A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
This addresses the need for accurate and interpretable spatiotemporal prediction models to optimize EV charging space and alleviate load on intelligent transportation systems, representing an incremental improvement over existing data-driven methods.
The paper tackled the problem of predicting regional electric vehicle charging demand by proposing a physics-informed and attention-based graph learning approach, achieving state-of-the-art forecasting performance on a dataset of 18,013 EV charging piles in Shenzhen, China.
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.