A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree  

作　　者：Jinrui Tang Ganheng Ge Jianchao Liu Honghui Yang 

机构地区：[1]School of Automation,Wuhan University of Technology,Wuhan,430070,China

出　　处：《Energy Engineering》2023年第5期1107-1132,共26页能源工程（英文）

基　　金：supported by National Key R&D Program of China(No.2021YFB2601602).

摘　　要：Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).

关 键 词：Electric vehicle charging load density-based spatial clustering of application with noise long-short termmemory load forecasting 

分 类 号：U491.8[交通运输工程—交通运输规划与管理]