A comprehensive incremental and ensemble learning approach for forecasting individual electric vehicle charging parameters

Electric vehicles (EVs) have the potential to reduce grid stress through smart charging strategies while simultaneously meeting user demand. This requires accurate forecasts of key charging parameters, such as energy demand and connection time. Although previous studies have made progress in this area, they have overlooked the importance of dynamic training to capture recent patterns and have excluded EV sessions with limited information, missing potential opportunities to use these data. To address these limitations, this study proposes a dual-model approach incorporating incremental learning with six machine-learning models to predict EV charging session parameters. This approach includes dynamic training updates, optimal features, and hyperparameter set selection for each model to make it more robust and inclusive. Using a data set of 170,000 measurements from the real world electric vehicle session, week-long charging parameters were predicted over a one-year period. The findings reveal a significant difference between workplace and residential charging locations regarding connection duration predictability, with workplace sessions being more predictable. The proposed stacking ensemble learning method enhanced forecasting accuracy, improving R 2 by 2.83% to 43.44% across all parameters and location settings. A comparison of the two models reveals that incorporating user IDs as a feature, along with the associated historical data, is the most significant factor influencing the accuracy of the forecast. Forecasts can be used effectively in smart charging and grid management applications by incorporating uncertainty quantification techniques, allowing charge point operators to optimize charging schedules and energy management. • Dual-model approach to forecasting key aspects for EV charging profile optimization. • Effectiveness tested on 170,000 real-world EV transactions for week-ahead prediction. • Optimal feature selection highlights historical and temporal data as key features. • With the stacking ensemble model, R 2 accuracy improved from 2.83% to 43.44%. • Compared to residential charging stations, workplaces appear more predictable.