# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 6 Conclusions  
This study proposes EVs-PredNet, a learning model based on LSTM and CNNs, to predict the demand for electric vehicles. This model is used to predict electric vehicle demand for four different outcomes (BEV, HEV, PHEV, and total electric vehicles). The model is trained using real data and tested using 10-fold cross-validation. The results show that the EVs-PredNet model provides significant superiority over traditional methods. This superiority is manifested in lower values of error metrics such as MAE, RMSE, and MSE and a higher R-Squared coefficient. These low error rates and high R-Squared values demonstrate the ability of the EVs-PredNet model to forecast electric vehicle demand more accurately and in more detail. In particular, the success of the model in different EV segments such as BEV, HEV, and PHEV shows that the model can capture all aspects of market dynamics. These results show that the model can predict demand trends in the electric vehicle market with greater accuracy and precision. Practical applications of the proposed model offer significant opportunities for both policymakers and industry stakeholders. First, car manufacturers can use these forecasts to optimize their production strategies according to demand. In particular, they can determine which models to prioritize in the production line by taking into account the differences in demand between different electric vehicle categories, such as BEV, HEV, and PHEV. In this way, manufacturers can make more accurate decisions about which models should be prioritized in the production line to reduce production costs and optimize inventory management. This approach allows companies to use their resources more efficiently and offers a strategic advantage in meeting consumer demand. Furthermore, these forecasts can effectively guide marketing strategies by identifying the segments with the highest growth potential, thereby optimizing the use of marketing budgets. The proposed model can also be used for infrastructure planning. As the use of electric vehicles becomes more widespread, it is important to develop charging infrastructure in a timely manner and in the right locations. These forecasts allow charging stations to be positioned in the most efficient areas and make infrastructure investments more efficient. Especially for city centers and high-demand regions, EV demand forecasts enable more conscious use of resources and facilitate charging access for EV users. EVs-PredNet can provide information on where charging networks should be concentrated by predicting the demand for electric vehicles in different geographical regions and time periods. This can support a more efficient use of energy resources and increase demand for renewable energy resources. The model can also make an important contribution to energy management. Considering the increasing energy consumption of electric vehicles, the demand forecasts provided by the model can be a critical data source for the management of energy supply and demand. In this way, an important contribution is made to the integration of renewable energy sources and ensuring sustainable energy use. The energy consumption of electric vehicles can have a significant impact on overall energy demand. This model can be used to better manage and optimize energy supply and demand as the use of electric vehicles increases. Such demand forecasts are crucial for planning the integration of renewable energy resources and increasing the sustainable use of energy resources. As a result, this study makes a significant contribution to the more accurate forecasting of electric vehicle demand. The EVs-PredNet model can be an effective tool for companies making strategic decisions in the electric vehicle market and for public institutions involved in infrastructure planning. Overall, the high accuracy provided by the EVs-PredNet model provides a reliable tool for strategic decision-making processes for both private companies and public institutions in the sector.