# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Ablation Study  
This section details the results of the ablation study performed to evaluate the performance of the proposed EVs-PredNet model. In this analysis, the effects on performance by removing or changing certain layers and hyperparameters of the model were investigated. The obtained results explain the effects of each change on the accuracy of the model. Additionally, for all ablation study analyses, the combined category ALL was considered as the output parameter. The results are as follows:  
* The effect of the components used in the proposed model on performance is a critical factor that directly determines the learning capacity and accuracy of deep learning models. In this study, the performance changes were examined when the model was run with only CNN and dense layers or only LSTM and dense layers. The results of the experiments conducted to observe the effect of removing one of the CNN or LSTM layers on the model accuracy are presented in Table 6.  
As seen in Table 6, when the model is run with only CNN or only LSTM layers, there is a significant decrease in model accuracy. On the other hand, using CNN and LSTM layers together in the proposed model significantly increased the model's accuracy. This result reveals that the synergistic operation of CNN and LSTM layers contributes to the model performance and increases the accuracy. In summary, the combined use of both layers optimizes the prediction performance by allowing the model to reach a wider learning capacity. * Effect of Number of Neurons and Filters in Layers: One of the most important factors determining the depth and feature extraction capacity of deep learning models is the number of neurons and filters in LSTM, CNN, and dense layers. In this study, the effects on model performance were systematically examined by changing the number of neurons in the LSTM layer, the number of filters in CNN layers and the number of neurons in the dense layer. This analysis reveals the effect of different numbers of neurons and filters on performance metrics such as model accuracy and error rate. The results are presented in Table 7.  
As seen in Table 7, the parameter combination in the proposed model (8.16 filters for CNN, 50 neurons for LSTM, 500 and 200 neurons for dense) provides the lowest error rates and the best performance by providing the highest R-Squared value. In the CNN layer, both the very low (8.32) and very high (32.128) number of filters decreased the model performance. The lower number of filters (8.16) in the proposed model increased the accuracy by providing better generalization of the model. In the LSTM layer, the proposed 50 neurons both decrease the error rate of the model and provide the best accuracy with an R-Squared value of 94.67%. Lower (10 or 20) or higher (100 or 200) neuron numbers caused a decrease in performance. In the dense layer, the combination of 500 and 200 neurons provided higher performance than the other combinations since it provided more feature extraction capacity. Combinations with a single layer or fewer neurons generally resulted in higher error rates and lower R-Squared values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline Only LSTM+Dense & 2.6179 & 3.3146 & 11.6984 & 89.84\% \\ Only CNN+Dense & 2.3834 & 3.0697 & 10.3354 & 92.31\% \\ Proposed Model (CNN+LSTM+Dense) & 2.0550 & 2.6285 & 7.4550 & 94.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of proposed model and alternative structures. As a result, choosing the right number of neurons and filters in each layer significantly increased the performance of the model. The proposed parameter combinations provided the model with a higher accuracy and generalization ability. This analysis emphasizes the importance of optimum parameters in structuring the layers. * Effect of Batch Size: Batch size plays an important role in the training time and accuracy of the model.