## 5 Discussion  
A comparative study of the AJODL-DSSEM model with recent models: the GRU [25], Bi-GRU [26], LSTM [27], Bi-LSTM [28], CNN-LSTM [29], CNN-GRU [30], and energy-net [31] models on the IHEPC dataset, is portrayed in Table 4. Figure 8 compares the MSE, RMSE, and MAE inspections of the AJODL-DSSEM model on the IHEPC dataset. The figure implied that the IHEPC dataset showed effectual outcomes with minimal values of MSEs, RMSEs, and MAEs. With respect to MSE, the AJODL-DSSEM algorithm obtained a reduced MSE of 0.092, whereas the GRU, Bi-GRU, LSTM, Bi-LSTM, CNN-LSTM, CNN-GRU, and energy-net models obtained increased MSEs of 0.270, 0.251, 0.413, 0.422, 0.431, 0.243, and 0.125, respectively. Moreover, in terms of the RMSE, the AJODL-DSSEM methodology obtained a lower RMSE of 0.303, whereas the GRU, Bi-GRU, LSTM, Bi-LSTM, CNN-LSTM, CNN-GRU, and energy-net methodologies obtained improved RMSEs of 0.518, 0.501, 0.643, 0.647, 0.662, 0.493, and 0.354, respectively. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**IHEPC Dataset** & & & & \\ \hline
**Models** & **MSE** & **RMSE** & **MAE** & **MAPE (\%)** \\ \hline GRU [24] & 0.270 & 0.518 & 0.389 & 65.200 \\ \hline Bi-GRU [25] & 0.251 & 0.501 & 0.372 & 63.900 \\ \hline LSTM [26] & 0.413 & 0.643 & 0.409 & 67.800 \\ \hline Bi-LSTM [27] & 0.422 & 0.647 & 0.392 & 65.300 \\ \hline CNN-LSTM [28] & 0.431 & 0.662 & 0.403 & 50.900 \\ \hline CNN-GRU [29] & 0.243 & 0.493 & 0.348 & 46.400 \\ \hline Energy-Net [30] & 0.125 & 0.354 & 0.287 & 39.200 \\ \hline AJODL-DSSEM & 0.092 & 0.303 & 0.262 & 32.900 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of AJODL-DSSEM technique with existing approaches under IHEPC dataset. Figure 8: Comparative analysis of AJODL-DSSEM algorithm under IHEPC dataset. Figure 9 demonstrates the MAPE analysis of the AJODL-DSSEM method on the IHEPC dataset. The figure exposed that the IHEPC dataset showed effectual outcomes with minimal values of MAPE. In terms of the MAPE, the AJODL-DSSEM technique obtained a lower MAPE of 32.9%, whereas the GRU, Bi-GRU, LSTM, Bi-LSTM, CNN-LSTM, CNN-GRU, and energy-net systems reached improved MAPEs of 65.2%, 63.9%, 67.8%, 65.3%, 50.9%, 46.4%, and 39.2%, respectively. A comparative study of the AJODL-DSSEM algorithm with recent approaches to the ISO-NE dataset is depicted in Table 5. Figure 10 illustrates the MSE, RMSE, and MAE examinations of the AJODL-DSSEM approach on the ISO-NE dataset. The figure exposed that the IHEPC dataset obtained effectual outcomes with lesser values of MSEs, RMSEs, and MAEs. In terms of the MSE, the AJODL-DSSEM algorithm obtained a decreased MSE of 0.208, whereas the GRU, Bi-GRU, LSTM, Bi-LSTM, CNN-LSTM, CNN-GRU, and energy-net approaches obtained increased MSEs of 0.619, 0.501, 0.792, 0.557, 0.456, 0.379, and 0.286, respectively. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ISO-NE Dataset** & & & & \\ \hline
**Models** & **MSE** & **RMSE** & **MAE** & **MAPE (\%)** \\ \hline GRU [24] & 0.619 & 0.794 & 0.513 & 49.200 \\ \hline Bi-GRU [25] & 0.501 & 0.713 & 0.461 & 60.900 \\ \hline LSTM [26] & 0.792 & 0.891 & 0.552 & 65.400 \\ \hline Bi-LSTM [27] & 0.557 & 0.746 & 0.534 & 62.300 \\ \hline CNN-LSTM [28] & 0.456 & 0.681 & 0.434 & 40.900 \\ \hline CNN-GRU [29] & 0.379 & 0.617 & 0.488 & 34.100 \\ \hline Energy-Net [30] & 0.286 & 0.535 & 0.414 & 29.300 \\ \hline AJODL-DSSEM & 0.208 & 0.456 & 0.384 & 23.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of AJODL-DSSEM algorithm with recent methodologies under ISO-NE dataset. Figure 9: MAPE analysis of AJODL-DSSEM algorithm under IHEPC dataset. With respect to the RMSE, the AJODL-DSSEM system obtained an RMSE of 0.384, whereas the GRU, Bi-GRU, LSTM, Bi-LSTM, CNN-LSTM, CNN-GRU, and energy-net techniques obtained improved RMSEs of 0.794, 0.713, 0.891, 0.746, 0.681, 0.617, and 0.535, respectively. Figure 11 illustrates the MAPE inspection of the AJODL-DSSEM approach on the ISO-NE dataset.