Uncertainty modeling of household appliance loads for smart energy management

Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplifi...

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Autores principales:	Yu Wang, Yu Fu, Haiyang Lin, Qie Sun, Jean-Louis Scartezzini, Ronald Wennersten
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Uncertainty Occupant behaviors Smart energy management Agent-based modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6b197be7b5e1463583c45747d4a191d2
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spelling	oai:doaj.org-article:6b197be7b5e1463583c45747d4a191d22021-12-04T04:34:59ZUncertainty modeling of household appliance loads for smart energy management2352-484710.1016/j.egyr.2021.11.097https://doaj.org/article/6b197be7b5e1463583c45747d4a191d22022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721012427https://doaj.org/toc/2352-4847Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.Yu WangYu FuHaiyang LinQie SunJean-Louis ScartezziniRonald WennerstenElsevierarticleUncertaintyOccupant behaviorsSmart energy managementAgent-based modelingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 232-237 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Uncertainty Occupant behaviors Smart energy management Agent-based modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Uncertainty Occupant behaviors Smart energy management Agent-based modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yu Wang Yu Fu Haiyang Lin Qie Sun Jean-Louis Scartezzini Ronald Wennersten Uncertainty modeling of household appliance loads for smart energy management
description	Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.
format	article
author	Yu Wang Yu Fu Haiyang Lin Qie Sun Jean-Louis Scartezzini Ronald Wennersten
author_facet	Yu Wang Yu Fu Haiyang Lin Qie Sun Jean-Louis Scartezzini Ronald Wennersten
author_sort	Yu Wang
title	Uncertainty modeling of household appliance loads for smart energy management
title_short	Uncertainty modeling of household appliance loads for smart energy management
title_full	Uncertainty modeling of household appliance loads for smart energy management
title_fullStr	Uncertainty modeling of household appliance loads for smart energy management
title_full_unstemmed	Uncertainty modeling of household appliance loads for smart energy management
title_sort	uncertainty modeling of household appliance loads for smart energy management
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/6b197be7b5e1463583c45747d4a191d2
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_version_	1718372972499042304

Uncertainty modeling of household appliance loads for smart energy management

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