Smart home energy management systems: Concept, configurations, and scheduling strategies

Zhou, Bin; Li, Wentao; Chan, Ka Wing; Cao, Yijia; Kuang, Yonghong; Liu, Xi; Wang, Xiong

doi:10.1016/j.rser.2016.03.047

Cited by 611 publications

(264 citation statements)

References 81 publications

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“…Threats in this type of scenario could incorporate an impersonation by an attacker to the client, a message modification attack, a replay attack, and much more. For instance, an attacker imitating the customer's cloud [23] that sends a message to HAN/ESI and requests it to turn on all of the home appliances for increasing the electricity bill or causes it to switch off the lights in a smart home, allowing the attacker to threaten the safety of those inside the house [24]. Also, message modification and a replay attack could have a significant impact on the smart home.…”

Section: Attack Against Home Monitoring and Controlmentioning

confidence: 99%

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SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

2017

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Abstract:The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead.

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Section: Attack Against Home Monitoring and Controlmentioning

confidence: 99%

“…The MCA approach employs Triangle Area Map generation (TAM) [24]. The approach extracts the correlated data between features in an observed data object, such as network traffic records.…”

Section: Mca Detection Analysismentioning

confidence: 99%

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

2017

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“…This point is discussed widely in the existing literature, and appliances are categorized in numerous manners [36,53,54]. Considering this work, appliances are categorized as Occupancy Independent (OI) and Occupancy Dependent (OD) appliances.…”

Section: Deviation Functionmentioning

confidence: 99%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

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Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at "low-priced" hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,"User Comfort Level (UCL)" is formulated on the basis of user preferences on cost saving, tolerance in delay regarding use of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

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“…These trends push even further, reaching the building and home level, and leading to the development of concepts such as Home Energy Management Systems (HEMS) [1]. In the smartgrid context, the flexibility features of renewables, storage technologies, demand response (DR) and interaction with the grid [2], can be exploited by different market agents to minimize operation costs.…”

Section: Introductionmentioning

confidence: 99%

Stochastic operation of home energy management systems including battery cycling

2018

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The present work proposes a stochastic approach for Day-Ahead operation of Home Energy Management Systems when batteries, solar photovoltaic resources and Electric Water Heaters are considered. The optimization problem minimizes the operation costs formed by energy procurement in the wholesale market and the equivalent cycling aging cost of the batteries, and also includes the uncertainty of the PV production and the load. The complete two-stage stochastic formulation results in a Mixed-Integer Nonlinear Programming problem that is decomposed using a Competitive Swarm Optimizer to handle the calculation of the battery cycling aging cost. A Storage Disaggregation Algorithm based on Lagrangian relaxation is used to reduce the problem size and to allocate individual State of Charge for the batteries. In addition, the advantages of considering a stochastic approach are shown by means of the Value of the Stochastic Solution. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to a use case that considers an aggregator that participates in the electricity market with a portfolio of prosumers with active demand capability.

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Smart home energy management systems: Concept, configurations, and scheduling strategies

Cited by 611 publications

References 81 publications

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Stochastic operation of home energy management systems including battery cycling

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