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Journals / Middle East Journal of Science / 2025 / Volume: 11 Issue: 2

PREDICTING DAILY ENERGY CONSUMPTION IN EV CHARGING STATIONS USING TREE-BASED MODELS

Merve Ertarğın(MUNZUR UNIVERSITY)0000-0003-4493-7260
Pages
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Publication Date
2025-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Specifically, for the z-score-cleaned dataset, LightGBM demonstrated the best performance with the lowest RMSE (37.35 kWh) and MAE (28.93 kWh), as well as the highest R2 value (0.9727) followed by XGBoost, RF and DT.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electric vehicles (EVs) have emerged as a cornerstone in the transition toward sustainable transportation, offering a cleaner alternative to conventional fossil fuel-based mobility.
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Key Findings & Data:Both the original dataset and its z-score-cleaned version were employed to evaluate the models' performance under different data conditions.
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Conclusion & Impact:Specifically, for the z-score-cleaned dataset, LightGBM demonstrated the best performance with the lowest RMSE (37.35 kWh) and MAE (28.93 kWh), as well as the highest R2 value (0.9727) followed by XGBoost, RF and DT.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Electric vehicles · energy consumption forecasting · LightGBM

Abstract

Electric vehicles (EVs) have emerged as a cornerstone in the transition toward sustainable transportation, offering a cleaner alternative to conventional fossil fuel-based mobility. Their integration into modern transportation systems not only supports global decarbonization efforts but also requires robust energy management solutions to address the growing demands on charging infrastructure. Ensuring efficient energy consumption forecasting is vital for optimizing EV charging station operations and maintaining grid stability. This study aims to predict the daily energy consumption of an EV charging station using a public dataset. Key features such as charging duration, vehicle count, mean and median connection time were utilized for the prediction. Both the original dataset and its z-score-cleaned version were employed to evaluate the models' performance under different data conditions. Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) models were implemented, with hyperparameter optimization conducted via GridSearchCV. The results revealed that all four models achieved high accuracy in predicting daily energy consumption across both datasets, with R2 values exceeding 0.97. Specifically, for the z-score-cleaned dataset, LightGBM demonstrated the best performance with the lowest RMSE (37.35 kWh) and MAE (28.93 kWh), as well as the highest R2 value (0.9727) followed by XGBoost, RF and DT.

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Cite This Article

Ertarğın, M. (2025). PREDICTING DAILY ENERGY CONSUMPTION IN EV CHARGING STATIONS USING TREE-BASED MODELS. Middle East Journal of Science, Volume: 11 Issue: 2. https://doi.org/10.51477/mejs.1674470

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