Implementation of home energy management system based on reinforcement learning

The implementation of machine learning methods in home energy management have been shown to be a feasible alternative in the minimization of electricity cost. These methods regulate the home electric appliance systems, which contribute to the most critical loads in a household, thus enabling consume...

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Autores principales:	Ejaz Ul Haq, Cheng Lyu, Peng Xie, Shuo Yan, Fiaz Ahmad, Youwei Jia
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Home energy management system Reinforcement learning Energy cost Thermal comfort Energy storage systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e353ff31750740bf9720e26e7cb46626
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id	oai:doaj.org-article:e353ff31750740bf9720e26e7cb46626
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spelling	oai:doaj.org-article:e353ff31750740bf9720e26e7cb466262021-12-04T04:35:12ZImplementation of home energy management system based on reinforcement learning2352-484710.1016/j.egyr.2021.11.170https://doaj.org/article/e353ff31750740bf9720e26e7cb466262022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721013172https://doaj.org/toc/2352-4847The implementation of machine learning methods in home energy management have been shown to be a feasible alternative in the minimization of electricity cost. These methods regulate the home electric appliance systems, which contribute to the most critical loads in a household, thus enabling consumers to save electricity while still enhancing their comfort. Furthermore, renewable energy supplies are continuously integrating with other electricity resources in number of homes that is an important component to optimize energy consumption which result in the reduction of peak load and can bring economic benefits. In this paper, a reinforcement learning algorithm is explored for monitoring household electric appliances with the intention of lowering energy consumption through properly optimizing and addressing the best use renewable energy resources. The proposed method does not necessitate any previous information or knowledge of the uncertain dynamics and parameters of different household electric appliances. Simulation-based findings using real-time data validate the efficiency and reliability of the proposed method.Ejaz Ul HaqCheng LyuPeng XieShuo YanFiaz AhmadYouwei JiaElsevierarticleHome energy management systemReinforcement learningEnergy costThermal comfortEnergy storage systemsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 560-566 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Home energy management system Reinforcement learning Energy cost Thermal comfort Energy storage systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Home energy management system Reinforcement learning Energy cost Thermal comfort Energy storage systems Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ejaz Ul Haq Cheng Lyu Peng Xie Shuo Yan Fiaz Ahmad Youwei Jia Implementation of home energy management system based on reinforcement learning
description	The implementation of machine learning methods in home energy management have been shown to be a feasible alternative in the minimization of electricity cost. These methods regulate the home electric appliance systems, which contribute to the most critical loads in a household, thus enabling consumers to save electricity while still enhancing their comfort. Furthermore, renewable energy supplies are continuously integrating with other electricity resources in number of homes that is an important component to optimize energy consumption which result in the reduction of peak load and can bring economic benefits. In this paper, a reinforcement learning algorithm is explored for monitoring household electric appliances with the intention of lowering energy consumption through properly optimizing and addressing the best use renewable energy resources. The proposed method does not necessitate any previous information or knowledge of the uncertain dynamics and parameters of different household electric appliances. Simulation-based findings using real-time data validate the efficiency and reliability of the proposed method.
format	article
author	Ejaz Ul Haq Cheng Lyu Peng Xie Shuo Yan Fiaz Ahmad Youwei Jia
author_facet	Ejaz Ul Haq Cheng Lyu Peng Xie Shuo Yan Fiaz Ahmad Youwei Jia
author_sort	Ejaz Ul Haq
title	Implementation of home energy management system based on reinforcement learning
title_short	Implementation of home energy management system based on reinforcement learning
title_full	Implementation of home energy management system based on reinforcement learning
title_fullStr	Implementation of home energy management system based on reinforcement learning
title_full_unstemmed	Implementation of home energy management system based on reinforcement learning
title_sort	implementation of home energy management system based on reinforcement learning
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/e353ff31750740bf9720e26e7cb46626
work_keys_str_mv	AT ejazulhaq implementationofhomeenergymanagementsystembasedonreinforcementlearning AT chenglyu implementationofhomeenergymanagementsystembasedonreinforcementlearning AT pengxie implementationofhomeenergymanagementsystembasedonreinforcementlearning AT shuoyan implementationofhomeenergymanagementsystembasedonreinforcementlearning AT fiazahmad implementationofhomeenergymanagementsystembasedonreinforcementlearning AT youweijia implementationofhomeenergymanagementsystembasedonreinforcementlearning
_version_	1718372944499965952

Implementation of home energy management system based on reinforcement learning

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