Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy

Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is ca...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Saqib Kazmi [verfasserIn] Nadeem Javaid [verfasserIn] Muhammad Junaid Mughal [verfasserIn] Mariam Akbar [verfasserIn] Syed Hassan Ahmed [verfasserIn] Nabil Alrajeh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Smart grid knapsack enhanced differential evolution harmony search algorithm home energy management system demand side management

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 24267-24281
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:24267-24281

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2017.2763624

Katalog-ID:	DOAJ047332328

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spelling	10.1109/ACCESS.2017.2763624 doi (DE-627)DOAJ047332328 (DE-599)DOAJa6fa77f9d5844bf08a4bbc86fb4e43bb DE-627 ger DE-627 rakwb eng TK1-9971 Saqib Kazmi verfasserin aut Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management. Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak-to-average ratio, and waiting time minimization. Smart grid knapsack enhanced differential evolution harmony search algorithm home energy management system demand side management Electrical engineering. Electronics. Nuclear engineering Nadeem Javaid verfasserin aut Muhammad Junaid Mughal verfasserin aut Mariam Akbar verfasserin aut Syed Hassan Ahmed verfasserin aut Nabil Alrajeh verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 24267-24281 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:24267-24281 https://doi.org/10.1109/ACCESS.2017.2763624 kostenfrei https://doaj.org/article/a6fa77f9d5844bf08a4bbc86fb4e43bb kostenfrei https://ieeexplore.ieee.org/document/8070309/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 24267-24281
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callnumber-first	T - Technology
author	Saqib Kazmi
spellingShingle	Saqib Kazmi misc TK1-9971 misc Smart grid misc knapsack misc enhanced differential evolution misc harmony search algorithm misc home energy management system misc demand side management misc Electrical engineering. Electronics. Nuclear engineering Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy
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topic	misc TK1-9971 misc Smart grid misc knapsack misc enhanced differential evolution misc harmony search algorithm misc home energy management system misc demand side management misc Electrical engineering. Electronics. Nuclear engineering
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title	Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy
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title_full	Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy
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title_sort	towards optimization of metaheuristic algorithms for iot enabled smart homes targeting balanced demand and supply of energy
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title_auth	Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy
abstract	Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management. Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak-to-average ratio, and waiting time minimization.
abstractGer	Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management. Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak-to-average ratio, and waiting time minimization.
abstract_unstemmed	Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management. Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak-to-average ratio, and waiting time minimization.
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title_short	Towards Optimization of Metaheuristic Algorithms for IoT Enabled Smart Homes Targeting Balanced Demand and Supply of Energy
url	https://doi.org/10.1109/ACCESS.2017.2763624 https://doaj.org/article/a6fa77f9d5844bf08a4bbc86fb4e43bb https://ieeexplore.ieee.org/document/8070309/ https://doaj.org/toc/2169-3536
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