Spatial–Temporal Deep Learning for Electric-Vehicle Charging Demand: An Exploratory Study of Graph Convolutional and LSTM Networks Performance

Document Type

Article

Source of Publication

IEEE Access

Publication Date

11-24-2025

Abstract

Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs operated on hourly node–time tensors with a dense adjacency (no self-loops) and were trained on an 80/20 temporal split, using a 1% subsample for tractability. All models used Adam (lr = 0.005), early stopping, and ReduceLROnPlateau. Temporal context was the main driver of LSTM accuracy: 24 h inputs outperformed 1 h, while ZIP features improved only the shorter window. For GCNs, depth and node features shaped performance: a 2-layer GCN with ZIP features achieved the lowest RMSE (4.75 kWh), whereas a 6-layer GCN without node features reached the lowest MAE (2.46 kWh). Despite higher computational cost, GCNs captured spatial coupling effectively. Overall, 24 h LSTMs provide a strong and efficient baseline, while GCNs add value when spatial correlations are relevant. A hybrid GCN–LSTM architecture is a promising next step to jointly leverage spatial and temporal dependencies.

ISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

13

First Page

202203

Last Page

202213

Disciplines

Computer Sciences

Keywords

Electric vehicle charging, energy consumption forecasting, graph convolutional networks (GCN), long short-term memory (LSTM), smart grid management, spatio-temporal deep learning

Scopus ID

105023323827

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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