Deep transfer learning based assistant system for optimal investment decision of distribution networks

With the rapid development of clean energy and the deepening of the interaction between supply and demand, power grid investment upgrading measures involve many new elements, such as clean energy installation and distribution automation. Traditional investment decision-making models are difficult to...

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Autores principales:	Jianping Yang, Yue Xiang, Wei Sun, Junyong Liu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Investment decision-making Correlation rule Deep transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/87c519195d4a4fa68e2926f4f4006a66
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id	oai:doaj.org-article:87c519195d4a4fa68e2926f4f4006a66
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spelling	oai:doaj.org-article:87c519195d4a4fa68e2926f4f4006a662021-12-04T04:35:07ZDeep transfer learning based assistant system for optimal investment decision of distribution networks2352-484710.1016/j.egyr.2021.11.135https://doaj.org/article/87c519195d4a4fa68e2926f4f4006a662022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S235248472101283Xhttps://doaj.org/toc/2352-4847With the rapid development of clean energy and the deepening of the interaction between supply and demand, power grid investment upgrading measures involve many new elements, such as clean energy installation and distribution automation. Traditional investment decision-making models are difficult to establish and solve. In view of this, this paper analyzes the investment benefit mechanism directly from the perspective of investment input–output relationship, and designs an interactive auxiliary investment decision-making system based on correlation rule mining. The system constructs an investment benefit mapping model from power grid investment measures to benefit output by means of deep transfer learning, and provides three objective functions, which consider the optimal economy, performance improvement and comprehensive index optimization, thus assisting decision makers to formulate investment alternatives according to different investment needs. A case demonstrates the decision-making process based on an actual power grid, and verifies the practicability and effectiveness of the system.Jianping YangYue XiangWei SunJunyong LiuElsevierarticleInvestment decision-makingCorrelation ruleDeep transfer learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 91-96 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Investment decision-making Correlation rule Deep transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Investment decision-making Correlation rule Deep transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jianping Yang Yue Xiang Wei Sun Junyong Liu Deep transfer learning based assistant system for optimal investment decision of distribution networks
description	With the rapid development of clean energy and the deepening of the interaction between supply and demand, power grid investment upgrading measures involve many new elements, such as clean energy installation and distribution automation. Traditional investment decision-making models are difficult to establish and solve. In view of this, this paper analyzes the investment benefit mechanism directly from the perspective of investment input–output relationship, and designs an interactive auxiliary investment decision-making system based on correlation rule mining. The system constructs an investment benefit mapping model from power grid investment measures to benefit output by means of deep transfer learning, and provides three objective functions, which consider the optimal economy, performance improvement and comprehensive index optimization, thus assisting decision makers to formulate investment alternatives according to different investment needs. A case demonstrates the decision-making process based on an actual power grid, and verifies the practicability and effectiveness of the system.
format	article
author	Jianping Yang Yue Xiang Wei Sun Junyong Liu
author_facet	Jianping Yang Yue Xiang Wei Sun Junyong Liu
author_sort	Jianping Yang
title	Deep transfer learning based assistant system for optimal investment decision of distribution networks
title_short	Deep transfer learning based assistant system for optimal investment decision of distribution networks
title_full	Deep transfer learning based assistant system for optimal investment decision of distribution networks
title_fullStr	Deep transfer learning based assistant system for optimal investment decision of distribution networks
title_full_unstemmed	Deep transfer learning based assistant system for optimal investment decision of distribution networks
title_sort	deep transfer learning based assistant system for optimal investment decision of distribution networks
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/87c519195d4a4fa68e2926f4f4006a66
work_keys_str_mv	AT jianpingyang deeptransferlearningbasedassistantsystemforoptimalinvestmentdecisionofdistributionnetworks AT yuexiang deeptransferlearningbasedassistantsystemforoptimalinvestmentdecisionofdistributionnetworks AT weisun deeptransferlearningbasedassistantsystemforoptimalinvestmentdecisionofdistributionnetworks AT junyongliu deeptransferlearningbasedassistantsystemforoptimalinvestmentdecisionofdistributionnetworks
_version_	1718372980345536512

Deep transfer learning based assistant system for optimal investment decision of distribution networks

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