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 b...

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Bibliographic Details
Published in2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI) pp. 1298 - 1302
Main Authors Suwei, Liu, Jianye, Liu, Wanting, Yin, Qinwu, Liao, Ying, Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.05.2024
Subjects
abnormal data
Accuracy
Electricity
electricity load
least squares support vector machine
Parameter estimation
particle swarm algorithm
Power demand
Power grids
Prediction algorithms
Support vector machines
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Summary: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.
DOI:10.1109/ICETCI61221.2024.10594434