Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning

Chen Siyu; Huang Xurong; Liu Ying; Chen Lu; Qin Kai; Xie Pei

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Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning

更新时间：2026-09-14

- Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning
- Vol. 63, Issue 9, (2026)
- 作者机构：
  
  1. 广西电网有限责任公司
  2. 广西电网有限责任公司贵港供电局
  3. 广西电网有限责任公司南宁供电局
- 作者简介：
- 基金信息：
- DOI：
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- Published：2026
- 稿件说明：
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Chen Siyu, Huang Xurong, Liu Ying, et al. Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning[J]. 2026, 63(9).
DOI：

Chen Siyu, Huang Xurong, Liu Ying, et al. Cross regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning[J]. 2026, 63(9). DOI：

摘要

传统跨地区电价异常识别方法受限于数据处理和分析的浅层次特性，难以深入挖掘电价数据中的深层次特征和潜在模式，导致电价异常识别精度较低。为此，提出一种基于时空图卷积网络和机器学习的跨地区电价异常识别方法。将电力系统网络拓扑看作图结构，通过傅里叶变换得到电价数据时空图矩阵，融合空间图卷积层和时间门控卷积层创建时空图卷积网络，通过网络学习输出跨地区电价异常时空特征；组建基于支持向量机的跨地区电价异常识别模型，运用差分进化的变异、交叉和选择环节寻优模型参数，训练电价样本数据集得到异常识别结果。实验结果表明，所提方法电价异常识别精度高，识别效率高，可为电力企业能源管理和数据分析提供参考。

Abstract

Traditional cross regional electricity price anomaly identification methods are limited by the shallow characteristics of data processing and analysis

making it difficult to deeply explore the deep features and potential patterns 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 spatiotemporal graph matrix of electricity price data is obtained through Fourier transform

a spatiotemporal graph convolutional 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 proposed 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.

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