A Dynamic Programming Algorithm for Leveraging Probabilistic Detection of Energy Theft in Smart Home

Zhou, Yuchen; Chen, Xiaodao; Zomaya, Albert Y.; Wang, Lizhe; Hu, Shiyan

doi:10.1109/tetc.2015.2484841

Cited by 54 publications

(18 citation statements)

References 11 publications

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“…In recent years, there has been much literature on RTU implementation. However, the RTU device development is limited to Power Distribution and Transmission System [20][21][22][23][24][25][26][27][28][29][30][31]. Thus, this work investigated and analysed the implementation of EV-RTU for the specific purpose in Electric Vehicles based on RTU development literature.…”

Section: Rtu Development For Evmentioning

confidence: 99%

A Review of Hybrid Battery Management System (H-BMS) for EV

et al. 2018

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Significant to a major pollution contrib utor in passenger vehicles, electric vehicles are more acceptab le to use on the road. Electric Vehicles (EVs) b urn energy b ased on the usage of the b attery. The usage of the b attery in EVs is monitored and controlle d b y Battery Management System (BMS). A few factors monitor and control Battery Management System (BMS). This paper reviewed the b attery charging technology and Remote Terminal Unit (RTU) development as a Hyb rid Battery Management System (H-BMS) for Electric Vehicle (EV).

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Section: Rtu Development For Evmentioning

confidence: 99%

A Review of Hybrid Battery Management System (H-BMS) for EV

et al. 2018

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“…75 Reliability can also be compromised when faults crop up in various components. Zhou et al 80 proposes a dynamic programming algorithm that inserts the minimum number of FRTUs satisfying the detection rate constraint. 76 Fault diagnostic methods can be model based or data driven.…”

Section: Security and Reliabilitymentioning

confidence: 99%

“…79 Deployment of Feeder Remote Terminal Unit (FRTU) helps narrow the search zone of energy theft in smart home and community. Zhou et al 80 proposes a dynamic programming algorithm that inserts the minimum number of FRTUs satisfying the detection rate constraint. As a deterministic polynomial time algorithm, it is able to handle large distribution networks.…”

Section: Security and Reliabilitymentioning

confidence: 99%

A review on the features and technologies for energy efficiency of smart grid

Anjana¹,

Shaji

2017

Int J Energy Res

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Summary In today's world striving for efficiency in every sector, especially power generation and distribution, smart grids emerge as the solution for efficiently meeting the increasing demand. They adjust themselves to optimally deliver energy at the lowest cost and highest quality possible. The grid successfully makes use of renewable energy resources, electric vehicles, and smart pricing techniques in its attempt to achieve energy efficiency. It also promotes a greener environment by striving to reduce greenhouse gas emissions. Information communication technology (ICT) helps the grid in collecting consumption data from the consumers and in sharing tariff information. ICT also helps to gather information about the status of the grid with regard to aspects like power quality, faults etc. The purpose of this paper is to review recent literature with a view to comprehensively present the technologies employed in the smart grid for achieving energy efficiency and the challenges involved therein.

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“…Despite this, only a few preventive energy theft methods were created to combat the issue. Zhou, Y. et al proposed a dynamic programming algorithm for leveraging probabilistic detection of energy theft in the smart home [11]. This proposed method requires the deployment of Feeder Remote Terminal Unit (FRTU) on top of a smart meter which incurs high costs for consumers.…”

Section: Introductionmentioning

confidence: 99%

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

Logenthiran

Phan

et al. 2019

IEEE Internet Things J.

123

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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.

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A Dynamic Programming Algorithm for Leveraging Probabilistic Detection of Energy Theft in Smart Home

Cited by 54 publications

References 11 publications

A Review of Hybrid Battery Management System (H-BMS) for EV

A Review of Hybrid Battery Management System (H-BMS) for EV

A review on the features and technologies for energy efficiency of smart grid

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

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