User Behavior Clustering Based Method for EV Charging Forecast

The increasing adoption of electric vehicles poses new problems for the electrical distribution network. For this reason, proper electric vehicle forecasting will be of fundamental importance for a predictive energy management system, which could greatly help the operation of the grid. This paper proposes a comprehensive novel methodology to forecast single charging sessions of electric vehicle and the resulting cumulative energy forecast of the charging infrastructure. Historical charging sessions are first clustered on the basis of similar user characteristics and their respective probability density functions are defined. From this, every charging session is predicted with a triplet of parameters, namely the arrival time, the charging duration and the average power expected during the process. The proposed method has been evaluated by considering a real case study. The results showed the ability to greatly improve the accuracy with respect to the chosen benchmark, both in terms of energy required by the station and the predicted number of charging sessions. The overall performance measured by Skill Score is 0.37 for the year 2019.

Automated summary

The increasing adoption of electric vehicles presents challenges for the electrical distribution network. Accurate forecasting of electric vehicle charging sessions is crucial for predictive energy management systems that improve grid operation. This paper proposes a comprehensive methodology that clusters historical charging sessions based on user characteristics and predicts each session using arrival time, duration, and expected power parameters. The method is evaluated using a real case study, showing significant improvements in energy accuracy and predicted charging sessions compared to a benchmark. The overall Skill Score for 2019 is 0.37.