Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach

Herbert Amezquita, Cindy P. Guzman () and Hugo Morais ()
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Herbert Amezquita: Interactive Technologies Institute, LARSyS, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Cindy P. Guzman: Department of Electrical and Computer Engineering, INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Instituto Superior Técnico (IST), Universidade de Lisboa, Rua Alves Redol, 9, 1000-029 Lisboa, Portugal
Hugo Morais: Department of Electrical and Computer Engineering, INESC-ID—Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Instituto Superior Técnico (IST), Universidade de Lisboa, Rua Alves Redol, 9, 1000-029 Lisboa, Portugal

Energies, 2024, vol. 17, issue 14, 1-27

Abstract: The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework’s effectiveness, and the results demonstrate its proficiency in predicting the EVs’ plugged-in status, with XGBoost’s classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs’ power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape.

Keywords: charging stations; electric vehicles; EVs’ charging behavior forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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