BiGRU-CNN Neural Network Applied to Electric Energy Theft Detection

Lucas Duarte Soares,Jesús M López-Lezama,Gloria P López,Edgar M Carreño-Franco,Nicolás Muñoz-Galeano,Altamira De Souza Queiroz

doi:10.3390/electronics11050693

Abstract

This paper presents an assessment of the potential behind the BiGRU-CNN artificial neural network to be used as an electric power theft detection tool. The network is based on different architecture layers of the bidirectional gated recurrent unit and convolutional neural network. The use of such a tool with this classification model can help energy sector companies to make decisions regarding theft detection. The BiGRU-CNN artificial neural network singles out consumer units suspected of fraud for later manual inspections. The proposed artificial neural network was programmed in python, using the keras package. The best detection model was that of the BiGRU-CNN artificial neural network when compared to multilayer perceptron, recurrent neural network, gated recurrent unit, and long short-term memory networks. Several tests were carried out using data of an actual electricity supplier, showing the effectiveness of the proposed approach. The metric values assigned to their classifications were 0.929 for accuracy, 0.885 for precision, 0.801 for recall, 0.841 for F1-Score, and 0.966 for area under the receiver operating characteristic curve.

Highlights

The economic progress of developing countries directly relates to the use of electricity by manufacturing industries
The present study proposes a bidirectional gated recurrent unit (BiGRU)-Convolutional Neural Network (CNN) electric energy theft detection modelconstructed using a Bi-gated recurrent unit (GRU) layer followed by a CNN layer
After training the neural networks through training samples, the internal parameters were tested to verify their ability to generalize the same results for unprecedented data, which are contained in the test sample

Summary

Introduction

The economic progress of developing countries directly relates to the use of electricity by manufacturing industries. There might be numerous reasons behind the shortage of electricity availability; the causes are classified as technical and non-technical losses [4]. Technical losses naturally occur due to irradiation and to electrical energy dissipation during its transmission and distribution, which entails losses in dielectrics and especially in electrical conductors by the Joule effect [5]. Non-technical losses, on the other hand, are defined as any energy consumed or any unbilled service due to the failure of measuring equipment or its fraudulent manipulation. These losses are caused by breakdown or illegal handling at the consumer’s premises. Non-technical losses are very difficult to predict [6]

Methods

Results

Conclusion