# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 5 Discussion  
### Comparison of Proposed Model with Existing Methods  
This study introduces a deep learning method for forecasting the total number of electric automobiles. In recent years, deep learning approaches have become more prevalent in prediction issues, alongside the traditional machine learning regression methods typically seen in the literature. This study investigates the effectiveness and efficiency of deep learning techniques for forecasting the number of electric vehicles. Accordingly, the performance of a deep learning-based approach is analyzed in comparison to traditional methods for forecasting the demand for electric vehicles. Machine learning-based regression methods such as decision tree (DT), linear regression (LR), Lasso Regression (Lasso-LR), Elastic Network Regression (ElasticNet-LR), Ridge Regression (Ridge), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost Regression (ABR), which are widely used in the literature, are selected for their predictive capabilities. The performance of each method is calculated and presented in comparison with the proposed model and detailed in Table 10. In the experimental evaluations, each machine learning regression method was implemented using Python's sklearn library (scikit-learn). Default parameters were used for the regression methods in this library. For more detailed information about the default parameters, please refer to the official documentation of scikit-learn [57]. Table 10 displays multiple metrics (MAE, RMSE, MSE, R-squared) assessing the effectiveness of different forecasting techniques (DT, LR, Lasso-LR, ElasticNet-LR, Ridge-LR, KNN, SVM, ABR) in predicting electric car demand. MAE is the mean absolute difference between the true and predicted values. A lower MAE indicates that the model's predictions are more accurate. RMSE is the root mean square of the difference between the true and predicted values. It emphasizes large errors more, so a lower RMSE indicates better model performance. MSE is the mean square of the prediction errors. Similarly, to RMSE, it penalizes large errors more severely. R-Squared is the ability of the independent variables to explain the change in the dependent variable. An R-Squared close to 1 indicates that the model explains the data well. Linear regression models are the most successful among machine learning methods for predicting HEV, BEV, and ALL output characteristics. The models can provide more accurate projections regarding the demand for these specific electric vehicles. The AdaBoost Regression (ABR) method outperforms other machine learning techniques in achieving higher success for the PHEV output parameter. The SVM approach exhibited the poorest performance among the other methods, particularly struggling to handle the intricacy of the data. The results emphasize that various machine learning algorithms can yield varying levels of success depending on the type of electric vehicles, underscoring the need for selecting the appropriate method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batch Size** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline Adam & 2.0550 & 2.6285 & 7.4550 & 94.67\% \\ Nadam & 2.8389 & 3.5141 & 14.0588 & 89.38\% \\ Adamax & 2.4161 & 2.9739 & 9.4828 & 92.86\% \\ RMSProp & 2.4340 & 3.0871 & 10.0275 & 91.49\% \\ SGD & 2.7561 & 3.4389 & 12.9862 & 88.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Effect of optimization methods on model performance. The proposed EVs PredNet model, based on LSTM and CNNs, outperforms other methods in terms of error rates and R-Squared values for each output parameter (BEV, HEV, PHEV, ALL). The proposed model for PHEV has notable success with lower error metrics and a higher R-Squared value compared to other techniques. These data show