Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Haohao Qu, Haoxuan Kuang, Jun Li, Linlin You

TL;DR

Evaluation results on a dataset of 18,061 EV charging piles in Shenzhen, China, show that the proposed approach can achieve state-of-the-art forecasting performance and the ability to understand the adaptive changes in charging demands caused by price fluctuations.

Abstract

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.

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

TL;DR

Evaluation results on a dataset of 18,061 EV charging piles in Shenzhen, China, show that the proposed approach can achieve state-of-the-art forecasting performance and the ability to understand the adaptive changes in charging demands caused by price fluctuations.

Abstract

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.
Paper Structure (21 sections, 14 equations, 10 figures, 3 tables, 2 algorithms)

This paper contains 21 sections, 14 equations, 10 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature review
  3. Emerging challenges
  4. Related solutions
  5. Proposed approach
  6. Problem definition
  7. Graph Embedding Module
  8. Multivariate Decoder Module
  9. Model Pre-training Module
  10. Algorithm of the Proposed Approach
  11. Model evaluation
  12. Evaluation preparation
  13. Evaluation data
  14. The setting of the proposed approach
  15. Compared methods
  16. ...and 6 more sections

Figures (10)

  • Figure 1: Misinterpretation of EV charging demand caused by price fluctuations.
  • Figure 2: Overall structure of the proposed approach, which consists of (a). Graph Embedding Module, (b). Multivariate Decoder Module, and (c). Model Pre-training Module.
  • Figure 3: Calculation process of the multivariate temporal decoder module.
  • Figure 4: Idea behind the model pre-training framework of Physics-Informed Meta-Learning.
  • Figure 5: Spatial distribution of public EV charging piles in Shenzhen, China. The studied area contains 247 traffic zones.
  • ...and 5 more figures