Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach

JOURNAL OF CLEANER PRODUCTION

卷:220

Article

Li, Xinyi¹  Yao, Runming^1,2  Yu, Wei¹  Meng, Xiangzhong¹  Liu, Meng¹  Short, Alan³  Li, Baizhan¹ 

[1] Chongqing Univ, Minist Educ, Joint Int Res Lab Green Bldg & Built Environm, Chongqing, Peoples R China

[2] Univ Reading, Sch Built Environm, Reading RG6 6DF, Berks, England

[3] Univ Cambridge, Dept Architecture, Cambridge CB2 1PX, England

关键词: Residential buildings;    Space heating and cooling;    Bottom-up engineering model;    Building stock;    Energy consumption;    Future climate;   

DOI  :  10.1016/j.jclepro.2019.02.023

来源: Elsevier

【 摘 要 】

Building stock modeling can predict stock energy consumption and carbon emissions for both current and future conditions to inform building design and retrofitting policies. A 'bottom-up' engineering approach for building stock energy modeling is attractive to built environment energy researchers because of its capacity for detailed energy analysis. However, such studies in China have been very limited to date. The aim of this research is to develop a modeling approach to residential building stock energy consumption for space heating and cooling. A holistic four-step approach of archetype configurations: building performance simulation: stock floor area estimation and local weather adjustment is presented. The Chongqing municipality was chosen to demonstrate the approach. The results show that adopting the northern China standard pattern of central space heating for Chongqing's urban residential stock is not feasible because it dramatically increases primary energy consumption and therefore carbon dioxide emissions from space heating usage. By applying energy conservation retrofit measures to the Chongqing urban residential stock, the total energy consumption for space heating and cooling and resulting carbon dioxide emissions can be significantly reduced, with estimated reductions of 57.6% -60.7% in 2020 and 553%-57.2% in 2050. The method described can provide useful information and guidance for policymakers contemplating energy retrofit schemes. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free