Institut for Forretningsudvikling og Teknologi

Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services

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Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services. / Nasab, Morteza Azimi; Zand, Mohammad; Eskandari, Mohsen; Sanjeevikumar, Padmanaban; Siano, Pierluigi.

I: Smart Cities, Bind 4, Nr. 3, 09.2021, s. 1173-1195.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Nasab, MA, Zand, M, Eskandari, M, Sanjeevikumar, P & Siano, P 2021, 'Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services', Smart Cities, bind 4, nr. 3, s. 1173-1195. https://doi.org/10.3390/smartcities4030063

APA

Nasab, M. A., Zand, M., Eskandari, M., Sanjeevikumar, P., & Siano, P. (2021). Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services. Smart Cities, 4(3), 1173-1195. https://doi.org/10.3390/smartcities4030063

CBE

MLA

Vancouver

Nasab MA, Zand M, Eskandari M, Sanjeevikumar P, Siano P. Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services. Smart Cities. 2021 sep.;4(3):1173-1195. https://doi.org/10.3390/smartcities4030063

Author

Nasab, Morteza Azimi ; Zand, Mohammad ; Eskandari, Mohsen ; Sanjeevikumar, Padmanaban ; Siano, Pierluigi. / Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services. I: Smart Cities. 2021 ; Bind 4, Nr. 3. s. 1173-1195.

Bibtex

@article{c992a4bbad294a33940b507c427c3ceb,
title = "Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services",
abstract = "One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users' requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.",
keywords = "energy storage, electrical appliance, home area energy management system (HAEMS), neural network, renewable energy resources, smart cities, ENERGY MANAGEMENT, SIMULTANEOUS-OPTIMIZATION, OPERATIONAL STRATEGY, HYBRID METHOD, DG CAPACITY, SYSTEM, MICROGRIDS",
author = "Nasab, {Morteza Azimi} and Mohammad Zand and Mohsen Eskandari and Padmanaban Sanjeevikumar and Pierluigi Siano",
year = "2021",
month = sep,
doi = "10.3390/smartcities4030063",
language = "English",
volume = "4",
pages = "1173--1195",
journal = "Smart Cities",
issn = "2624-6511",
publisher = "MDPI",
number = "3",

}

RIS

TY - JOUR

T1 - Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services

AU - Nasab, Morteza Azimi

AU - Zand, Mohammad

AU - Eskandari, Mohsen

AU - Sanjeevikumar, Padmanaban

AU - Siano, Pierluigi

PY - 2021/9

Y1 - 2021/9

N2 - One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users' requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

AB - One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users' requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

KW - energy storage

KW - electrical appliance

KW - home area energy management system (HAEMS)

KW - neural network

KW - renewable energy resources

KW - smart cities

KW - ENERGY MANAGEMENT

KW - SIMULTANEOUS-OPTIMIZATION

KW - OPERATIONAL STRATEGY

KW - HYBRID METHOD

KW - DG CAPACITY

KW - SYSTEM

KW - MICROGRIDS

U2 - 10.3390/smartcities4030063

DO - 10.3390/smartcities4030063

M3 - Journal article

VL - 4

SP - 1173

EP - 1195

JO - Smart Cities

JF - Smart Cities

SN - 2624-6511

IS - 3

ER -