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

TL;DR

This paper introduces PAG, a novel graph and attention-based model with physics-informed meta-learning for accurate, interpretable EV charging demand prediction in urban areas, outperforming existing methods.

Contribution

It presents a new spatiotemporal prediction approach combining graph attention, temporal attention, and physics-informed meta-learning for better accuracy and interpretability.

Findings

Achieved state-of-the-art forecasting performance on Shenzhen EV data.

Demonstrated understanding of demand changes due to price fluctuations.

Validated effectiveness of physics-informed meta-learning in demand prediction.

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.