## 4 Results and Analysis
### Result Analysis  
Table 1 offers a comprehensive predictive outcome of the AJODL-DSSEM model on two datasets. Figure 4 reports a brief result analysis of the AJODL-DSSEM model under different cases of the IHEPC dataset. The figure implied that the AJODL-DSSEM model attained enhanced performance in all aspects. For instance, with the autumn season, the AJODL-DSSEM model obtained an RMSE, MAE, and MAPE of 0.291, 0.270, and 0.349, respectively. Furthermore, with the spring season, the AJODL-DSSEM technique reached an RMSE, MAE, and MAPE of 0.271, 0.218, and 0.330, respectively. In addition, with winter the season, the AJODL-DSSEM methodology obtained an RMSE, MAE, and MAPE of 0.319, 0.280, and 0.302, respectively.  
Figure 5 demonstrates a detailed result analysis of the AJODL-DSSEM approach under distinct cases of the ISO-NE dataset. The figure exposed that the AJODL-DSSEM technique attained improved performance under all aspects. For instance, with the autumn season, the AJODL-DSSEM model achieved an RMSE, MAE, and MAPE of 0.413, 0.333, and 0.256, respectively. Moreover, with the spring season, the AJODL-DSSEM algorithm obtained  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Label** & **RMSE** & **MAE** & **MAPE** \\ \hline
**IHEPC Dataset** & & & \\ \hline Autumn & 0.291 & 0.270 & 0.349 \\ \hline Summer & 0.330 & 0.281 & 0.335 \\ \hline Spring & 0.271 & 0.218 & 0.330 \\ \hline Winter & 0.319 & 0.280 & 0.302 \\ \hline
**Average** & **0.303** & **0.262** & **0.329** \\ \hline
**ISO-NE Dataset** & & & \\ \hline Autumn & 0.413 & 0.333 & 0.256 \\ \hline Summer & 0.453 & 0.364 & 0.241 \\ \hline Spring & 0.480 & 0.422 & 0.218 \\ \hline Winter & 0.479 & 0.416 & 0.231 \\ \hline
**Average** & **0.456** & **0.384** & 0.237 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result analysis of AJODL-DSSEM technique with various measures under two datasets.  
an RMSE, MAE, and MAPE of 0.480, 0.422, and 0.218, respectively. Furthermore, with the winter season, the AJODL-DSSEM methodology reached an RMSE, MAE, and MAPE of 0.479, 0.416, and 0.231, respectively.  
Figure 4: Result analysis of AJODL-DSSEM technique under IHEPC dataset.  
Figure 5: Result analysis of AJODL-DSSEM technique under ISO-NE dataset.  
Table 2 and Figure 6 illustrate the actual vs. predicted global active power of the AJODL-DSSEM model under distinct time steps on the IHEPC dataset. The results indicated that the AJODL-DSSEM model predicted the values much closer to the actual values. For instance, with a time step of 20 h and actual value of 4.308, the IHEPC dataset obtained a predicted value of 4.383. Furthermore, with a time step of 80 h and actual value of 1.532, the IHEPC dataset reached a predicted value of 1.387. In addition, with a time step of 160 h and actual value of 0.182, the IHEPC dataset attained a predicted value of 0.222. In addition, with a time step of 200 h and actual value of 0.478, the IHEPC dataset obtained a predicted value of 0.415.  
Table 3 and Figure 7 demonstrate the actual vs. predicted system load of the AJODL-DSSEM algorithm under distinct time steps on the ISO-NE dataset. The outcomes showed that the AJODL-DSSEM methodology predicted the values much closer to the actual values. For instance, with a time step of 20 h and actual value of 0.401, the IHEPC dataset achieved a predicted value of 0.394. Furthermore, with a time step of 40 h and actual value of 0.309, the IHEPC dataset reached a predicted value of 0.322. In addition, with a time step of 80 h and actual value of 0.125, the IHEPC dataset obtained a predicted value of 0.139. In addition, with a time step of 100 h and actual value of 0.406, the IHEPC dataset obtained a predicted value of 0.420.