Smart City Gnosys
Smart city article details
| Title | Forecasting Electric Vehicle Charging Demand In Smart Cities Using Hybrid Deep Learning Of Regional Spatial Behaviours |
|---|---|
| ID_Doc | 26834 |
| Authors | Cavus M.; Ayan H.; Dissanayake D.; Sharma A.; Deb S.; Bell M. |
| Year | 2025 |
| Published | Energies, 18, 13 |
| DOI | http://dx.doi.org/10.3390/en18133425 |
| 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 (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. © 2025 by the authors. |
| Author Keywords | charging infrastructure; deep learning; demand forecasting; electric vehicles; hybrid models; machine learning; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
42812  | 0.924 | Appadurai J.P.; Rajesh T.; Yugha R.; Sarkar R.; Thirumalraj A.; Kavin B.P.; Seng G.H. | Prediction Of Ev Charging Behavior Using Boa-Based Deep Residual Attention Network | Revista Internacional de Metodos Numericos para Calculo y Diseno en Ingenieria, 40, 2 (2024) |
| 35913 | 0.919 | Shahriar S.; Al-Ali A.R.; Osman A.H.; Dhou S.; Nijim M. | Machine Learning Approaches For Ev Charging Behavior: A Review | IEEE Access, 8 (2020) |
| 40684 | 0.918 | Vijay Kumar M.; Gondesi J.R.; Krishna G.S.; Kumar I.A. | Optimization Of Users Ev Charging Data Using Convolutional Neural Network | Lecture Notes in Networks and Systems, 731 LNNS (2024) |
| 22524 | 0.91 | Doda D.K.; Beemkumar N.; Awasthi A.; Gautam A.K. | Electric Vehicle Energy Management: Charging In Sustainable Urban Settings For Smart Cities | E3S Web of Conferences, 540 (2024) |
| 24056 | 0.903 | Aldossary M. | Enhancing Urban Electric Vehicle (Ev) Fleet Management Efficiency In Smart Cities: A Predictive Hybrid Deep Learning Framework | Smart Cities, 7, 6 (2024) |
| 7022 | 0.898 | Khan A.A.; Mahendran R.K.; Ullah F.; Ali F.; Bashir A.K.; Dabel M.M.A.; Omar M. | Ai-Driven Dynamic Allocation And Management Optimization For Ev Charging Stations | IEEE Transactions on Intelligent Transportation Systems (2025) |
| 40337 | 0.892 | Alanazi F.; Alshammari T.O.; Azam A. | Optimal Charging Station Placement And Scheduling For Electric Vehicles In Smart Cities | Sustainability (Switzerland), 15, 22 (2023) |
| 8508 | 0.885 | Senthilkumar T.; Sivaraju S.S.; Anuradha T.; Vimalarani C. | An Intelligent Electric Vehicle Charging System In A Smart Grid Using Artificial Intelligence | Optimal Control Applications and Methods, 46, 3 (2025) |
| 22520 | 0.884 | Mazhar T.; Asif R.N.; Malik M.A.; Nadeem M.A.; Haq I.; Iqbal M.; Kamran M.; Ashraf S. | Electric Vehicle Charging System In The Smart Grid Using Different Machine Learning Methods | Sustainability (Switzerland), 15, 3 (2023) |
| 40790 | 0.883 | Suanpang P.; Jamjuntr P. | Optimizing Electric Vehicle Charging Recommendation In Smart Cities: A Multi-Agent Reinforcement Learning Approach | World Electric Vehicle Journal, 15, 2 (2024) |