Charging load prediction using variable bandwidth kernel estimation combined with convolutional neural networks
To address the challenges of time-consuming processes,low efficiency,and inaccurate re-sults in Electric Vehicle(EV)charging load prediction,this study proposes a prediction method com-bining variable bandwidth kernel estimation with Convolutional Neural Network(CNN)-based time se-ries prediction.First,the charging and driving data of large-scale EVs are collected by analyzing their charging behaviors and driving habits.Using extensive real-time data,the study conducts an in-depth analysis of multiple factors influencing large-scale EV charging load and constructs a unit mileage en-ergy consumption model based on these influencing factors and actual road conditions.Next,to im-prove data fitting accuracy,three traditional probabilistic models are introduced,and their advantages,disadvantages as well as fitting accuracy are analyzed and compared.Finally,based on the fitting re-sults,the variable bandwidth kernel estimation model with the highest fitting accuracy is used to fit the EV charging load.The fitted results are then combined with a CNN to predict EV charging load.The results demonstrate that the proposed method reduces the average prediction error of EV charging load to 3.11%and the maximum error to 6.42%,which significantly improves the prediction accuracy,pro-viding reference and guidance for the maintenance of power grid systems.
electric vehiclesvariable bandwidth kernel estimationconvolution neural networksload forecasting
万方数据
维普
