Big Data Analytics for Electricity Theft Detection in Smart Grids

Khan, Inam Ullah and Javaid, Nadeem and Taylor, C. James and Gamage, Kelum and Ma, Xiandong (2021) Big Data Analytics for Electricity Theft Detection in Smart Grids. In: 2021 IEEE Madrid PowerTech - 14th IEEE Power and Energy Society PowerTech Conference :. IEEE. ISBN 9781665435970

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Abstract

In Smart Grids (SG), Electricity Theft Detection (ETD) is of great importance because it makes the SG cost-efficient. Existing methods for ETD cannot efficiently handle data imbalance, missing values, variance and non-linear data problems in the smart meter data. Therefore, an effective integrated strategy is required to address underlying issues and accurately detect electricity theft using big data. In this work, a simple yet effective approach is proposed by integrating two different modules, such as data pre-processing and classification, in a single framework. The first module involves data imputation, outliers handling, standardization and class balancing steps to generate quality data for classifier training. The second module classifies honest and dishonest users with a Support Vector Machine (SVM) classifier. To improve the classifier’s learning trend and accuracy, a Bayesian optimization algorithm is used to tune SVM’s hyperparameters. Simulation results confirm that the proposed framework for ETD significantly outperforms previous machine learning approaches such as random forest, logistic regression and SVM in terms of accuracy.