Abstract: The non-technical loss （NTL） on the power consumption side caused by malicious power theft by users has always been one of the problems that power companies around the world expect to solve. With the rapid development of artificial intelligence algorithms and the popularization of smart meters， modeling and detection of electricity theft will effectively reduce the occurrence of such situations. Firstly， this article introduces the methods of collecting， processing， and sampling electricity consumption behavior data. Secondly， it analyzes and compares the characteristics of various algorithms and summarizes existing work on outlier detection， machine learning methods， and deep learning methods for mining abnormal electricity behavior. Finally， by discussing the problems of intelligent methods in the research of electricity theft detection and future research works， it provides some reference for researchers in this field.

Key words: smart grid, electricity theft detection, machine learning, deep learning