Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

Abstract

In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

“…The proposed architecture employs a generator based on DRN and a discriminator based on CNN to provide high reconstruction accuracy while sacrificing the computational complexity [95]. Reference [96] employs Wasserstein GAN with gradient penalty to capture the real distribution of the electricity consumption data for electricity theft detection. Reference [97] employs GAN for power loss mitigation of active distribution networks.…”

Image recognition algorithms based on deep learning

“…Its effect exceeded sigmoid in deeper networks, optimized the gradient dispersion problem of sigmoid in deeper networks, and established the position of convolutional neural network in deep learning. With the increase of data volume, the problems encountered become more and more complex, followed by more excellent models such as AlexNet [3] , VGG [4] , GoogleNet [5] and MobileNet [6] .…”

An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

“…Tumen et al implemented the LSTM fraud detection algorithm [12], which excels the anomaly detector but does not automatically generate features [12]; therefore, there is a data selection module which attempts to select some data. Ahmed et al [13] implemented two algorithms that handle the issue that fraud data are imbalanced data. The authors use Python imbalance handling library SMOTEENN which performs oversampling by the SMOTE function and cleaning using ENN: "synthetic minority oversampling technique with edited nearest neighbor" [14].…”