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.

翻译：随着电动汽车（EV）的普及，优化电动汽车充电空间的使用可显著缓解智能交通系统日益增长的负荷。作为实现此类优化的基础，需要一种适用于城市区域的电动汽车充电需求时空预测方法。尽管已有多种基于数据驱动的深度学习方法被提出，但可以发现这些面向性能的方法在正确解释充电需求与价格之间的反向关系时可能存在误解。针对训练准确且可解释的预测模型所面临的新挑战，本文提出了一种新方法，该方法在特征提取阶段整合了图注意力机制与时序注意力机制，并在模型预训练阶段利用物理信息元学习实现知识迁移。基于中国深圳18,013个电动汽车充电桩数据集的评估结果表明，所提出的方法PAG（基于物理信息的注意力图学习方法）不仅实现了最先进的预测性能，还能够理解由价格波动引起的充电需求适应性变化。