A Novel Smart Energy Theft System (SETS) for IoT based Smart Home

Li, Weixian, Logenthiran, Thillainathan, Phan, Van Tung and Woo, Wai Lok (2019) A Novel Smart Energy Theft System (SETS) for IoT based Smart Home. IEEE Internet of Things Journal, 6 (3). pp. 5531-5539. ISSN 2327-4662

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Official URL: https://ieeexplore.ieee.org/document/8661504


In the modern smart home, smart meters and Internet of Things (IoT) have been massively deployed to replace traditional analogue meters. It digitalises the data collection and the meter readings. The data can be wirelessly transmitted that significantly reduces manual works. However, the community of smart home network is vulnerable to energy theft. Such attacks cannot be effectively detected since the existing techniques require certain devices to be installed to work. This imposes a challenge for energy theft detection systems to be implemented despite the lack of energy monitoring devices. This paper develops an energy detection system called Smart Energy Theft System (SETS) based on machine learning and statistical models. There are 3 stages of decision-making modules, the first stage is the prediction model which uses multi-model forecasting System. This system integrates various machine learning models into a single forecast system for predicting the power consumption. The second stage is the primary decision making model that uses Simple Moving Average (SMA) for filtering abnormally. The third stage is the secondary decision making model that makes the final stage of the decision on energy theft. The simulation results demonstrate that the proposed system can successfully detect 99.96% accuracy that enhances the security of the IoT based smart home.

Item Type: Article
Uncontrolled Keywords: Smart homes, Smart grid, Internet of things, energy theft, machine learning techniques
Subjects: G400 Computer Science
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 06 Mar 2019 12:27
Last Modified: 01 Aug 2021 11:18
URI: http://nrl.northumbria.ac.uk/id/eprint/38304

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