Towards Efficient Energy Utilization Using Big Data Analytics in Smart Cities for Electricity Theft Detection

Abstract

In energy sectors, power utilities face financial losses due to Electricity Theft (ET). It happens when electricity is consumed without billing. Several methods are developed to detect ET automatically. Most of these methods only assess Electricity Consumption (EC) records. However, it is challenging to detect fraudulent consumers by only observing EC records because of diverse theft strategies (line tapping, meter tampering, etc.) and the irregularity of ET behavior. Furthermore, many methods have poor classification accuracy due to imbalanced data. This work proposes two novel methods to resolve the above-mentioned issues: Tomek Link Borderline Synthetic Minority Oversampling Technique with Support Vector Machine (TBSSVM) and Temporal Convolutional Network with Enhanced Multi-Layer Perceptron (TCN-EMLP). The former resamples the data by balancing the majority and minority class instances. Whereas, the latter classifies normal and fraudulent consumers. Moreover, deep learning models suffer from high variance in their final results due to the assignment of different weights. Therefore, an averaging ensemble strategy is applied in this work to reduce the high variance. Furthermore, State Grid Cooperation of China (SGCC) and Pakistan Residential Electricity Consumption (PRECON) datasets are used in this paper for performing the simulations. SGCC is an imbalanced and labeled dataset while PRECON is an unlabeled dataset comprised of normal consumers' EC records (sequential) and auxiliary (non-sequential) data. Simulation results show that the proposed model outperforms the baselines, i.e., wide and deep convolutional neural network, extreme gradient boosting, long short-term memory with multi-layer perceptron, etc., in terms of ET detection.