Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

Remote Sensing

Zhuoqun Zhao¹  Guoqin Zhang¹  Yiyi Huang¹  Hong Ye¹  Han Yan¹  Tao Lin¹  Xuchao Yang² 

[1]Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

[2]Ocean College, Zhejiang University, Zhoushan 316021, China

关键词: downscaling;    Cubist;    fine scale;    building energy consumption;    carbon emissions;    partial least squares regression;   

DOI  :  10.3390/rs13214346

来源: DOAJ

【 摘 要 】

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 km² 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 km² 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 CO₂ emission peak and carbon neutralization.

【 授权许可】

Unknown