A stacked machine and deep learning-based approach for analysing electricity theft in smart grids

Khan, I. U., Javeid, N., Taylor, C. J., Gamage, K. A.A. and Ma, X. (2022) A stacked machine and deep learning-based approach for analysing electricity theft in smart grids. IEEE Transactions on Smart Grid, 13(2), pp. 1633-1644. (doi: 10.1109/TSG.2021.3134018)

Abstract

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications.

• Item Type: Articles
• Additional Information: This work was supported in part by Lancaster University, U.K.; in part by COMSATS University Islamabad (Lahore Campus); and in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/R02572X/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gamage, Professor Kelum
• Authors: Khan, I. U., Javeid, N., Taylor, C. J., Gamage, K. A.A., and Ma, X.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Transactions on Smart Grid
• Publisher: IEEE
• ISSN: 1949-3053
• ISSN (Online): 1949-3061
• Published Online: 09 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Smart Grid 13(2):1633-1644
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher