Next Article in Journal
Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge
Next Article in Special Issue
Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems
Previous Article in Journal
The Potential of Wobble Plate Opposed Piston Axial Engines for Increased Efficiency
Previous Article in Special Issue
Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler
Article

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
School of Electrical Engineering and Computing, The University of Newcastle, Callaghan 2308, Australia
3
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan
*
Authors to whom correspondence should be addressed.
Energies 2020, 13(21), 5599; https://doi.org/10.3390/en13215599
Received: 21 September 2020 / Revised: 17 October 2020 / Accepted: 19 October 2020 / Published: 26 October 2020
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
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. View Full-Text
Keywords: electricity theft detection; smart grids; electricity consumption; electricity thefts; smart meter; imbalanced data electricity theft detection; smart grids; electricity consumption; electricity thefts; smart meter; imbalanced data
Show Figures

Figure 1

MDPI and ACS Style

Aslam, Z.; Javaid, N.; Ahmad, A.; Ahmed, A.; Gulfam, S.M. A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids. Energies 2020, 13, 5599. https://doi.org/10.3390/en13215599

AMA Style

Aslam Z, Javaid N, Ahmad A, Ahmed A, Gulfam SM. A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids. Energies. 2020; 13(21):5599. https://doi.org/10.3390/en13215599

Chicago/Turabian Style

Aslam, Zeeshan, Nadeem Javaid, Ashfaq Ahmad, Abrar Ahmed, and Sardar M. Gulfam 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids" Energies 13, no. 21: 5599. https://doi.org/10.3390/en13215599

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop