Browse by author
Lookup NU author(s): Muhammed CavusORCiD, Dr Huseyin Ayan, Dr Dilum Dissanayake, Dr Anurag SharmaORCiD, Dr Sanchari Deb, Emerita Professor Margaret Carol Bell CBE
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 by the authors. 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 (Formula presented.) score of 0.3996. Compared to the best-performing traditional model (Linear Regression, (Formula presented.)), HCB-Net improved predictive accuracy by 13.5% in terms of (Formula presented.), and outperformed other deep learning models such as LSTM ((Formula presented.)) and GRU ((Formula presented.)), 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 (Formula presented.) 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.
Author(s): Cavus M, Ayan H, Dissanayake D, Sharma A, Deb S, Bell M
Publication type: Article
Publication status: Published
Journal: Energies
Year: 2025
Volume: 18
Issue: 13
Online publication date: 29/06/2025
Acceptance date: 26/06/2025
Date deposited: 21/07/2025
ISSN (electronic): 1996-1073
Publisher: MDPI
URL: https://doi.org/10.3390/en18133425
DOI: 10.3390/en18133425
Data Access Statement: The data and code are publicly available at https://github.com/cavusmuhammed68/Low_Carbon (accessed on 1 June 2025).
Altmetrics provided by Altmetric
