# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Results  
This study proposes an LSTM and CNN-based learning model to forecast the total number of electric cars on the road. Convolution layers, various activation functions, maximum pooling layers, LSTM layers, and dense layers are all included in our suggested model. The suggested model consists of four types of electric vehicles: PHEV, HEV, BEV, and all electric vehicles (ALL). Experimental studies were carried out based on four different outputs. Table 5 provides a summary of the performance outcomes of the experimental studies carried out. Table 5 presents a detailed evaluation of the efficacy of the proposed EVs-PredNet model in forecasting the demand for electric vehicles and their individual performance for each type of vehicle. As per the findings, the model that was suggested had an output of 3.6705 MAE, 4.7297 RMSE, 24.4612 MSE, and 92.09% R2. In contrast, the model that was suggested had an output of 4.1358 MAE, 5.1894 RMSE, 27.9417 MSE, 86.69% R2, and 1.3801 MAE when it had the BEV dependent parameter. There were also results of 1.7512 RMSE, 3.3972 MSE, and 78.07% R2. Furthermore, the study's maximum score--which was determined by averaging three dependent parameter variables--was achieved with performance metrics of 2.0550 MAE, 2.6285 RMSE, 7.4550 MSE, and 94.67% R2. According to the output parameter, the PHEV category has the lowest average error rates, while the ALL category, which combines all factors, has the highest coefficient of determination value. This suggests that the model functions better in forecasting the total number (ALL) of electric vehicle types. The 10-fold cross-validation approach was utilized to obtain the findings shown in Table 5. This approach divides the dataset into ten equal-sized parts. Nine of the pieces are used for training, and the remaining one is used for testing to assess the model's performance. Ten times through this process, a separate piece is set aside for testing. Figure 4 displays the scores for each parameter based on this 10-fold cross-validation procedure. Based on the 10-fold cross-validation method, Figure 4 shows the predictions generated for each fold of the BEV, HEV, PHEV, and ALL output parameters in experimental tests. The performance metrics--R-Squared (blue bars), mean absolute error (MAE) (orange bars), and root mean squared error (RMSE) (green bars)--are presented for each fold, providing a comprehensive overview of the model's consistency and effectiveness across multiple validation cycles. As can be seen in Figure 4, the results of these metrics as R-Squared, MAE, and RMSE indicate that the model performs effectively across all folds and categories. While there are small variations in performance between folds, these are not significant enough to indicate problems with model stability. The ability of the model to maintain performance across different folds suggests that it is not overfitting to any particular subset of the data and that it generalizes well across different types of electric vehicles. Finally, Figure 4 provides a detailed representation of the robust performance of the model across all output parameters. The 10-fold cross-validation results show that the proposed model provides stable and reliable predictions, effectively minimizing errors and consistently explaining the variance in the data. These results confirm that the model \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline BEV & 3.6705 & 4.7297 & 24.4612 & 92.09\% \\ HEV & 4.1358 & 5.1894 & 27.9417 & 86.69\% \\ PHEV & 1.3801 & 1.7512 & 3.3972 & 78.07\% \\ ALL & 2.0550 & 2.6285 & 7.4550 & 94.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance results of the proposed EVs-PredNet model. generalizes well across different types of electric vehicle datasets, making it suitable for prediction tasks in diverse settings.