An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems
Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, bil...
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doaj-eacd8cbf73bd4b89aabba365ec762e2d2021-03-30T03:50:53ZengIEEEIEEE Access2169-35362020-01-01822176722178210.1109/ACCESS.2020.30426369281043An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution SystemsZeeshan Aslam0Fahad Ahmed1https://orcid.org/0000-0003-3260-3044Ahmad Almogren2https://orcid.org/0000-0002-8253-9709Muhammad Shafiq3https://orcid.org/0000-0001-7337-7608Mansour Zuair4https://orcid.org/0000-0003-2490-5739Nadeem Javaid5https://orcid.org/0000-0003-3777-8249Department of Computer Science, COMSATS University Islamabad, Islamabad Campus, Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad Campus, Islamabad, PakistanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Islamabad Campus, Islamabad, PakistanElectricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts' involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.https://ieeexplore.ieee.org/document/9281043/Electricity theft detectionsmart gridsrelational denoising autoencoderelectricity consumptionTripleGAN |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zeeshan Aslam Fahad Ahmed Ahmad Almogren Muhammad Shafiq Mansour Zuair Nadeem Javaid |
spellingShingle |
Zeeshan Aslam Fahad Ahmed Ahmad Almogren Muhammad Shafiq Mansour Zuair Nadeem Javaid An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems IEEE Access Electricity theft detection smart grids relational denoising autoencoder electricity consumption TripleGAN |
author_facet |
Zeeshan Aslam Fahad Ahmed Ahmad Almogren Muhammad Shafiq Mansour Zuair Nadeem Javaid |
author_sort |
Zeeshan Aslam |
title |
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems |
title_short |
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems |
title_full |
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems |
title_fullStr |
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems |
title_full_unstemmed |
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems |
title_sort |
attention guided semi-supervised learning mechanism to detect electricity frauds in the distribution systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts' involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches. |
topic |
Electricity theft detection smart grids relational denoising autoencoder electricity consumption TripleGAN |
url |
https://ieeexplore.ieee.org/document/9281043/ |
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