A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances

In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be ab...

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Autores principales:	Claudia De Vizia, Edoardo Patti, Enrico Macii, Lorenzo Bottaccioli
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Demand side management energy aggregator agent based modeling learn user preference user acceptance Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6505e0e70dab42aa973e8214a9f6020f
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id	oai:doaj.org-article:6505e0e70dab42aa973e8214a9f6020f
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spelling	oai:doaj.org-article:6505e0e70dab42aa973e8214a9f6020f2021-11-18T00:05:54ZA Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances2169-353610.1109/ACCESS.2021.3125247https://doaj.org/article/6505e0e70dab42aa973e8214a9f6020f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599666/https://doaj.org/toc/2169-3536In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month.Claudia De ViziaEdoardo PattiEnrico MaciiLorenzo BottaccioliIEEEarticleDemand side managementenergy aggregatoragent based modelinglearn user preferenceuser acceptanceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150495-150507 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Demand side management energy aggregator agent based modeling learn user preference user acceptance Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Demand side management energy aggregator agent based modeling learn user preference user acceptance Electrical engineering. Electronics. Nuclear engineering TK1-9971 Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
description	In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month.
format	article
author	Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli
author_facet	Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli
author_sort	Claudia De Vizia
title	A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
title_short	A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
title_full	A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
title_fullStr	A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
title_full_unstemmed	A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
title_sort	win-win algorithm for learning the flexibility of aggregated residential appliances
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/6505e0e70dab42aa973e8214a9f6020f
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A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances

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