A electricity theft detection method through contrastive learning in smart grid

As an important edge device of power grid, smart meters enable the detection of illegal behaviors such as electricity theft by analyzing large-scale electricity consumption data. Electricity theft poses a major threat to the economy and the security of society. Electricity theft detection (ETD) methods can effectively reduce losses and suppress illegal behaviors. On electricity consumption data from smart meters, ETD methods always train deep learning models. However, these methods are limited to extract different electricity consumption characteristics between independent users, and the pattern differences between users cannot be actively learned. Such difficulty prevents ETD further performance improvement. Therefore, a novel ETD method is proposed, which is the first attempt to apply supervised contrastive learning for electricity theft detection. On the one hand, our method allows the detection model to improve its detection performance by actively comparing users' representation vectors. On the other hand, in order to obtain high-quality augmented views, largest triangle three buckets time series downsampling is adopted innovatively to improve model stability through data augment. Experiments on real-world datasets show that our model outperforms state-of-the-art models.