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.