Data-Driven Analysis of EV Energy Prediction and Planning of EV Charging Infrastructure

To reduce the current crisis on energy and environmental problems, the adoption of electric vehicles is an effective way. But limited access and availability of charging infrastructure are restricting the EV market development. This paper presents data-driven analysis of current EV charging infrastructure to plan future EV infrastructure in the city. This paper tackles the problem using three approaches. The first approach that is covered in this paper is to predict the number of EV charging stations that need to be implemented in a geographic location. This is done using the SARIMAX model on the California EV charging station dataset that is preprocessed to be split into zip codes. The second approach that is tackled by this paper is the energy consumption prediction by an EV charging station. This information is useful to allow planners to understand how much energy is consumed by an EV station across multiple different time periods. An optimized Meta-Fusion regression model based on a stacked model is proposed for this approach and is applied to the Palo Alto charging station dataset. The third approach utilized the K-means clustering algorithm on the Palo Alto charging station dataset to predict the optimal location for EV charging stations in that city. The SARIMAX mode had an R2 score of 0.97, a MAE of 6.50, and RMSE of 17.43 when predicting EV station counts in the zip code 92101. The stacked model had an R2 score of 0.98, a MAE of 0.91 and an RMSE of 2.26 when predicting the daily energy consumption by an EV station.