Efficient detection of abnormal user electricity consumption behavior is the foundation for ensuring the safety and stability of the power grid

in response to the problems of poor accuracy and low efficiency in existing detection methods for abnormal user electricity consumption behavior

a user abnormal electricity consumption behavior detection strategy is proposed based on an electric energy information collection system

which combines an improved K-means algorithm

an improved time dynamic bending algorithm

and a Stacking ensemble learning algorithm. By improving the K-means algorithm to cluster the characteristic curves of user electricity consumption behavior

the time dynamic bending algorithm to complete the initial screening of user abnormal electricity consumption

by using the Stacking ensemble learning algorithm to determine user abnormal electricity consumption behavior. Its superiority is verified through experiments. The results indicate that

the proposed method has the best detection performance compared to conventional detection methods

with a detection accuracy of 99.93% and a detection time of 8.32 seconds. It can provide certain assistance for the safety of the power grid.