## 4 Experiments and Results Analysis
### Performance Comparison of Different Deep Learning Models
#### 4.3.3 Single Model Performance Comparison Results  
Table 2 shows the MAPE values of all models under different prediction scenarios, with the best-performing model in each scenario marked in bold. Figure 3 shows the visualization results of MAPE values for each model with sliding time windows of 60 min and 30 min. From MAPE performance, we observe the following:  
(1) Deep learning models generally outperform shallow learning models: In most prediction scenarios, deep learning models (LSTM-DNN and DNN models) have lower MAPE values than shallow learning models. (2) Sliding time window length affects model performance:  
When the sliding time window is 60 min, different DNN models perform differently under different prediction scales, with no single model performing best in all scenarios. When the sliding time window is 30 min, the LSTM-DNN model achieves the best MAPE values under all prediction scales, showing clear advantages. (3) Performance differences in shallow learning models: Linear regression and ridge regression perform similarly and relatively well on MAPE, while LASSO regression and random forest perform poorly. (4) Prediction scale impact: As the prediction scale increases (from 2 min to 60 min), MAPE values of all models generally show an upward trend, indicating that short-term predictions are more accurate than long-term predictions. In terms of RMSE performance, as shown in Table 3, the results show different patterns: Figure 4 shows the visualization results of RMSE values for each model with sliding time windows of 60 min and 30 min. From RMSE performance:  
(1) Advantages of shallow learning models emerge: In most prediction scenarios, shallow learning models, particularly random forest models, perform better than deep learning models on RMSE. (2) Sliding time window length impact:  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MAPE (\%), Sliding Time Window = 60 min**} & \multicolumn{3}{c}{**MAPE (\%), Sliding Time Window = 30 min**} \\ \hline Prediction Horizon (mins) & 2 & 10 & 20 & 30 & 60 & 2 & 10 & 20 & 30 & 60 \\ Linear Regression & 8.962 & 15.698 & 17.760 & 18.882 & 20.792 & 8.937 & 15.615 & 17.582 & 18.722 & 20.782 \\ Ridge Regression & 8.962 & 15.698 & 17.760 & 18.882 & 20.792 & 8.937 & 15.615 & 17.582 & 18.722 & 20.782 \\ LASSO Regression & 32.310 & 28.689 & 26.806 & 25.658 & 23.523 & 32.312 & 28.731 & 26.867 & 25.701 & 23.542 \\ Random Forest & 32.270 & 29.798 & 28.793 & 28.034 & 26.158 & 32.289 & 29.875 & 28.986 & 28.228 & 26.264 \\ LSTM-DNN & 39.403 & 15.617 & 18.633 & 18.790 & 21.525 & 8.647 & 14.292 & 15.529 & 15.634 & 17.411 \\ DNN64\_2 & 10.584 & 15.219 & 17.694 & 17.594 & 18.262 & 9.947 & 21.350 & 17.511 & 18.742 & 19.766 \\ DNN364\_4 & 11.549 & 17.851 & 18.433 & 18.095 & 23.310 & 13.495 & 14.386 & 17.672 & 17.705 & 20.717 \\ DNN128\_2 & 10.376 & 20.101 & 16.549 & 17.096 & 23.622 & 8.854 & 15.149 & 17.763 & 20.178 & 18.967 \\ DNN128\_4 & 10.699 & 16.069 & 17.627 & 19.749 & 22.794 & 9.081 & 14.575 & 22.282 & 19.043 & 23.114 \\ DNN256\_2 & 9.108 & 18.223 & 17.320 & 24.121 & 18i. When the sliding time window is 60 min, LASSO regression achieves the optimal RMSE value at a 2 min prediction scale, while random forest achieves optimal RMSE values at 10, 20, 30, and 60 min prediction scales. ii. When the sliding time window is 30 min, LSTM-DNN achieves optimal RMSE values at 2 and 20 min prediction scales, while random forest achieves optimal RMSE values at 10, 30, and 60 min prediction scales. (3) LASSO regression performance contrast on RMSE: although LASSO regression performs poorly on MAPE, its RMSE performance surpasses linear regression and ridge regression. (4) Prediction scale impact: similar to MAPE, RMSE values generally show an upward trend as the prediction scale increases. These results indicate that different models may perform differently on different evaluation metrics, with no single model having advantages across all scenarios and metrics.