Analyzing the factors influencing energy consumption at electric vehicle charging stations with Shapley additive explanations

Concerns about fossil fuels and the harmful effects of climate change are pushing everyone to use green and clean energy. The use of electric vehicles is becoming a necessity to achieve this goal. It simultaneously needs the infrastructure for electric vehicle charging stations to meet the tremendous demand for electric vehicles. Hence, there will be a huge consumption of energy in this regard. This work proposes a novel approach for analyzing the factors influencing energy consumption at electric vehicle charging stations using an explainable AI technique such as SHAP explanation. The study aims to identify the key factors that affect energy consumption and provide insights into how these variables can be optimized to minimize energy consumption. The research methodology uses machine learning algorithms to model the relationship between energy consumption and several other factors. The SHAP explanation technique is then applied to interpret the models and identify the key factors, such as charging time and maximum power. Using SHAP for feature selection can lead to better predictive models by iden-tifying the essential features and interactions between features. Reducing the number of features in the model and focusing on the most important ones can also improve its interpretability and generalization performance of the model. The study's findings will be useful for policymakers, electric vehicle manufacturers, and charging station operators to optimize the energy efficiency of electric vehicle charging stations.