A Comprehensive Incremental and Ensemble Learning Approach for Forecasting Individual Electric Vehicle Charging Parameters

TL;DR

This paper introduces a dual-model incremental and ensemble learning approach for predicting EV charging parameters, improving forecast accuracy by dynamically updating models and selecting optimal features, with implications for smart grid management.

Contribution

It presents a novel incremental and ensemble learning framework that incorporates dynamic training and feature optimization for EV charging parameter prediction.

Findings

Workplace charging sessions are more predictable than residential ones.

Stacking ensemble improves forecasting accuracy by up to 43.44%.

Including user IDs significantly enhances prediction accuracy.

Abstract

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 R2 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.