Multiverse Recurrent Expansion With Multiple Repeats: A Representation Learning Algorithm for Electricity Theft Detection in Smart Grids

Abstract

Electricity theft, known as “Non-Technical Loss” (NTL) is certainly one of the priorities of power distribution utilities. Indeed, NTL could lead to serious damage ranging from massive financial losses to loss of reputation resulting from poor power quality. With advances in metering infrastructure technologies, the availability of user data has fueled the emergence of data-driven methods in NTL detection. Among these methods, deep learning (DL) is an indisputable alternative to conventional human-centric approaches. Typically, modeling based on NTL data is subject to three main challenges, including (i) missing information; (ii) class imbalance; and (iii) data complexity. In this context, this paper contributes to solving these three main problems while paying more attention to data complexity related to cardinality. Accordingly, a multiverse recurrent expansion with multiple repeats (MV-REMR) algorithm is proposed in this paper. MV-REMR is able to provide deeper representations than ordinary DL networks and take advantage of different trained deep network responses to build an efficient model. For MV-REMR efficiency analysis, a realistic NTL dataset is considered. As a result, MV-REMR has shown that it can achieve what is considered excellent feature mapping proven by both scatter visualization and variations in widely used classification metrics. Moreover, MV-REMR shows its ability to marginalize the distance of data classes with superior performance. In addition, thanks to the new mapping scheme, MV-REMR shows its ability to correct outliers resulting from errors in missing values filling techniques. Finally, a comparison with some recent successful works also confirms the superiority of the MV-REMR model.