## 4 Experimental Results
### Quantitative Study on IHEPC Dataset  
Table 5 represents the extensive experiment conducted on the IHEPC dataset to evaluate the efficiency of the proposed metaheuristic based on the LSTM network by means of MAPE, MAE, RMSE, and MSE. The proposed metaheuristic based on the LSTM network obtained a minimum MAPE of 0.05, an MAE of 0.04, an RMSE of 0.16, and an MSE of 0.04, which are effective compared to other experimental models, such as linear regression, CNN, SVR, the LSTM network, and the Bi-LSTM network on the IHEPC database. In addition, the prediction time of metaheuristic based on LSTM network is 25% minimum compared to other experimental models. In this research, the metaheuristic based on the LSTM network superiorly handles the complex time series patterns and moderates the error value at every interval related to the other experimental models. Graphical presentation of the experimental models for the IHEPC dataset is depicted in Figure 9.  
The LSTM network with BOA achieved better results in energy forecasting compared to other optimizers in light of MAPE, MAE, RMSE, and MSE. As seen in Table 6, the BOA reduced the error value in energy forecasting by almost 20-50%, and the prediction time by 25% compared to other hyperparameter optimizers in the LSTM network for the IHEPC dataset. The experimental result shows that the metaheuristic-based BOA model obtained a successful solution, and it effectively reduces computational complexity in determining the optimal parameters in the context of EECP. Graphical presentation of dissimilar hyperparameter optimizers in the LSTM network on the IHEPC dataset is depicted in Figure 10. Additionally, the fitness comparison of different optimizers by varying the iteration number is graphically presented in Figure 11.  
Figure 8: Graphical presentation of the dissimilar hyperparameter optimizers in the LSTM network on the AEP dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **MAPE** & **MAE** & **RMSE** & **MSE** & **Predicting Time (s)** \\ \hline Linear regression & 0.82 & 0.62 & 0.90 & 0.23 & 34 \\ CNN & 0.13 & 0.14 & 0.29 & 0.17 & 22 \\ SVR & 0.47 & 0.26 & 0.82 & 0.38 & 29 \\ LSTM network & 0.11 & 0.12 & 0.31 & 0.19 & 19 \\ Bi-LSTM network & 0.09 & 0.12 & 0.19 & 0.11 & 18.20 \\ Metaheuristic based on the & & 0.05 & 0.04 & 0.16 & 0.04 & 13 \\ LSTM network & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the experimental models on IHEPC dataset.  
Figure 10: Graphical presentation of the dissimilar hyperparameter optimizers in the LSTM network on the IHEPC dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{LSTM Network} \\ \hline
**Optimizers** & **MAPE** & **MAE** & **RMSE** & **MSE** & **Predicting Time (s)** \\ \hline GWO & 0.12 & 0.23 & 0.29 & 0.11 & 18 \\ PSO & 0.18 & 0.12 & 0.23 & 0.17 & 18.20 \\ GA & 0.09 & 0.07 & 0.20 & 0.07 & 17 \\ ACO & 0.12 & 0.21 & 0.20 & 0.11 & 16 \\ ABC & 0.14 & 0.17 & 0.18 & 0.12 & 14 \\ BOA & 0.05 & 0.04 & 0.16 & 0.04 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the dissimilar hyperparameter optimizers in the LSTM network on the IHEPC dataset.  
Figure 9: Graphical presentation of the experimental models for the IHEPC dataset.  
The prediction performance of the metaheuristic-based BOA model for the AEP and IHEPC datasets are graphically presented in Figures 12 and 13. Through an examination of these graphs, the proposed metaheuristic-based BOA model was shown to generate effective prediction results in the EECP domain.  
Figure 11: Fitness comparison of different optimizers achieved by varying the iteration number on the IHEPC dataset.  
Figure 12: Prediction performance of the proposed model for the AEP dataset.  
Figure 13: Prediction performance of the proposed model for the IHEPC dataset.