Non technical losses in power systems

represented by abnormal electricity consumption by power users

will typically result in significant increase in the operating costs of power supply companies. Firstly

deep neural detection method for abnormal electricity consumption by power users is proposed. Based on the characteristics of electricity consumption by power users

a deep confidence network（DBN）is used to extract features from the original electricity load data and obtain corresponding features.Then

feature classification is completed using an extreme learning machine（ELM）

thus establishing a basic model for detecting abnormal electricity consumption by power users. Finally

an improved fruit fly optimization algorithm（IFOA）to optimize the network weights and inter layer bias parameters of DBN is proposed

thereby obtaining an abnormal electricity consumption detection model for power users based on IFOA-DBN-ELM. Experimental results show that compared with other detection methods

the accuracy

precision

and detection rate of the method proposed are significantly higher

and false detection rate are lower than other methods. It can accurately detect power users with abnormal electricity consumption behavior and help reduce the operating costs of power supply companies.