RoadPowerFM: Graphormer-JEPA-Based Foundation Model for Road-Power Coupling Network

Coupling effects in road-power coupling networks (RPCNs) attract increasing attention, as they are crucial for addressing the road-power intertwined challenges caused by the rapid growth of electric vehicles (EVs). To solve these challenges, this paper, based on Graphormer and Joint-Embedding Predictive Architecture (JEPA), proposes a road power foundation model (RPFM) as a generic solution. Our RPFM, by employing graph-pretraining methods to bridge RPCNs, demonstrates exceptional performance in downstream tasks such as EV Charging Station (EVCS) Load Prediction, Road Traffic Prediction, and EVCS Location Planning. By experimenting on real world dataset, the proposed methodology is proved to be generic and achieves state-of-the-art performance across downstream tasks by improving an average of 7.53% of baseline models’ performance.