检索结果-南通市图书馆

Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China:A case study of Turpan residential buildings

引用

Building Simulation 2023年第2期16卷 279-297页

作者： Junkang Song Wanjiang Wang Pingan Ni Hanjie Zheng Zihan Zhang Yihuan Zhou College of Architectural and Civil Engineering Xinjiang University830017China College of Architecture Xi'an University of Architecture and Technology710000China College of Architecture and Environment Sichuan University610065China

At present,buildings in arid and hot regions are facing severe challenges of indoor comfort improvement and carbon emission reduction,especially in rural ***-objective optimization could be an effective tool for tackling the aforementioned ***,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 ***,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 *** accuracy index of the modified model is R2>0.99,cv(RMSE)≤1%,and NMBE≤0.2%,which gets rid of the dilemma of low prediction accuracy of traditional machine learning *** 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 ***,sensitivity analysis is conducted on the variables,which supports guidance for practitioners to carry out the low-carbon ***,the multi-objective optimization analysis for a farmhouse in Turpan is taken as a case study to evaluate the performance of 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.

关键词： life cycle assessment arid regions deep neural networks entropy-based topsis method multi-criteria optimization

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