基于多源条件去噪扩散模型的电动车充电站占用率预测OA

[Background]The rapid growth of electric vehicles has intensified spatial competition among charging stations,making accurate occupancy rate prediction essential for grid scheduling and improving user experience.Compared with conventional forecasting of the total charging load,occupancy rates better capture users'discrete choice behavior,which is shaped by multi-source spa-tiotemporal factors,such as pricing and the distance between the target station and surrounding loca-tions.[Objective]This study aims to effectively capture complex spatiotemporal competitive rela-tionships across heterogeneous information sources and enable more efficient data fusion.[Method]This study proposes a charging station occupancy prediction approach based on a multi-source condi-tional denoising diffusion probability model.The model integrates the target station's historical occu-pancy data,dynamic electricity pricing,meteorological information,and exponentially weighted pric-ing information from surrounding stations.A gated fusion mechanism is used to combine dynamic and static features,and a median-prior fine-tuning strategy is incorporated to improve the quality of the predicted probability intervals.[Data]Experiments were conducted using the publicly available UrbanEV dataset.[Result]The experimental results show that the proposed multi-source conditional denoising diffusion probabilistic model significantly outperforms the autoregressive integrated mov-ing average model,long short-term memory networks,transformers,spatiotemporal graph convolu-tional networks,and denoising diffusion probabilistic models.This improvement addresses the limi-tations of traditional approaches in handling multisource,heterogeneous data and complex spatial competition.[Application]The proposed method provides an effective solution for accurate charg-ing station occupancy prediction,which can support grid scheduling and improve the user charging experience.