## 4 Experiment Study
### Ablation Study  
In this paper, we enumerate three different combinations to compare the experimental results with prediction scales of 1 h, 2 h, and 3 h, respectively. We confirm that the use of different modules in ELFNet has a positive impact on the final experimental results through a comparison between the errors obtained by ELFNet structures with those of four different modules:  
* CNN: the convolutional structure proposed in Section 2.2, without CA and SA modules;
* CNN-CA: the CNN structure with only the CA module added;
* CNN-SA: the CNN structure with only the SA module added;
* ELFNet: the proposed final model in this paper (shown by Figure 5 in Section 3). The prediction results of the above structures with different modules are shown in Table 2. Figure 6 shows that, for the proposed ELFNet model, the five evaluation metric values for RMSE, MAE, SMAPE, MAPE, and R are 0.0316, 0.0254, 0.2246, 0.3323, and 0.9920, respectively, at the 1 h forecast. The values of the evaluation measures when utilizing only the CNN structure without the CA and SA modules were 0.0465, 0.0373, 0.2806, 0.3769, and 0.9899. The evaluation metric values for the CNN-CA model were 0.0349, 0.0274, 0.2259, 0.3179, and 0.9916, respectively. The evaluation metric values were 0.0409, 0.0321, 0.2652, 0.4021, and 0.9892, corresponding to the CNN-SA model. Based on these metrics, it can be seen that the proposed ELFNet delivers the best prediction results on the four-evaluation metrics of RMSE, MAE, SMAPE, and R, and can provide accurate and effective prediction results. They all show that the CA module has a significant impact on the optimization of intrinsic features, while only the CNN-CA structural model achieves the best results for the MAPE measurements. The results of the 2 h ahead electricity load forecast for different deep learning structures are shown in Figure 7. The values of the evaluation metrics RMSE, MAE, SMAPE, MAPE, and R of the proposed model ELFNet with the 2 h ahead forecast are shown in Table 2 and are, respectively, 0.0346, 0.0270, 0.2402, 0.3689, and 0.9911, and the corresponding error metrics increase when the CA and SA modules are not used. The RMSE drops from 0.0442 to 0.0402 when the CA module is applied to the CNN framework, increasing forecast accuracy by 9%; other evaluation indicators also show varying degrees of improvement, demonstrating the beneficial effects of the CA and SA modules. How the CA and SA modules affect the electricity load forecasting accuracy is also shown. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Horizon** & **CNN** & **CNN-CA** & **CNN-SA** & **ELFNet** \\ \hline \multirow{3}{*}{RMSE} & 1 h & 0.0465 & 0.0349 & 0.0409 & 0.0316 \\  & 2 h & 0.0442 & 0.0402 & 0.0441 & 0.0346 \\  & 3 h & 0.0542 & 0.0479 & 0.0482 & 0.0417 \\ \hline \multirow{3}{*}{MAE} & 1 h & 0.0373 & 0.0274 & 0.0321 & 0.0254 \\  & 2 h & 0.0354 & 0.0317 & 0.0348 & 0.0270 \\  & 3 h & 0.0440 & 0.0377 & 0.0379 & 0.0333 \\ \hline \multirow{3}{*}{SMAPE} & 1 h & 0.2806 & 0.2259 & 0.2652 & 0.2246 \\  & 2 h & 0.2777 & 0.2525 & 0.2753 & 0.2402 \\  & 3 h & 0.3225 & 0.3050 & 0.2983 & 0.2813 \\ \hline \multirow{3}{*}{MAPE} & 1 h & 0.3769 & 0.3179 & 0.4021 & 0.3323 \\  & 2 h & 0.3939 & 0.3846 & 0.4941 & 0.3689 \\  & 3 h & 0.5631 & 0.4151 & 0.4495 & 0.4118 \\ \hline \multirow{3}{*}{R} & 1 h & 0.9899 & 0.9916 & 0.9892 & 0.9920 \\  & 2 h & 0.9895 & 0.9888 & 0.9869 & 0.9911 \\ \cline{1-1}  & 3 h & 0.9779 & 0.9840 & 0.9836 & 0.9892 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the evaluation metrics of CNN with different combinations of modules.