电力系统自动化2026，Vol.50Issue(15)：123-133,11.DOI:10.7500/AEPS20241216002
基于改进Informer的多时间尺度电动汽车可调度容量时空分布预测
Improved Informer Based Multi-timescale Spatio-temporal Distribution Prediction of Electric Vehicle Schedulable Capacity
摘要
Abstract
High-precision and fine-grained multi-timescale prediction of electric vehicle(EV)schedulable capacity is of great significance for large-scale EV participating in power grid ancillary services.However,existing EV schedulable capacity prediction algorithms suffer from a significant reduction in prediction accuracy and efficiency when the prediction sequences are long.To address the requirements of multiple dispatching scenarios in the power system,this paper proposes an improved Informer algorithm to construct a multi-timescale,aggregated,schedulable capacity prediction model for electric vehicle aggregators(EVAs)in terms of day-ahead,ultra-short-term,and real-time scales.First,the historical schedulable capacity data of EV As obtained from processing large-scale individual EV charging records is used as the data set of the improved Informer prediction algorithm.Then,the convolutional sparse attention mechanism is used to replace the attention mechanism in the traditional Informer algorithm,and an improved Informer algorithm is constructed to enhance its performance in capturing trend information of historical time sequences,thereby improving the prediction accuracy of long sequences.Finally,more than half a million actual historical charging records of EVs in a city are used as samples to validate the proposed model at multiple timescales and spatial dimensions.The results show that the proposed method significantly improves the prediction accuracy on different timescales as well as reduces the prediction time of long sequences.关键词
电动汽车/车网互动/可调度容量/深度学习/多时间尺度/Informer/预测/注意力机制Key words
electric vehicle(EV)/vehicle-to-grid(V2G)/schedulable capacity/deep learning/multi-timescale/Informer/prediction/attention mechanism引用本文复制引用
茆美琴,刘志博,王吉文,朱明磊,杜燕,施永..基于改进Informer的多时间尺度电动汽车可调度容量时空分布预测[J].电力系统自动化,2026,50(15):123-133,11.基金项目
安徽省自然科学基金资助项目(2108085UD02) （2108085UD02）
国家自然科学基金资助项目(51577047) （51577047）
高等学校学科创新引智计划("111"计划)资助项目(BP0719039). This work is supported by Anhui Provincial Natural Science Foundation of China(No.2108085UD02),National Natural Science Foundation of China(No.51577047),and Program of Introducing Talents of Discipline to Universities("111"Program)(No.BP0719039). （"111"计划）
