Electricity Theft Detection Based on Multi-head Attention Mechanism

Abstract

Electricity theft causes significant damage to social and economic development.How to detect malicious electricity theft based on power big data has been widely concerned by academia and industry.Aiming at the problems of traditional methods relying on manual features,insufficient behavior sequence representation,poor detection accuracy,etc.,this paper proposes an electricity theft detection model based on multi-head attention mechanism (ETD-MHA).The bidirectional gated recurrent unit is used to fully capture the time features of the electricity consumption behavior sequence,and the distinction of key features is gradually enhanced in the multi-head attention mechanism,and finally,the learning effect is improved by deepening the networks.Extended experiments are conducted on the smart meter datasets of Ireland and China State Grid.The results show that the proposed method achieves better performance compared with the linear regression (LR),support vector machine (SVM),random forest (RF),and other traditional algorithms.For example,the AUC value of the proposed model is improved by up to 34.6%compared to the LR algorithm.

Keywords

• smart grids|electricity theft detection|deep learning|gated recurrent neural network|multi-head attention mechanism