## 4 Experiment Study
### Comparative Experiment Results and Analysis  
In this subsection, we compare our proposed ELFNet model with several prevalent deep learning algorithms for tackling image data in order to further validate the multi-scale prediction performance of our model. The deep learning methods we selected to take part in this comparison are ResNet-18, ResNeXt-50, and GoogLeNet. Table 3 summarizes the comprehensive results for following the evaluation metrics: RMSE, MAE, MAPE, SMAPE, and R.  
The results for the 1 h ahead forecasting and the discrepancies between the predicted results and actual values are shown in Figure 9. Table 3 shows that, for the proposed forecasting model ELFNet, the values of the evaluation metrics are 0.0316, 0.0254, 0.2246, 0.3323, and 0.9920, respectively, under the 1 h ahead forecast. The evaluation metrics for ResNeXt-50 are 0.0514, 0.0404, 0.3070, 0.4520, and 0.9812, respectively, whereas those for ResNet-18 are 0.0569, 0.0409, 0.0307, 0.4520, and 0.9812. The evaluation metrics for GoogLeNet have values of 0.0506, 0.0395, 0.3013, 0.3009, and 0.8806. A visualization of the experimental data can be seen in Figure 10. It is apparent from the statistics of these evaluation metrics that ResNet-18 has the poorest prediction results and ELFNet has the best prediction performance for all evaluation metrics. The scatter plot composed of the real and actual value data pairings is presented in Figure 9c. The black dashed line in the figure, which runs from the lower left corner to the upper right corner, is where all points should fall in perfect situations, so the nearer the scatter points to this line, the more accurate the model predictions will be. It is obvious that ELFNet performs admirably in this regard compared to all other models. The results for the 2 h ahead forecasting are shown in Figures 11 and 12. Table 3 shows that the five evaluation indicators for the prediction ELFNet model developed in this paper are 0.0346, 0.0270, 0.2402, 0.3689, and 0.9911, respectively, under the assumption that the forecast scale is 2 h. Compared to other deep learning techniques, ELFNet produced the greatest results using the RMSE assessment measure, with GoogLeNet coming in second, with a score of 0.0464. The least accurate prediction was made by ResNet-18, which showed that relying only on residual connections to mined features is insufficient to capture all of the available characteristics of the experimental data. In this comparative experiment, ELFNet differed significantly from the other three deep learning methods using the evaluation index of R. The R for the other three techniques varied greatly. The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Horizon** & **ResNet-18** & **ResNeXt-50** & **GoogLeNet** & **ELFNet** \\ \hline \multirow{4}{*}{RMSE} & 1 h & 0.0569 & 0.0514 & 0.0506 & 0.0316 \\  & 2 h & 0.0700 & 0.0586 & 0.0464 & 0.0346 \\  & 3 h & 0.0836 & 0.0661 & 0.0541 & 0.0417 \\  & 1 h & 0.0445 & 0.0404 & 0.0395 & 0.0254 \\ MAE & 2 h & 0.0563 & 0.0460 & 0.0353 & 0.0270 \\  & 3 h & 0.0671 & 0.0522 & 0.0415 & 0.0333 \\  & 1 h & 0.3322 & 0.3070 & 0.3013 & 0.2246 \\ SMAPE & 2 h & 0.4161 & 0.3612 & 0.2697 & 0.2402 \\  & 3 h & 0.4996 & 0.4186 & 0.3009 & 0.2813 \\  & 1 h & 0.4498 & 0.4520 & 0.3812 & 0.3323 \\ MAPE & 2 h & 0.5449 & 0.6055 & 0.3762 & 0.3689 \\  & 3 h & 0.6703 & 0.6727 & 0.4141 & 0.4118 \\  & 1 h & 0.9752 & 0.9812 & 0.9806 & 0.9920 \\ R & 2 h & 0.9738 & 0.9792 & 0.9841 & 0.9911 \\  & 3 h & 0.9657 & 0.9754 & 0.9784 & 0.9892 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the evaluation metrics for ELFNet and deep learning methods. Figure 12c shows that the degree of dispersion is the minimum and that the actual value and anticipated value of ELFNet are more in line with the middle black dotted line. It also shows how effective and precise ELFNet's prediction capabilities are. The results for the 3 h ahead forecasting are shown in Figures 13 and 14.