# Solving Optimal Electric Vehicle Charger Deployment Problem
## 6 Case Study 2: Region with High EV Density
### Case-Specific Refinement of Solving Method
#### 6.1.3 Training Methods  
As a means to train the machine learning models, we follow these steps:  
1. _Data Preparation:_ The collected and merged dataset undergoes preprocessing to ensure its suitability for training, including handling missing values, data normalization, and feature engineering. 2. _Model Selection:_ The decision tree (DT) [67], support vector machine (SVM) [68], and random forest (RF) [69] models are considered as potential candidates for training due to their widespread usage in site selection problems. 3. _Training Process:_ Each selected model is trained using the prepared dataset, which is divided into training and validation sets. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the model's performance during training. 4. _Model Evaluation:_ After training, the models are evaluated using the validation set to assess their predictive capabilities. The evaluation metrics are used to compare the models' performance and identify the model with the highest accuracy or other desired performance metrics. Figure 6: Visualization of the buffer in QGIS (See Figure 5 for the legend of each data dot). Model Selection: Based on the evaluation results, the model demonstrating the best performance is selected as the final machine learning model for the site selection task. Following these steps ensures effective training of the machine learning models, enabling them to provide reliable predictions for optimal charging station locations. To determine the model used for training, we compare the performance of commonly used models in site selection problems, namely, DT, SVM, and RF. Each model is trained with the data, and their performance is compared. (We justify the adoption of the three techniques as follows. This case study particularly targets to solve an optimal location problem in a region with a very high EV density. We found it adequate to rely on the methods that were proven to be computationally efficient in processing such an extremely large number of data points.) For comparing the predicted optimal charging station locations using our proposed technique, we introduce the variable \"consistency.\" Consistency measures the agreement between the predicted locations obtained through our approach and the current locations of installed charging stations in Jeju Island. It is normalized, ranging from 0 to 1. A consistency value of 1 indicates a perfect match, while 0 signifies no agreement between the predicted and actual locations. For instance, a consistency value of 0.8 means that our technique's predicted charging station locations match 80% of the current charging station distribution in Jeju Island, while the remaining 20% differ from our optimal predictions. Table 3 presents the evaluation matrix for each model after training with the merged data. Based on these results, we select the Random Forest model, which exhibits the highest performance, to proceed with the site selection process. Using the selected model, we follow the steps outlined in Figure 7's flowchart to identify the conditions for site selection that yield high consistency. Figure 7: Algorithm of site selection. The conditions for each branching point are as follows:  
(a) Charging Station Type: We examine the influence of the charging station type, which serves as the basis for training, on consistency. A comparison is made between training using only level-2 chargers or DC fast chargers and training with a mix of both. (b) Uniform Distribution of Reference Data: The reference data initially set consists of charging station data which is not uniformly distributed across Jeju Island. To assess the impact of non-uniform data distribution on consistency, we compare the results of training based on 250 m grid points that are uniformly distributed as reference data with the results obtained from the original training.