# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Ablation Study
In this study, the accuracy of the model was evaluated using different batch size values. The results are given in Table 8.  
As seen in Table 8, batch sizes of 16 and 32 provide the highest accuracy. While smaller batch sizes increase accuracy by providing more stable updates, it was observed that larger batch sizes speed up the training process but cause a slight decrease in accuracy. Therefore, the optimal batch size for the proposed model was determined as 16. * Effect of Optimization Methods: Optimization algorithms directly affect the learning process and accuracy of the model. In this study, the effects of different optimization algorithms on model performance were investigated. The results obtained are presented in Table 9.  
Table 9 shows the effects of different optimization algorithms on the model performance. While the Adam algorithm provided the best performance by providing the highest accuracy, the other algorithms exhibited lower performance. Therefore, the Adam algorithm was selected as the most suitable optimization method for the proposed model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Output** & **Parameters** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline \multirow{8}{*}{CNN} & 8, 32 & 2.7386 & 3.3804 & 12.4958 & 89.22\% \\  & 16, 32 & 2.2463 & 2.8367 & 8.2721 & 93.56\% \\  & 16, 64 & 2.4091 & 3.0395 & 10.1010 & 90.74\% \\  & 32, 64 & 2.3428 & 3.0315 & 10.5096 & 90.96\% \\  & 8, 128 & 2.4903 & 3.1471 & 10.5165 & 90.95\% \\  & 32, 128 & 2.3595 & 2.9062 & 9.8455 & 92.85\% \\
**Proposed Model (8, 16)** & **2.0550** & **2.6285** & **7.4550** & **94.67\%** \\ \hline \multirow{8}{*}{LSTM} & 10 & 2.3899 & 2.9029 & 9.1013 & 93.05\% \\  & 20 & 2.4979 & 3.2685 & 11.4975 & 90.57\% \\ \cline{1-1}  & 80 & 2.5517 & 3.0457 & 9.9074 & 92.23\% \\ \cline{1-1}  & 100 & 2.2130 & 2.8600 & 9.3327 & 91.88\% \\ \cline{1-1}  & 200 & 2.6208 & 3.2832 & 11.6316 & 89.51\% \\ \cline{1-1}  & **Proposed Model (50)** & **2.0550** & **2.6285** & **7.4550** & **94.67\%** \\ \hline \multirow{8}{*}{Dense} & 100 & 2.6882 & 3.3378 & 12.2882 & 89.73\% \\  & 200 & 2.4831 & 3.1654 & 10.9938 & 91.81\% \\ \cline{1-1}  & 500 & 2.2492 & 2.7455 & 8.0008 & 93.24\% \\ \cline{1-1}  & 200, 100 & 2.4696 & 3.1491 & 11.2245 & 91.22\% \\ \cline{1-1}  & 200, 50 & 2.3387 & 2.9090 & 9.1680 & 92.84\% \\ \cline{1-1}  & 300, 200 & 2.3268 & 2.9676 & 9.5501 & 92.07\% \\ \cline{1-1}  & 500, 100 & 2.2931 & 2.8261 & 9.0596 & 92.02\% \\ \cline{1-1}  & **Proposed Model (500, 200)** & **2.0550** & **2.6285** & **7.4550** & **94.67\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effect of different layer and parameter combinations on model performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batch Size** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline
8 & 2.3888 & 3.0683 & 10.4971 & 91.91\% \\
16 & 2.0550 & 2.6285 & 7.4550 & 94.67\% \\
32 & 2.2575 & 2.8623 & 9.0737 & 93.60\% \\
64 & 2.3812 & 2.9316 & 9.2983 & 91.95\% \\
128 & 2.6015 & 3.2963 & 11.7663 & 89.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effect of batch size on model performance. This ablation study clearly demonstrates the contribution of each component of the proposed model to the model's performance. In particular, it is seen that the number of neurons and filters in the CNN and LSTM layers, the batch size, and the optimization algorithm provide significant contributions to the overall accuracy of the model. The findings support the high performance of the EVs-PredNet model in electric vehicle demand forecasting and demonstrate the effectiveness of the hyperparameters used in model configuration.