PSO-LSSVM based load power usage anomaly data identification

The anomaly identification research is carried out for the anomalous data existing in the load and power consumption dataset. Firstly, the LSSVM algorithm is used to identify the abnormal data, in view of the problem of low recognition accuracy of the LSSVM algorithm, the LSSVM algorithm optimized based on the improved PSO algorithm is proposed; then, the PSO-LSSVM algorithm is trained by using the historical load and power consumption dataset and abnormal dataset, and the abnormal data recognition model is obtained to identify the daily load curve which contains the abnormal data of load and power consumption. Finally, the feature curve judgment method is used to determine the exact location of the abnormal load and power consumption data in the curve. Comparison and analysis of simulation examples verify the reliability of PSO-LSSVM algorithm in identifying the abnormal electricity data.