Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods

Abstract

Scheduling electric vehicle charging sessions allows to aggregate flexibility in order to minimize energy costs and reduce congestion in the electricity grid. Existing research shows that user input for energy and parking duration does not serve as a reliable prediction. The study presents an evaluation of the forecast error and computational performance of the different models. Two main methods are investigated: tree-based (gradient boosted trees LightGBM) and cluster-based (Gaussian mixture model). We also present a novel dynamic cluster-based method, the Similar Sessions method, which employs the similarity between charging sessions based on numerical variables. The results highlight the importance of selecting the forecast model influenced by the availability of training data. The effect of user registration on the accuracy of the forecast is investigated. The tests are run using ACN-Data dataset of Electric Vehicle charging sessions in California, United States. While underperforming on a small dataset with a short look-back period, tree-based methods show superiority while the charging data are accumulating. The Similar Sessions method shows superior accuracy under various data availability conditions. The proposed method requires no prior training, but has slower computational performance in deployment.