Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations

Mustafa Teke; Mahmud Esad Yiğit

doi:10.30939/ijastech..1905847

Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations

Abstract

The rapid global increase in electric vehicles (EVs) usage is leading a significant burden on charging infrastructure and requires precise energy management solutions for grid stability. Accurate forecasting of daily energy consumption at charging stations is critical for minimizing operational costs and optimizing infrastructure planning. This study presents a comprehensive comparative performance analysis of the deployment of different learning models to predict the daily energy demand of EV charging stations. In this paper, predictions have been performed based on variables such as the number of vehicles, charging time and connection time using real-time charging data. Machine Learning (ML) algorithms such as the Decision Tree, Random Forest, Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Bayesian Ridge, Hist Gradient, XGBoost, LightGBM, CatBoost, Ensemble Learning and Deep Learning (DL)-based TabNet have been integrated to resolve the complex and nonlinear structure of the dataset. The prediction performances of the models have been compared using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-Squared (Coefficient of Determination-R²) metrics. According to the analysis results, the TabNet architecture has been the most successful model in terms of overall performance, achieving the lowest RMSE of 37.30 and the highest R2 score of 0.9724. It has been followed by the Ensemble Voting model, which shared the same coefficient of determination (R2: 0.9724) and produced the lowest MAE value of 28.77. The obtained testing results demonstrate that TabNet and Ensemble Voting outperform traditional methods in capturing complex user behaviors and variable charging patterns.

Keywords

References

Nasri S, Mansouri N, Mnassri A, Lashab A, Vasquez J, Rezk H. Global analysis of electric vehicle charging infrastructure and sustainable energy sources solutions. World Electr Veh J. 2025;16(4):194. https://doi.org/10.3390/wevj16040194
Alaraj M, Radi M, Alsisi E, Majdalawieh M, Darwish M. Machine learning-based electric vehicle charging demand forecasting: a systematized literature review. Energies. 2025;18(17):4779. https://doi.org/10.3390/en18174779
Szumska E, Pawlik Ł, Frej D, Wilk-Jakubowski JŁ. Machine learning applications in energy consumption forecasting and management for electric vehicles: a systematic review. Energies. 2025;18(20):5420. https://doi.org/10.3390/en18205420
Akshay KC, Grace GH, Gunasekaran K, Samikannu R. Power consumption prediction for electric vehicle charging stations and forecasting income. Sci Rep. 2024;14:6497. https://doi.org/10.1038/s41598-024-56507-2
Matkovic D, Sudic I, Capuder T. Day-ahead and intra-day forecasting of electric vehicle charging station energy consumption. In: 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech); 2023; Split/Bol, Croatia. https://doi.org/10.23919/SpliTech58164.2023.10193158
Almaghrebi A, Aljuheshi F, Rafaie M, James K, Alahmad M. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies. 2020;13(16):4231. https://doi.org/10.3390/en13164231
Abida A, Majdoul R, Zegrari M. Comparative of prediction algorithms for energy consumption by electric vehicle chargers for demand side management. Int J Electr Comput Eng. 2025;15(4):4192-4201. https://doi.org/10.11591/ijece.v15i4.pp4192-4201
Ostermann A, Haug T. Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement. Energy Inform. 2024;7:13. https://doi.org/10.1186/s42162-024-00319-1

Hussain I, Ching KB, Uttraphan C, Tay KG, Noor A. Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: a comparative study. Sci Rep. 2025;15:16124. https://doi.org/10.1038/s41598-025-94946-7
Zhang T, Peng Q, Zeng S. Predicting EV charging demand in renewable-energy-powered grids using explainable machine learning. Sustainability. 2025;17(9):4158. https://doi.org/10.3390/su17094158
Shern SJ, Sarker MT, Haram MHSM, Ramasamy G, Thiagarajah SP, Al Farid F. Artificial intelligence optimization for user prediction and efficient energy distribution in electric vehicle smart charging systems. Energies. 2024;17(22):5772. https://doi.org/10.3390/en17225772
Sreekumar AV, Lekshmi RR. Electric vehicle charging station demand prediction model deploying data slotting. Results Eng. 2024;24:103095. https://doi.org/10.1016/j.rineng.2024.103095
Ertarğin M. Predicting daily energy consumption in EV charging stations using tree-based models. Middle East J Sci. 2025;11(2):263-275. https://doi.org/10.51477/mejs.1674470
Osman H, Azab A, AlGhazi A, Duffuaa S, Baki F. Forecasting electric vehicle charging loads using random forest and gene expression programming ensemble models. Results Eng. 2025;28:108369. https://doi.org/10.1016/j.rineng.2025.108369
Mystakidis A, Tsalikidis N, Koukaras P, Skaltsis G, Ioannidis D, Tjortjis C, et al. EV charging forecasting exploiting traffic, weather and user information. Int J Mach Learn Cybern. 2025;16:6737-6763. https://doi.org/10.1007/s13042-025-02643-8
Talaat FM, Salem M, Hamza AA. EV charging duration prediction using advanced ensemble learning techniques and feature importance analysis. Neural Comput Appl. 2025;37(30):25257-25281. https://doi.org/10.1007/s00521-025-11576-w
Genov E, De Cauwer C, Van Kriekinge G, Coosemans T, Messagie M. Forecasting flexibility of charging of electric vehicles: tree and cluster-based methods. Appl Energy. 2024;353:121969. https://doi.org/10.1016/j.apenergy.2023.121969
Jabari M, Ghoreishi M, Bragatto T, Santori F, Maccioni M, Bellesini F. Predictive modeling and analysis of energy consumption in EV charging stations using machine learning techniques. In: 2025 IEEE International Conference on Environment and Electrical Engineering and 2025 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe); 2025; Chania, Crete, Greece. https://doi.org/10.1109/EEEIC/ICPSEurope64998.2025.11169195
Koohfar S, Woldemariam W, Kumar A. Performance comparison of deep learning approaches in predicting EV charging demand. Sustainability. 2023;15(5):4258. https://doi.org/10.3390/su15054258
Ressler Z, Grijalva M, Ignacio AM, Torres M, Rojas AC, Moghadam R, et al. LSTM-based forecasting and analysis of EV charging demand in a dense urban campus. arXiv Preprint. 2025;arXiv:2510.16719. https://doi.org/10.48550/arXiv.2510.16719
Akshayaa RN, Sajidha SA, Radha R. Session and energy forecasting for electric vehicle charging station using custom weighted ensemble machine learning. Results Eng. 2025;26:105104. https://doi.org/10.1016/j.rineng.2025.105104
Gholizadeh N, Musilek P. Daily electric vehicle charging dataset for training reinforcement learning algorithms. Data Brief. 2024;55:110587. https://doi.org/10.1016/j.dib.2024.110587
Lee ZJ, Li T, Low SH. ACN-Data: analysis and applications of an open EV charging dataset. In: Proceedings of the Tenth ACM International Conference on Future Energy Systems; 2019; Phoenix, AZ, USA. https://doi.org/10.1145/3307772.3328313
Barr AA, Rozman R, Guo E. Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation. arXiv Preprint. 2025;arXiv:2502.14523. https://doi.org/10.48550/arXiv.2502.14523
Chen JQ, He YL, Cheng YC, et al. A multiple kernel-based kernel density estimator for multimodal probability density functions. Eng Appl Artif Intell. 2024;132:107979. https://doi.org/10.1016/j.engappai.2024.107979
Hussain M, Haque A, Mateen S. Predictive analytics for EV charging demand: a machine learning perspective. In: 2025 7th International Conference on Energy, Power and Environment (ICEPE); 2025; Sohra (Cherrapunjee), India. https://doi.org/10.1109/ICEPE65965.2025.11139647
González S, García S, Del Ser J, Rokach L, Herrera F. A practical tutorial on bagging and boosting based ensembles for machine learning: algorithms, software tools, performance study, practical perspectives and opportunities. Inf Fusion. 2020;64:205-237. https://doi.org/10.1016/j.inffus.2020.07.007
Wang Y, Luo Q, Cao L, Seth A, Liu J, Gopaluni B. Data-driven battery capacity estimation using support vector regression and model bagging under fast-charging conditions. Can J Chem Eng. 2024;102(10):3322-3332. https://doi.org/10.1002/cjce.25394
Chung YW, Khaki B, Li T, Chu C, Gadh R. Ensemble machine learning-based algorithm for electric vehicle user behavior prediction. Appl Energy. 2019;254:113732. https://doi.org/10.1016/j.apenergy.2019.113732
Jafari S, Kim J, Choi W, Byun YC. Integrating multilayer perceptron and support vector regression for enhanced state of health estimation in lithium-ion batteries. IEEE Access. 2025;13:11463-11478. https://doi.org/10.1109/ACCESS.2024.3497656
Wang Y, Gao G, Li X, Chen Z. A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory. J Power Sources. 2020;449:227543. https://doi.org/10.1016/j.jpowsour.2019.227543
Wang Y, Chen Z. A framework for state-of-charge and remaining discharge time prediction using unscented particle filter. Appl Energy. 2020;260:114324. https://doi.org/10.1016/j.apenergy.2019.114324
Ma S, Ning J, Mao N, Liu J, Shi R. Research on machine learning-based method for predicting industrial park electric vehicle charging load. Sustainability. 2024;16(17):7258. https://doi.org/10.3390/su16177258
Tolun ÖC, Zor K, Tutsoy O. A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction. J Supercomput. 2025;81:475. https://doi.org/10.1007/s11227-025-06975-8
Kashifi MT, Ahmad I. Efficient histogram-based gradient boosting approach for accident severity prediction with multisource data. Transp Res Rec. 2022;2676(6):236-258. https://doi.org/10.1177/03611981221074370
Lim Y, Bae S, Moon J. Optimal control scheme of electric vehicle charging using combined model of XGBoost and cumulative prospect theory. Energies. 2024;17(24):6457. https://doi.org/10.3390/en17246457
Mu Y, Zhou R, Zhao K, Jia H, Zu G, Yang Y. A charging demand prediction method for individual electric vehicle users based on dual-layer multisource data clustering and a LightGBM. Energy AI. 2025;22:100644. https://doi.org/10.1016/j.egyai.2025.100644
Siddiqui J, Ahmed U, Amin A, Alharbi T, Alharbi A, Aziz I, et al. Electric vehicle charging station load forecasting with an integrated DeepBoost approach. Alex Eng J. 2025;116:331-341. https://doi.org/10.1016/j.aej.2024.12.069
Sfar W, Amhaimar L, Khalidi A, Talbi B. A hybrid long-term photovoltaic power prediction model integrating a BiLSTM network with residual correction via CatBoost. Results Eng. 2026;29:108898. https://doi.org/10.1016/j.rineng.2025.108898
Ullah I, Liu K, Yamamoto T, Zahid M, Jamal A. Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach. Int J Green Energy. 2021;18(9):896-909. https://doi.org/10.1080/15435075.2021.1881902
Dikmen İC, Yildiran N, Karadag T. Machine learning approaches for enhancing the SoH estimation of LTO batteries. Int J Automot Sci Technol. 2025;9(1):48-59. https://doi.org/10.30939/ijastech..1522403
Mühendis A, Akkaya R. Integrating machine learning with advanced features of L9963E BMS for enhanced state of charge estimation. Int J Automot Sci Technol. 2025;9(4):475-490. https://doi.org/10.30939/ijastech..1698286
Arık SÖ, Pfister T. TabNet: attentive interpretable tabular learning. Proc AAAI Conf Artif Intell. 2021;35(8):6679-6687. https://doi.org/10.1609/aaai.v35i8.16826

Details

Primary Language

English

Subjects

Automotive Engineering (Other)

Journal Section

Research Article

Authors

Mustafa Teke
0000-0002-7262-4918
Türkiye

Mahmud Esad Yiğit ^*
0000-0002-0148-1913
Türkiye

Publication Date

June 6, 2026

Submission Date

March 9, 2026

Acceptance Date

May 22, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

DOI

https://doi.org/10.30939/ijastech..1905847

IZ

https://izlik.org/JA53DN55BF

Cite

RIS / Bibtex

APA

Teke, M., & Yiğit, M. E. (2026). Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations. International Journal of Automotive Science And Technology, 10(2), 400-410. https://doi.org/10.30939/ijastech..1905847

AMA

1.Teke M, Yiğit ME. Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations. IJASTECH. 2026;10(2):400-410. doi:10.30939/ijastech.1905847

Chicago

Teke, Mustafa, and Mahmud Esad Yiğit. 2026. “Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations”. International Journal of Automotive Science And Technology 10 (2): 400-410. https://doi.org/10.30939/ijastech. 1905847.

EndNote

Teke M, Yiğit ME (June 1, 2026) Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations. International Journal of Automotive Science And Technology 10 2 400–410.

IEEE

[1]M. Teke and M. E. Yiğit, “Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations”, IJASTECH, vol. 10, no. 2, pp. 400–410, June 2026, doi: 10.30939/ijastech..1905847.

ISNAD

Teke, Mustafa - Yiğit, Mahmud Esad. “Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations”. International Journal of Automotive Science And Technology 10/2 (June 1, 2026): 400-410. https://doi.org/10.30939/ijastech. 1905847.

JAMA

1.Teke M, Yiğit ME. Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations. IJASTECH. 2026;10:400–410.

MLA

Teke, Mustafa, and Mahmud Esad Yiğit. “Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations”. International Journal of Automotive Science And Technology, vol. 10, no. 2, June 2026, pp. 400-1, doi:10.30939/ijastech. 1905847.

Vancouver

1.Mustafa Teke, Mahmud Esad Yiğit. Comparative Performance Analysis of Predictive Model Deployment for Daily Energy Demand of Electric Vehicle Charging Stations. IJASTECH. 2026 Jun. 1;10(2):400-1. doi:10.30939/ijastech. 1905847