# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Implement Details  
In this study, a 10-fold cross-validation approach was used in our experimental studies to ensure the reliability and robustness of the performance results. This technique allowed us to evaluate the performance and generalization ability of our models by dividing our dataset into ten subsets and systematically training and testing on different combinations. This rigorous validation methodology increased the reliability of our findings and provided valuable insight into the robustness of our proposed methods. Other validation methods, such as leave-one-out, can significantly increase computational costs, especially in large datasets, while methods such as stratified sampling are preferred in imbalanced datasets. Since there is no significant imbalance between dependent and independent variables in the dataset used in our study, stratified sampling was not necessary. The 10-fold cross-validation ensured that the model performed consistently on different data subsets. The hyperparameters used in deep learning models have a direct impact on the architecture, training process, and overall performance of the model. These parameters include values such as the number of LSTM and CNN layers, the number of neurons in each layer, the learning rate, batch size, and regulation terms. The correct setting of these hyperparameters is critical for the model to train consistently and produce reliable results. The hyperparameters used in the training phase of the proposed model were determined using the GridSearch method. This method was used to determine the settings that give the best performance by trying different combinations of hyperparameters. In order for the model to train consistently and produce reliable results, it is crucial to determine these hyperparameters correctly. The ranges of hyperparameters were determined by taking into account similar studies in the literature and common ranges of values used in the construction of deep learning models. In particular, hyperparameters such as number of cells, number of filters, and learning rates proposed in LSTM and CNN architectures were determined as initial ranges based on previous studies. These ranges were optimized using the GridSearch method. The parameter ranges used for the LSTM and CNN layers were chosen in a wide range to enable the model to better learn complex data relationships. In line with the research in the literature that informs this study, several experiments were conducted to find the most appropriate combinations of hyperparameters. This process should ensure that the model achieves the best performance. Table 4 lists the optimal values and search ranges of the hyperparameters of the proposed regression model. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameters** & **Search Ranges** & **Optimum Values** \\ \hline Number of LSTM units & 10–1000 & 50 \\ \hline Number of neurons (dense-1) & 10–1000 & 500 \\ \hline Number of neurons (dense-2) & 10–1000 & 200 \\ \hline Number of filters (conv1d\_1) & 8–128 & 8 \\ \hline Number of filters (conv1d\_2) & 8–128 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The hyperparameters and search ranges for the proposed regression model.