Predicting Building Energy Consumption with a New Grey Model

Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with th...

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Autores principales:	Yan Zhang, Huiping Wang, Yi Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a5c7ff4bb6f5425a835b09d4f20cfd9e
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spelling	oai:doaj.org-article:a5c7ff4bb6f5425a835b09d4f20cfd9e2021-11-29T00:55:32ZPredicting Building Energy Consumption with a New Grey Model2314-478510.1155/2021/7873310https://doaj.org/article/a5c7ff4bb6f5425a835b09d4f20cfd9e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7873310https://doaj.org/toc/2314-4785Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, tα) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, tα) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.Yan ZhangHuiping WangYi WangHindawi LimitedarticleMathematicsQA1-939ENJournal of Mathematics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Yan Zhang Huiping Wang Yi Wang Predicting Building Energy Consumption with a New Grey Model
description	Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, tα) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, tα) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.
format	article
author	Yan Zhang Huiping Wang Yi Wang
author_facet	Yan Zhang Huiping Wang Yi Wang
author_sort	Yan Zhang
title	Predicting Building Energy Consumption with a New Grey Model
title_short	Predicting Building Energy Consumption with a New Grey Model
title_full	Predicting Building Energy Consumption with a New Grey Model
title_fullStr	Predicting Building Energy Consumption with a New Grey Model
title_full_unstemmed	Predicting Building Energy Consumption with a New Grey Model
title_sort	predicting building energy consumption with a new grey model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a5c7ff4bb6f5425a835b09d4f20cfd9e
work_keys_str_mv	AT yanzhang predictingbuildingenergyconsumptionwithanewgreymodel AT huipingwang predictingbuildingenergyconsumptionwithanewgreymodel AT yiwang predictingbuildingenergyconsumptionwithanewgreymodel
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Predicting Building Energy Consumption with a New Grey Model

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