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June 20, 2026Journal of Informatics and Web EngineeringOpen Access

Analysis and Predictive Modelling of EV Charging Patterns and User Behaviour

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Authors

MGMichael Kah Ong GohMultimedia UniversityYLYi Xuan LawMultimedia UniversityCLCheck Yee LawMultimedia University

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Overview

Randomized trial predicts energy consumption and charging duration in electric vehicles, suggesting improved infrastructure planning.

Key Points

  • This research aims to develop a machine learning model for predicting energy consumption and charging duration of electric vehicles.
  • Analyzed a dataset of 1320 EV charging sessions from January to February 2024.
  • Implemented three regression models: Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Regression (SVR).
  • Conducted feature engineering and data preprocessing, followed by model training using GridSearchCV and TimeSeriesSplit cross-validation.
  • Random Forest achieved the highest accuracy in predicting energy consumption with an R² of 0.6620.
  • Light Gradient Boosting Machine performed best in predicting charging duration with an R² of 0.9152.
  • Final tests on unseen data confirmed the models' generalization capabilities.

Cite This Study

Goh et al. (2026) studied this question.

synapsesocial.com/papers/6a362d32db0793dc1a535aa6https://doi.org/10.33093/jiwe.2026.5.2.6
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Understanding User Behaviour and Predicting Charging Costs: A Machine Learning Approach to Support Electric Vehicle Adoption Decisions2025 · 7 citations
  2. 2Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations2026
  3. 3An integrated machine learning framework for EV charging management2026
  4. 4Forecasting Energy Demand for Electric Vehicles Using Machine Learning Techniques2026
  5. 5Predicting EV Charging Duration Using Machine Learning and Charging Transactions at Three Sites2024 · 9 citations