Research on abnormal power consumption detection method of power big data based on LightGBM model and LSTM model
With the proposal of the dual-carbon economy,smart grids are developing in the direction of energy con-servation and emission reduction,and the abnormal power consumption of users has caused serious loss of power re-sources.Aiming at the problems of low accuracy and slow operation efficiency of traditional abnormal power con-sumption detection methods,a lightGBM model combined with an improved long short-term memory network model is proposed for abnormal power consumption detection.Anomaly detection is carried out by combining sampling and lightGBM model,and abnormal electricity consumption category is given by improving long short-term memory net-work model.The advantages of the proposed method are analyzed through experiments.The results show that,com-pared with traditional detection methods,the proposed method can detect abnormal users quickly and effectively,with a detection accuracy of 98.64％,meanwhile,the abnormal data is effectively classified,and the comprehen-sive classification accuracy rate is 96.60％,which provides a certain reference for the development of anomaly de-tection technology.
power transformerabnormal power consumptionLightGBM modelLSTM modeldual-carbon economy
万方数据
维普
