Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

Benish Kabir, Umar Qasim, Nadeem Javaid (), Abdulaziz Aldegheishem, Nabil Alrajeh and Emad A. Mohammed
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Benish Kabir: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Umar Qasim: Department of Computer Science, University of Engineering and Technology at Lahore (New Campus), Lahore 54000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Abdulaziz Aldegheishem: Department of Urban Planning, College of Architecture and Planning, King Saud University, Riyadh 11574, Saudi Arabia
Nabil Alrajeh: Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Emad A. Mohammed: Department of Engineering, Faculty of Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, Canada

Sustainability, 2022, vol. 14, issue 22, 1-19

Abstract: The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively.

Keywords: deep learning; GRU; healthcare; MLP; non-technical losses; PRECON; smart cities; smart grids; smart meters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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