# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Results
The graphs in Figure 5 visually compare the model predictions with the actual data for four categories: BEV (battery electric vehicles), HEV (hybrid electric vehicles), PHEV (plug-in hybrid electric vehicles), and the combined category ALL. These visualizations effectively demonstrate the consistency and reliability of the model's predictions across different electric vehicle categories. In each subplot, the blue points represent the original data, while the red line indicates the model's predictions. The close match between these elements demonstrates the model's ability to capture both broad trends and more subtle variations, confirming its accuracy in forecasting the data. Looking more closely, the model shows a remarkable ability to deal with sudden changes in the data, including sharp peaks and rapid declines. For example, in the BEV and ALL categories, the model adapts well to the steep upward trends observed in the latter data points, closely following the actual values. Similarly, in the HEV and PHEV categories, despite the presence of more noise and variability in the data, the model continues to track these variations effectively. This consistency across all categories demonstrates the robustness of the proposed model. The sharp increases and decreases in certain sections are particularly well represented, demonstrating the model's ability to capture not only general trends but also to respond to volatile changes within the dataset. The close proximity between predicted and actual values in these categories suggests that the proposed model generalizes well to different types of electric vehicle data, ensuring reliable prediction performance. A graphical representation of the prediction values obtained by the 10-fold cross-validation method for each output parameter is shown in Figure 5.  
Figure 4: Scores of each fold based on the proposed EVs-PredNet model. In conclusion, Figure 5 effectively demonstrates the predictive power of the proposed model across several categories of electric vehicles. By consistently matching the actual data points, especially during periods of sudden change, the model demonstrates its ability to produce reliable forecasts. These visualizations confirm that the model is not only capable of accurately predicting the overall trend but is also finely tuned to the dynamic nature of the underlying data across different vehicle types. The performance results of the EVs-PredNet model proposed in this study show that it is a reliable method for predicting the demand for electric vehicles. The high R-Squared values and low error rates obtained indicate that the model successfully captures the demand dynamics in different types of electric vehicles. This success of the model is of strategic importance, especially for the increasingly available battery electric vehicle (BEV) and hybrid electric vehicle (HEV) segments on the market. These accuracy rates offer the potential for EV manufacturers and distributors to make more effective production plans in line with demand. On the other hand, the low MAE value obtained in the plug-in hybrid electric vehicle (PHEV) category shows that the model provides high precision, especially in this segment. This finding provides critical data for charging infrastructure planners, along with the widespread use of PHEVs in urban and suburban areas. Accurate demand forecasts, especially for this category, can contribute to the optimization of infrastructural strategies such as energy management and charging station placement. The high R-Squared value (94.67%) achieved by the model in the ALL category, which includes all EV types, shows the accuracy of comprehensive demand forecasts across the sector. The increase in the accuracy of demand forecasts in the EV market provides a reliable basis for policymakers and investors in the sector in order to make more conscious government policies and infrastructure investments.