# A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction

arXiv: https://arxiv.org/abs/2309.05259 · HTML: https://mlsanity.com/paper/2309.05259/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)

## Built on by (most cited first, 9 of 9)

- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks](https://mlsanity.com/paper/2405.00742/) (2024)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction](https://mlsanity.com/paper/2503.06370/) (2025)
- [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction](https://mlsanity.com/paper/2510.00457/) (2025)
- [Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences](https://mlsanity.com/paper/2410.18766/) (2024)
- [Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers](https://mlsanity.com/paper/2506.16764/) (2025)
- [TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting](https://mlsanity.com/paper/2508.14782/) (2025)
- [Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting](https://mlsanity.com/paper/2603.01363/) (2026)

## Abstract (arXiv)

Along with the proliferation of electric vehicles (EVs), optimizing the use of EV charging space can significantly alleviate the growing load on intelligent transportation systems. As the foundation to achieve such an optimization, a spatiotemporal method for EV charging demand prediction in urban areas is required. Although several solutions have been proposed by using data-driven deep learning methods, it can be found that these performance-oriented methods may suffer from misinterpretations to correctly handle the reverse relationship between charging demands and prices. To tackle the emerging challenges of training an accurate and interpretable prediction model, this paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction and the usage of physic-informed meta-learning in the model pre-training step for knowledge transfer. Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China, show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance and the ability in understanding the adaptive changes in charging demands caused by price fluctuations.