Summary of Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences, by Haoxuan Kuang et al.
Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
by Haoxuan Kuang, Kunxiang Deng, Linlin You, Jun Li
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents CityEVCP, a learning approach for predicting electric vehicle charging demand at the city level. The model addresses limitations in previous studies by incorporating urban region attributes and multivariate temporal influences. It uses attentive hypergraph networks to learn non-pairwise relationships between service areas, which are clustered based on points of interest. Graph attention mechanisms facilitate information propagation between neighboring areas. Additionally, a variable selection network is proposed to adaptively learn dynamic auxiliary information and improve the Transformer encoder’s performance with gated mechanisms for fluctuating charging time-series data. The approach outperforms competing baselines in experiments on a citywide electric vehicle charging dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict how much people will charge their electric vehicles in different parts of a city. It tries to do better than previous studies by considering things like the type of places people visit and changes over time. The researchers use a special kind of computer model that looks at relationships between different areas of the city and helps it learn from this information. They also use another part of the model to pick which extra information is most important and how to use it. The results show that their approach works better than others and can even help us understand why predictions are different in different parts of the city. |
Keywords
» Artificial intelligence » Attention » Encoder » Time series » Transformer
