A Survey of Home Energy Management Systems in Future Smart Grid Communications

Javaid, Nadeem; Khan, İlyas; Ullah, M. N.; Mahmood, Anzar; Farooq, Muhammad Umar

doi:10.1109/bwcca.2013.80

Cited by 45 publications

(20 citation statements)

References 10 publications

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“…It is a challenge to ensure that an SB that has various components will be intelligible. Typically, each of these components might have been produced by different vendors, each of which may have created under different design constraints and considerations [306]. Therefore it becomes essential to satisfy interoperability so that a number of heterogeneous communication and networking technologies could coexist in various parts of SBs.…”

Section: E Interoperabilitymentioning

confidence: 99%

“…Achieving expected levels of reliability, especially when linked with communication technologies utilized with these devices that may be expected in SBs, is a great challenge. There are different reasons for these challenges differences in technological approaches, regulations, development culture, and the expectations of the market [306].…”

Section: F Reliabilitymentioning

confidence: 99%

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Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

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Section: E Interoperabilitymentioning

confidence: 99%

Section: F Reliabilitymentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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“…Benefits of an energy management program may be increased if distributed energy resources (DER) coordination is adopted in parallel with the appliance coordination [100]. In [97], the concept of the DER coordination has been assessed.…”

Section: Energy Management By Considering Pricing Schemementioning

confidence: 99%

Energy Management for Smart Homes—State of the Art

2019

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Smart home is a concept that aims to enhance the comfort of residents and facilitate household activities. The smart home is an application of ubiquitous computing which can provide the user with context-aware automated or assistive services in the form of ambient intelligence, remote control of home appliances, or automation. Smart homes attempt to integrate smartness into homes to guarantee the residents’ convenience, safety, and security, while conserving the energy. The capabilities of a smart home in the context of different applications, have been scrutinized for this investigation. Different proposed architectures, protocols, and infrastructures have been taken into consideration. As the data management process is a vital part of a smart home system, many procedures of data collection, storage, and analysis have been surveyed. Methods of data acquisition has also been discussed. Existing challenges, pros, and cons of proposed schemes along with future perspectives of smart homes are identified in this report, which is intended to promote future research directions.

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“…Such as ToUP, DAP, peak pricing (PP), CPP and RTP [40]. In most of the appliances scheduling schemes, the pricing of electricity is assumed to be DAP or ToUP, because the RTP increase the communication complexity.…”

Section: Energy Pricing Modelmentioning

confidence: 99%

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

et al. 2017

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Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

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A Survey of Home Energy Management Systems in Future Smart Grid Communications

Cited by 45 publications

References 10 publications

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Energy Management for Smart Homes—State of the Art

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

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