Charging load prediction using variable bandwidth kernel estimation combined with convolutional neural networks
WANG Guojun
WANG Liye
LIAO Chenglin
WANG Lifang
YUAN Xiaodong
WANG Mingshen
Abstract：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.
Keywords：electric vehiclesvariable bandwidth kernel estimationconvolution neural networksload forecasting
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 155-161 )