IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3425-d1690568.html
   My bibliography  Save this article

Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours

Author

Listed:
  • Muhammed Cavus

    (Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8SA, UK)

  • Huseyin Ayan

    (School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
    School of Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye)

  • Dilum Dissanayake

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK)

  • Anurag Sharma

    (Faculty of Science, Agriculture & Engineering (SAgE), Newcastle University, Newcastle upon Tyne NE1 7RU, UK)

  • Sanchari Deb

    (School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK)

  • Margaret Bell

    (School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK)

Abstract

This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. Motivated by the accelerating regional demand accompanying EV adoption, this work introduces HCB-Net: a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Extreme Gradient Boosting (XGBoost) for robust regression. The framework is trained on user-level survey data from two demographically distinct UK regions, the West Midlands and the North East, incorporating user demographics, commute distance, charging frequency, and home/public charging preferences. HCB-Net achieved superior predictive performance, with a Root Mean Squared Error (RMSE) of 0.1490 and an R 2 score of 0.3996. Compared to the best-performing traditional model (Linear Regression, R 2 = 0.3520 ), HCB-Net improved predictive accuracy by 13.5% in terms of R 2 , and outperformed other deep learning models such as LSTM ( R 2 = − 0.3756 ) and GRU ( R 2 = − 0.6276 ), which failed to capture spatial patterns effectively. The hybrid model also reduced RMSE by approximately 23% compared to the standalone CNN (RMSE = 0.1666). While the moderate R 2 indicates scope for further refinement, these results demonstrate that meaningful and interpretable demand forecasts can be generated from survey-based behavioural data, even in the absence of high-resolution temporal inputs. The model contributes a lightweight and scalable forecasting tool suitable for early-stage smart city planning in contexts where telemetry data are limited, thereby advancing the practical capabilities of EV infrastructure forecasting.

Suggested Citation

  • Muhammed Cavus & Huseyin Ayan & Dilum Dissanayake & Anurag Sharma & Sanchari Deb & Margaret Bell, 2025. "Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours," Energies, MDPI, vol. 18(13), pages 1-30, June.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3425-:d:1690568
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3425/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/13/3425/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Armin Razmjoo & Meysam Majidi Nezhad & Lisa Gakenia Kaigutha & Mousa Marzband & Seyedali Mirjalili & Mehdi Pazhoohesh & Saim Memon & Mehdi A. Ehyaei & Giuseppe Piras, 2021. "Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    2. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    3. Torkey, Alaa & Abdelgawad, Hossam, 2022. "Framework for planning of EV charging infrastructure: Where should cities start?," Transport Policy, Elsevier, vol. 128(C), pages 193-208.
    4. Heidrich, Oliver & Hill, Graeme A. & Neaimeh, Myriam & Huebner, Yvonne & Blythe, Philip T. & Dawson, Richard J., 2017. "How do cities support electric vehicles and what difference does it make?," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 17-23.
    5. Abdullah Dik & Siddig Omer & Rabah Boukhanouf, 2022. "Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration," Energies, MDPI, vol. 15(3), pages 1-26, January.
    6. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    7. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    8. Guilherme Gloriano de Souza & Ricardo Ribeiro dos Santos & Ruben Barros Godoy, 2025. "Optimizing power grids: A valley-filling heuristic for energy-efficient electric vehicle charging," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-36, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ayşe Tuğba Yapıcı & Nurettin Abut & Tarık Erfidan, 2025. "Comparing the Effectiveness of Deep Learning Approaches for Charging Time Prediction in Electric Vehicles: Kocaeli Example," Energies, MDPI, vol. 18(8), pages 1-21, April.
    2. Bogdanov, Dmitrii & Breyer, Christian, 2024. "Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level," Energy, Elsevier, vol. 301(C).
    3. Schulz, Arne & Boysen, Nils & Briskorn, Dirk, 2024. "Centrally-chosen versus user-selected swaps: How the selection of swapping stations impacts standby battery inventories," European Journal of Operational Research, Elsevier, vol. 319(3), pages 726-738.
    4. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    5. Heeyun Lee & Hyunjoong Kim & Hyewon Kim & Hyunsup Kim, 2025. "Optimal Vehicle-to-Grid Charge Scheduling for Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 18(5), pages 1-15, February.
    6. Lydia Chu, 2023. "Why Do Consumers Buy Green Smart Buildings without Engaging in Energy-Saving Behaviors in the Workplace? The Perspective of Materialistic Value," Sustainability, MDPI, vol. 15(12), pages 1-9, June.
    7. Paul Plachinda & Julia Morgan & Maria Coelho, 2022. "Towards Net Zero: Modeling Approach to the Right-Sized Facilities," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    8. Benoliel, Peter & Taylor, Margaret & Coburn, Timothy & Desai, Ranjit R. & Schey, Stephen & Gerdes, Mindy & Peng, Peng, 2025. "Soft costs and EVSE – Knowledge gaps as a barrier to successful projects," Applied Energy, Elsevier, vol. 389(C).
    9. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    10. Kuang, Haoxuan & Deng, Kunxiang & You, Linlin & Li, Jun, 2025. "Citywide electric vehicle charging demand prediction approach considering urban region and dynamic influences," Energy, Elsevier, vol. 320(C).
    11. Peng, Yuan & Bai, Xuemei, 2023. "What EV users say about policy efficacy: Evidence from Shanghai," Transport Policy, Elsevier, vol. 132(C), pages 16-26.
    12. Aparna Venugopal & Dhirendra Shukla, 2019. "Identifying consumers' engagement with renewable energy," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 53-63, January.
    13. Chaoxi Liang & Qingtao Yang & Hongyuan Sun & Xiaoming Ma, 2024. "Unveiling consumer satisfaction and its driving factors of EVs in China using an explainable artificial intelligence approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    14. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    15. Tian, Zhirui & Sun, Wenpu & Wu, Chenye, 2025. "MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting," Applied Energy, Elsevier, vol. 383(C).
    16. Wang, Shengyou & Li, Yuan & Shao, Chunfu & Wang, Pinxi & Wang, Aixi & Zhuge, Chengxiang, 2025. "An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 383(C).
    17. Francisco Haces-Fernandez, 2023. "Risk Assessment Framework for Electric Vehicle Charging Station Development in the United States as an Ancillary Service," Energies, MDPI, vol. 16(24), pages 1-17, December.
    18. Stefano Menicanti & Marco di Benedetto & Davide Marinelli & Fabio Crescimbini, 2022. "Recovery of Trains’ Braking Energy in a Railway Micro-Grid Devoted to Train plus Electric Vehicle Integrated Mobility," Energies, MDPI, vol. 15(4), pages 1-25, February.
    19. Rajaeifar, Mohammad Ali & Tabatabaei, Meisam & Aghbashlo, Mortaza & Nizami, Abdul-Sattar & Heidrich, Oliver, 2019. "Emissions from urban bus fleets running on biodiesel blends under real-world operating conditions: Implications for designing future case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 276-292.
    20. Yellapragada Venkata Pavan Kumar & Sivakavi Naga Venkata Bramareswara Rao & Kottala Padma & Challa Pradeep Reddy & Darsy John Pradeep & Aymen Flah & Habib Kraiem & Michał Jasiński & Srete Nikolovski, 2022. "Fuzzy Hysteresis Current Controller for Power Quality Enhancement in Renewable Energy Integrated Clusters," Sustainability, MDPI, vol. 14(8), pages 1-22, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3425-:d:1690568. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.