干旱区生态环境知识资源中心(Arid): Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings

Arid

DOI	10.1007/s12273-022-0941-9
	Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings
	Song, Junkang; Wang, Wanjiang; Ni, Pingan; Zheng, Hanjie; Zhang, Zihan; Zhou, Yihuan
通讯作者	Wang, WJ
来源期刊	BUILDING SIMULATION
ISSN	1996-3599
EISSN	1996-8744
出版年	2023
卷号	16 期号:2 页码:279-297
英文摘要	At present, buildings in arid and hot regions are facing severe challenges of indoor comfort improvement and carbon emission reduction, especially in rural areas. Multi-objective optimization could be an effective tool for tackling the aforementioned challenges. Therefore, this paper proposes a life-cycle optimization framework considering thermal comfort, which is beneficial to promoting residents' motivation for low-carbon retrofit in arid climate regions. First, in response to the above problems, three objective functions are specified in the framework, which are global warming potential (GWP), life cycle cost (LCC), and thermal discomfort hours (TDH). To improve the optimization efficiency, this research uses Deep Neural Networks (DNN) combined with NSGA-II to construct a high-precision prediction model (meta-model for optimization) based on the energy consumption simulation database formed by the orthogonal multi-dimensional design parameters. The accuracy index of the modified model is R-2 > 0.99, cv(RMSE) <= 1%, and NMBE <= 0.2%, which gets rid of the dilemma of low prediction accuracy of traditional machine learning models. In the scheme comparison and selection stage, the TOPSIS based on two empowerment methods is applied to meet different design tendencies, where the entropy-based method can avoid the interference of subjective preference and significantly improve the objectivity and scientific nature of decision analysis. Additionally, sensitivity analysis is conducted on the variables, which supports guidance for practitioners to carry out the low-carbon design. Finally, the multi-objective optimization analysis for a farmhouse in Turpan is taken as a case study to evaluate the performance of the framework. The results show that the framework could significantly improve the building performance, with 60.8%, 52.5%, and 14.2% reduction in GWP, LCC, and TDH, respectively.
英文关键词	life cycle assessment arid regions deep neural networks entropy-based TOPSIS method multi-criteria optimization
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000862224600003
WOS关键词	SENSITIVITY-ANALYSIS ; DESIGN ; OPTIMIZATION ; SIMULATION ; VARIANCE ; CLIMATE
WOS类目	Thermodynamics ; Construction & Building Technology
WOS研究方向	Thermodynamics ; Construction & Building Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395621
推荐引用方式 GB/T 7714	Song, Junkang,Wang, Wanjiang,Ni, Pingan,et al. Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings[J],2023,16(2):279-297.
APA	Song, Junkang,Wang, Wanjiang,Ni, Pingan,Zheng, Hanjie,Zhang, Zihan,&Zhou, Yihuan.(2023).Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings.BUILDING SIMULATION,16(2),279-297.
MLA	Song, Junkang,et al."Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings".BUILDING SIMULATION 16.2(2023):279-297.