A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids

Aslam, Zeeshan, Javaid, Nadeem, Ahmad, Ashfaq, Ahmed, Abrar and Gulfam, Sardar Muhammad (2020) A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids. Energies, 13 (21). ISSN 1996-1073

[thumbnail of Published_Version]
PDF (Published_Version) - Published Version
Available under License Creative Commons Attribution.

Download (6MB) | Preview


Electricity is widely used around 80% of the world. Electricity theft has dangerous effects on utilities in terms of power efficiency and costs billions of dollars per annum. The enhancement of the traditional grids gave rise to smart grids that enable one to resolve the dilemma of electricity theft detection (ETD) using an extensive amount of data formulated by smart meters. This data are used by power utilities to examine the consumption behaviors of consumers and to decide whether the consumer is an electricity thief or benign. However, the traditional data-driven methods for ETD have poor detection performances due to the high-dimensional imbalanced data and their limited ETD capability. In this paper, we present a new class balancing mechanism based on the interquartile minority oversampling technique and a combined ETD model to overcome the shortcomings of conventional approaches. The combined ETD model is composed of long short-term memory (LSTM), UNet and adaptive boosting (Adaboost), and termed LSTM–UNet–Adaboost. In this regard, LSTM–UNet–Adaboost combines the advantages of deep learning (LSTM-UNet) along with ensemble learning (Adaboost) for ETD. Moreover, the performance of the proposed LSTM–UNet–Adaboost scheme was simulated and evaluated over the real-time smart meter dataset given by the State Grid Corporation of China. The simulations were conducted using the most appropriate performance indicators, such as area under the curve, precision, recall and F1 measure. The proposed solution obtained the highest results as compared to the existing benchmark schemes in terms of selected performance measures. More specifically, it achieved the detection rate of 0.92, which was the highest among existing benchmark schemes, such as logistic regression, support vector machine and random under-sampling boosting technique. Therefore, the simulation outcomes validate that the proposed LSTM–UNet–Adaboost model surpasses other traditional methods in terms of ETD and is more acceptable for real-time practices.

Item Type: Article
Uncontrolled Keywords: sdg 7 - affordable and clean energy ,/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 21 Dec 2021 08:30
Last Modified: 21 Apr 2023 01:18
URI: https://ueaeprints.uea.ac.uk/id/eprint/82750
DOI: 10.3390/en13215599


Downloads per month over past year

Actions (login required)

View Item View Item