基于知识嵌入超图深度学习的城市快充站运行状态估计OA

Accurate operation state estimation for urban fast-charging stations is a crucial prerequisite for the efficient utilization of electric vehicle charging networks.Existing studies lack the consideration of the actual operation mechanisms of fast-charging stations,failing to explore the spatio-temporal interaction characteristics of multi-station operation states.To address this,by transforming state estimation into a sample generation process that integrates operation knowledge and historical data of fast-charging stations,this paper proposes an operation state estimation method for urban fast-charging stations based on knowledge-embedded hypergraph deep learning architecture.First,the dynamic operation states of fast-charging stations and their multi-spatial distribution are represented as a hypergraph model,and the temporal convolution attention network and hypergraph convolution network are used to capture temporal similarity and spatial correlation in graph-structured feature information.Second,operation strategies of fast-charging stations,physical rules,and constraints are decoupled into a knowledge loss function,which is integrated into the sample learning process of generative adversarial networks to construct a knowledge-data dual-driven loss function.Through the"encoding-decoding"paradigm,the conditional probability distribution of samples is implicitly learned to estimate the operation state of individual charging piles across different fast-charging stations.Finally,using the actual road network and operating fast-charging stations in Nanjing,China,as a case study,simulation results demonstrate that the proposed method can accurately characterize the spatio-temporal distribution characteristics of operation states for fast-charging stations,providing theoretical support for subsequent electric vehicle charging scheduling.