电力建设2025，Vol.46Issue(9)：57-70,14.DOI:10.12204/j.issn.1000-7229.2025.09.005
融合路网-气象-日期多特征信息的电动汽车充电负荷预测
Electric Vehicle Charging Load Forecasting by Integrating Multi-Feature Information of Road Network,Meteorology,and Date
摘要
Abstract
[Objective]To address the challenge of load forecasting for multiple spatially dispersed and mutually coupled electric vehicle charging stations in urban road networks,a joint forecasting model integrating road network,meteorological,and data features is proposed.This model aims to enhance the accuracy and efficiency of multistation load forecasting,providing technical support for the economic dispatch and efficient operation of new power systems(NPSs).[Methods]The model employs an improved Dijkstra algorithm to construct a spatial correlation network among charging stations and utilizes a graph convolutional network(GCN)to integrate road network features.Meteorological variables were selected using the Spearman rank correlation coefficient,and date features were binary-encoded to distinguish between weekdays and non-weekdays.To mitigate the high computational complexity of transformer models in long-term forecasting,a selective state-space model(SSSM)based on Mamba was introduced to capture long-term temporal features while reducing computational demands.[Results]The model was validated using operational data from 45 charging stations in a city in the Jilin Province.The results demonstrate that the GCN-Mamba model effectively captures inter-station interactions and the impacts of external factors such as meteorology and dates.Its root mean square error(RMSE)and mean absolute error(MAE)were significantly lower than those of traditional models,and its computation time was reduced by approximately 30%compared with transformer models,indicating superior performance in multistation load forecasting.[Conclusions]The GCN-Mamba model innovatively combines road network spatial correlations with the SSSM and offers a novel approach for NPS load forecasting.Furthermore,it exhibits high accuracy and efficiency.However,this study is limited by the use of a single-city dataset.Future work should include multi-city and multi-scenario validations,as well as improvements to the adaptability of the model under extreme weather conditions.关键词
电动汽车/多充电站负荷联合预测/图卷积神经网络/选择性状态空间模型/多特征融合Key words
electric vehicle/joint load prediction of multiple charging stations/graph convolutional neural network/selective state-space model/multifeature fusion分类
信息技术与安全科学引用本文复制引用
胡枭,张泽朕,杨家全,杨金铎,和学豪,王闯..融合路网-气象-日期多特征信息的电动汽车充电负荷预测[J].电力建设,2025,46(9):57-70,14.基金项目
国家重点研发计划资助项目(2023YFB2407300)This work is supported by National Key R&D Program of China(No.2023YFB2407300). （2023YFB2407300）
