## 4 Experimental Results
### Quantitative Study on AEP Dataset  
In this scenario, an extensive experiment is conducted on the AEP dataset to evaluate and validate the proposed metaheuristic based on the LSTM network's effectiveness and robustness for real-world issues. The refined AEP dataset is split into a 20:80% ratio for the proposed model's testing and training. The proposed metaheuristic based on the LSTM network utilizes 20% of data during testing and 80% of data during training. As seen in Table 3, the proposed metaheuristic based on the LSTM network obtained results closely related to the native properties of energy and the actual consumed energy level. By inspecting Table 3, the proposed model achieved effective results compared to other existing models, such as linear regression, CNN, SVR, the LSTM network and the Bi-LSTM network in light of MAPE, MAE, RMSE and MSE. Hence, the irregular tendencies of energy consumption are easily and effectively handled by the proposed metaheuristic based on the LSTM network. Hence, the proposed model attained a minimum MAPE of 0.09, an MAE of 0.07, an RMSE of 0.13, and an MSE of 0.05. In addition to this, the proposed model reduces prediction time by almost 30% compared to other models for the AEP dataset. A graphical presentation of the experimental models for the AEP dataset is depicted in Figure 7.  
In Table 4, the hyperparameter selection in the LSTM network is carried out with dissimilar optimization techniques, such as BOA, Grey Wolf Optimizer (GWO), Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Ant Colony Optimizer (ACO), and Artificial Bee Colony (ABC), and the performance validation is done using four metrics, namely, MAPE, MAE, RMSE, and MSE on the AEP dataset. As evident from Table 4, the combination of the LSTM network with BOA obtained an MAPE of 0.09, an MAE of 0.07, an RMSE of 0.13, and an MSE of 0.05, which are minimal compared to other optimization techniques. Due to naive selection of the hyperparametric values and the noisy electric data, the LSTM network obtained unacceptable forecasting results. An optimal LSTM network configuration is therefore needed to discover the time series dynamics in the energy domain and to describe the electric consumption pattern precisely. In this article, a metaheuristic-based BOA is applied to identify the optimal hyperparametric values of the LSTM network in the EEC domain. The BOA effectively learns the hyper parameters of the LSTM network to forecast energy consumption. Graphical presentation of dissimilar hyperparameter optimizers in the LSTM network on the AEP dataset is depicted in Figure 8.  
\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 & 0.23 & 0.18 & 0.56 & 0.15 & 30 \\ PSO & 0.19 & 0.09 & 0.31 & 0.11 & 43 \\ GA & 0.26 & 0.13 & 0.82 & 0.19 & 64 \\ ACO & 0.22 & 0.17 & 0.44 & 0.13 & 29 \\ ABC & 0.12 & 0.11 & 0.37 & 0.08 & 25 \\ BOA & 0.09 & 0.07 & 0.13 & 0.05 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance 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 & 1.54 & 1.99 & 2.12 & 1.24 & 38 \\ CNN & 0.34 & 0.40 & 1.02 & 0.49 & 29 \\ SVR & 0.72 & 0.88 & 1.92 & 0.82 & 49 \\ LSTM network & 0.28 & 0.17 & 0.92 & 0.29 & 21 \\ Bi-LSTM network & 0.24 & 0.13 & 0.78 & 0.14 & 18 \\ Metaheuristic based on the LSTM network & 0.09 & 0.07 & 0.13 & 0.05 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the experimental models on the AEP dataset.  
Figure 7: Graphical presentation of the experimental models for the AEP dataset.