# Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences

arXiv: https://arxiv.org/abs/2410.18766 · HTML: https://mlsanity.com/paper/2410.18766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://mlsanity.com/paper/2309.05305/) (2023)
- [Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction](https://mlsanity.com/paper/2204.02650/) (2022)
- [A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction](https://mlsanity.com/paper/2309.05259/) (2023)
- [Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks](https://mlsanity.com/paper/2106.04986/) (2021)

## Abstract (arXiv)

Electric vehicle charging demand prediction is important for vacant charging pile recommendation and charging infrastructure planning, thus facilitating vehicle electrification and green energy development. The performance of previous spatio-temporal studies is still far from satisfactory nowadays because urban region attributes and multivariate temporal influences are not adequately taken into account. To tackle these issues, we propose a learning approach for citywide electric vehicle charging demand prediction, named CityEVCP. To learn non-pairwise relationships in urban areas, we cluster service areas by the types and numbers of points of interest in the areas and develop attentive hypergraph networks accordingly. Graph attention mechanisms are employed for information propagation between neighboring areas. Additionally, we propose a variable selection network to adaptively learn dynamic auxiliary information and improve the Transformer encoder utilizing gated mechanisms for fluctuating charging time-series data. Experiments on a citywide electric vehicle charging dataset demonstrate the performances of our proposed approach compared with a broad range of competing baselines. Furthermore, we demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our area clustering method.