Detection Method for Abnormal Carbon Emission Data of Electric Power Based on LC-GAN
Considering the problem of false injection of bad data in current electricity carbon emission data,a detection method for electricity carbon emission data anomalies based on generative adversarial networks was proposed.Firstly,the improved generation adversarial network for time series data was constructed,and the two-layer long short-term memory network was introduced to deeply mine the internal relationship of time series data;then,the generator and discriminator of the improved model of the generative adversarial network were scored together to identify the anomalies of the electric power carbon emission data;finally,carbon emission data testing for electricity was conducted on a carbon emission monitoring platform in a certain province in East China.The results show that the method of abnormal detection of power carbon emissions data based on the improved generative adversarial network model has stable confrontation training,fast convergence speed of loss function,and a detection rate of 87.5％.The accuracy of abnormal data detection for electric power carbon emissions time series is high.
electric power carbon emissionsgenerative adversarial networktwo-layer long short-term memory networktime series datadata anomaly detection
万方数据
维普
