Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sus...

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Autores principales:	Zhuoqun Zhao, Xuchao Yang, Han Yan, Yiyi Huang, Guoqin Zhang, Tao Lin, Hong Ye
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	downscaling Cubist fine scale building energy consumption carbon emissions partial least squares regression Science Q
Acceso en línea:	https://doaj.org/article/cf3995f6bf4e45a3aa4ab2ef91126a5a
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spelling	oai:doaj.org-article:cf3995f6bf4e45a3aa4ab2ef91126a5a2021-11-11T18:54:24ZDownscaling Building Energy Consumption Carbon Emissions by Machine Learning10.3390/rs132143462072-4292https://doaj.org/article/cf3995f6bf4e45a3aa4ab2ef91126a5a2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4346https://doaj.org/toc/2072-4292The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km<sup>2</sup> spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km<sup>2</sup> BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO<sub>2</sub> emission peak and carbon neutralization.Zhuoqun ZhaoXuchao YangHan YanYiyi HuangGuoqin ZhangTao LinHong YeMDPI AGarticledownscalingCubistfine scalebuilding energy consumptioncarbon emissionspartial least squares regressionScienceQENRemote Sensing, Vol 13, Iss 4346, p 4346 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	downscaling Cubist fine scale building energy consumption carbon emissions partial least squares regression Science Q
spellingShingle	downscaling Cubist fine scale building energy consumption carbon emissions partial least squares regression Science Q Zhuoqun Zhao Xuchao Yang Han Yan Yiyi Huang Guoqin Zhang Tao Lin Hong Ye Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
description	The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km<sup>2</sup> spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km<sup>2</sup> BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO<sub>2</sub> emission peak and carbon neutralization.
format	article
author	Zhuoqun Zhao Xuchao Yang Han Yan Yiyi Huang Guoqin Zhang Tao Lin Hong Ye
author_facet	Zhuoqun Zhao Xuchao Yang Han Yan Yiyi Huang Guoqin Zhang Tao Lin Hong Ye
author_sort	Zhuoqun Zhao
title	Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
title_short	Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
title_full	Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
title_fullStr	Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
title_full_unstemmed	Downscaling Building Energy Consumption Carbon Emissions by Machine Learning
title_sort	downscaling building energy consumption carbon emissions by machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cf3995f6bf4e45a3aa4ab2ef91126a5a
work_keys_str_mv	AT zhuoqunzhao downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT xuchaoyang downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT hanyan downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT yiyihuang downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT guoqinzhang downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT taolin downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning AT hongye downscalingbuildingenergyconsumptioncarbonemissionsbymachinelearning
_version_	1718431671435395072

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

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