# Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand

arXiv: https://arxiv.org/abs/2106.10940 · HTML: https://mlsanity.com/paper/2106.10940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems](https://mlsanity.com/paper/1808.08798/) (2018)
- [Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes](https://mlsanity.com/paper/2001.07402/) (2020)
- [Demand Prediction for Electric Vehicle Sharing](https://mlsanity.com/paper/1903.04051/) (2019)

## Built on by (most cited first, 5 of 5)

- [Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks](https://mlsanity.com/paper/2405.00742/) (2024)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)
- [A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting](https://mlsanity.com/paper/2511.22072/) (2025)
- [Discovering EV Charging Site Archetypes Through Few Shot Forecasting: The First U.S.-Wide Study](https://mlsanity.com/paper/2510.26910/) (2025)
- [Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods](https://mlsanity.com/paper/2512.17257/) (2025)

## Abstract (arXiv)

Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph Convolution Models are the most suitable to capture the correlations. The proposed Temporal Graph Convolutional Networks provide the most accurate forecasts for short and long-term forecasting compared with other forecasting methods.