSTM-SCN model achieves reductions of 37.901 in RMSE, 29.781 in MAE, and 0.391% in MAPE, respectively. In Regions 2, 3, 4, and 5, the reductions in these three metrics for the LSTM-SCN model are as follows: 39.923 in RMSE, 31.502 in MAE, and 0.389% in MAPE for Region 2; 42.201 in RMSE, 29.990 in MAE, and 0.420% in MAPE for Region 3; 34.951 in RMSE, 24.734 in MAE, and 0.332% in MAPE for Region 4; and 34.552 in RMSE, 24.074 in MAE, and 0.325% in MAPE for Region 5. These results demonstrate that the LSTM-SCN model exhibits excellent performance in handling high-volatility regions, thereby highlighting the advantages of the LSTM-SCN model in the field of power load forecasting. To explain why the LSTM-SCN model exhibits optimal performance across the five high-volatility regions, we offer the following analysis:  
1. Adaptive Dynamic Network Structure: The LSTM-SCN model relies on a supervision mechanism during prediction, whereas other models depend on the backpropagation mechanism of fully connected layers. Backpropagation requires gradient optimization and relies on a fixed network structure, while the supervision mechanism can dynamically adjust the network structure based on data and task requirements. This adaptability allows the model to construct an optimal network architecture during learning, avoiding overfitting and unnecessary complexity. 2. Robustness to Gradient Issues: Backpropagation in deep networks often faces challenges like vanishing or exploding gradients, which hinder model convergence and learning efficiency. In contrast, the supervision mechanism does not rely entirely on \begin{table}
\begin{tabular}{c c c c c c} \hline
**Region Name** & **Region 1** & **Region 2** & **Region 3** & **Region 4** & **Region 5** \\ \hline Data Range & [200; 240] & [540; 580] & [875; 915] & [1165; 1205] & [1310; 1350) \\ \hline \end{tabular}
\end{table}