电测与仪表2026，Vol.63Issue(9)：87-95,9.DOI:10.19753/j.issn1001-1390.2026.09.009
基于时空图卷积网络和机器学习的跨地区电价异常识别
Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning
摘要
Abstract
Traditional cross regional electricity price anomaly identification methods are limited by the shallow char-acteristics of data processing and analysis,making it difficult to deeply explore the deep features and potential pat-terns in electricity price data,resulting in low accuracy in electricity price anomaly identification.To this end,a cross regional electricity price anomaly recognition method based on spatiotemporal graph convolutional network and machine learning is proposed.Viewing the topology of the power system network as a graph structure,the spatio-temporal graph matrix of electricity price data is obtained through Fourier transform,a spatiotemporal graph convo-lutional network is created by integrating spatial graph convolutional layers and time gated convolutional layers,and the spatiotemporal characteristics of cross regional electricity price anomalies are output through network learning;a cross regional electricity price anomaly recognition model is built based on support vector machines,mutation,crossover,and selection processes of differential evolution are used to optimize model parameters,and an electricity price sample dataset is trained to obtain anomaly recognition results.The experimental results show that the pro-posed method has high accuracy and fast recognition efficiency in identifying abnormal electricity prices,and can provide reference for energy management and data analysis in power enterprises.关键词
时空图卷积网络/机器学习/支持向量机/电价异常识别/差分进化Key words
spatiotemporal graph convolutional network/machine learning/support vector machine/identification of abnormal electricity price/differential evolution分类
信息技术与安全科学引用本文复制引用
陈思宇,黄绪荣,刘颖,陈璐,覃恺,谢佩..基于时空图卷积网络和机器学习的跨地区电价异常识别[J].电测与仪表,2026,63(9):87-95,9.基金项目
广西电网有限公司科技项目(044600KC23040002) （044600KC23040002）
