Abnormal electricity detection with hybrid deep neural network model

Jie Liu, Xiang Cao, Diangang Wang, Kejia Pan, Cheng Zhang, Xin Wang, Nader Asnafi
2018 MATEC Web of Conferences  
This paper tackles a new challenge in abnormal electricity detection: how to promptly detect stealing electricity behavior by a large-scale data from power users. Proposed scheme firstly forms power consumption gradient model by extracting daily trend indicators of electricity consumption, which can exactly reflect the short-term power consumption trend for each user. Furthermore, we design the line-losing model by analyzing the difference between power supplying and actual power consumption.
more » ... nally, a hybrid deep neural network detection model is built by combining with the power consumption gradient model and the line-losing model, which can quickly pin down to the abnormal electricity users. Comprehensive experiments are implemented by large-scale user samples from the State Grid Corporation and Tensorflow framework. Extensive results show that comparing with the state-of-the-arts, proposed scheme has a superior detection performance, and therefore is believed to be able to give a better guidance to abnormal electricity detection.
doi:10.1051/matecconf/201818903001 fatcat:pn6n67rojbgynnsr33juvpszqa