# Solving Optimal Electric Vehicle Charger Deployment Problem
## 6 Case Study 2: Region with High EV Density
### Results and Discussions  
Table 4 summarizes the configuration conditions and consistency results for each condition. The implications of these results are as follows:  
1. Installation criteria differ between DC fast chargers and level-2 chargers. Significant differences in consistency are observed when training separately based on each charging station type or when training with both types together. Consequently, it can be concluded that chargers have been installed at locations that meet their respective criteria for both DC fast chargers and level-2 chargers. Figure 8: Size comparison of buffers. \begin{table}
\begin{tabular}{c c c c} \hline
**Model Name** & **Decision Tree** & **Support Vector Machine** & **Random Forest** \\ \hline Consistency & 0.6583 & 0.4295 & 0.7580 \\ \hline Precision & 0.6712 & 0.1845 & 0.7580 \\ \hline Recall & 0.6583 & 0.4295 & 0.7580 \\ \hline F1-Score & 0.6593 & 0.2581 & 0.7509 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation matrix. 2. Non-uniform distribution of reference data does not significantly affect training results. There is no significant difference in consistency between training based on non-uniformly distributed chargers and training based on grid points uniformly distributed at regular intervals. Thus, it can be concluded that the non-uniform distribution of data does not impact the training results. 3. Buffer size influences data consistency. Decreasing the buffer size results in increased consistency. The reason for the decrease in consistency at buffer sizes below 125 m is that the polygon data used for learning is 250 m \(\times\) 250 m grid data, resulting in buffers that do not contain data from the 125 m radius buffer size. This problem can be solved by using smaller grid data than 250 m \(\times\) 250 m grid data during data preprocessing. In conclusion, this result shows that larger buffer sizes increase data redundancy and affect consistency. Based on the evaluation results, the Random Forest model demonstrates the highest consistency and is selected as the final machine learning model for the site selection task. Several conditions for site selection are analyzed, including the charging station type, uniform distribution of reference data, and buffer size. The results indicate that EV charging stations should be installed based on their respective criteria for both fast chargers and slow chargers. Furthermore, the non-uniform distribution of reference data does not significantly affect the training results, providing flexibility in data selection. Adjusting the buffer size influences data consistency, with smaller buffer sizes leading to increased consistency. By adjusting various conditions, we propose a method to achieve higher consistency in site selection through machine learning. The final location selection is conducted based on the facts found from the setting conditions and results of the learning. First, since this study considers only public DC fast chargers, learning is conducted using only public DC fast chargers' data. In addition, the distribution of reference points for generating buffers was set as random points to give diversity in learning, considering that the plane data used for learning is uniformly distributed as 250 m \(\times\) 250 m grid data. Moreover, despite the highest consistency obtained when the size of the buffer is 125 m, there are many areas that the buffer cannot cover. Therefore, a buffer of 500 m size, which has the highest match among the buffer sizes that include the whole of Jeju Island, is selected. Figure 9 shows the area containing 125 m and 500 m buffer.