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Lookup NU author(s): Weixian Li,
Dr Thillainathan Logenthiran,
Dr Wai Lok Woo
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© 2014 IEEE.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 three stages of decision-making modules, the first stage is the prediction model which uses multimodel 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.
Author(s): Li W, Logenthiran T, Phan V-T, Woo WL
Publication type: Article
Publication status: Published
Journal: IEEE Internet of Things Journal
Print publication date: 01/06/2019
Online publication date: 06/03/2019
Acceptance date: 28/02/2019
ISSN (electronic): 2327-4662
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