电机与控制应用2024，Vol.51Issue(3)：21-29,9.DOI:10.12177/emca.2023.191
基于多头注意力机制和门控循环单元神经网络的居民充电桩容量预测
Residential Charging Station Capacity Prediction Based on Multi-Head Attention and Gated Recurrent Unit Neural Network
摘要
Abstract
The capacity prediction of residential charging stations can provide a reference for its capacity selection and contribute to the carbon peaking and caron neutrality goals.In this regard,a data-driven method for predicting the capacity of residential charging stations is proposed.Firstly,historical capacity data of residential charging stations are collected and preprocessed.Secondly,different-sized time windows are used to slice the data as input features.Finally,a prediction model combining multi-head attention mechanism and gated recurrent unit neural network is constructed,and the features are input into the model to achieve accurate prediction of future capacity.The results of the case analysis show that the exponential mean absolute error and exponential root mean square error of the model are 33.19 and 102.14％respectively.Compared to other models,the proposed model significantly improves the prediction accuracy and provides new insights for capacity prediction of residential charging stations.关键词
数据驱动/充电桩/容量预测/多头注意力机制/门控循环单元神经网络Key words
data-driven/charging station/capacity prediction/multi-head attention mechanism/gated recurrent unit neural network分类
信息技术与安全科学引用本文复制引用
谢乐,杨浙,刘东..基于多头注意力机制和门控循环单元神经网络的居民充电桩容量预测[J].电机与控制应用,2024,51(3):21-29,9.基金项目
国家自然科学联合基金(U1934204) （U1934204）
四川省区域创新合作项目(21QYCX0096)Joint Funds of the National Natural Science Foundation of China(U1934204) （21QYCX0096）
Sichuan Provincial Regional Innovation Cooperation Project(21QYCX0096) （21QYCX0096）
