基于多目标优化的围护结构低碳设计研究——以寒冷地区办公建筑为例

Low-carbon design of envelope structure based on multi-objective optimization——Taking an office building in cold region as an example

为更好地推动低碳导向下建筑设计的发展,针对建筑围护结构的设计,以寒冷地区办公建筑为例,在保证室内热舒适性、自然采光效果以及控制围护结构建造成本的同时,降低建筑运行阶段碳排放量,探究基于多目标优化的围护结构低碳设计的流程。通过设置变量参数、选取评价指标、确定优化目标和利用寻优算法等流程,得到了优化后的帕累托前沿解集,该部分优化解集所对应的围护结构低碳设计参数可以在兼顾室内热舒适性、自然采光效果和建造成本的同时,相对于参照建筑降低建筑运行阶段碳排放量11.7%~14.0%。利用TOPSIS法对帕累托前沿解集进行了筛选,对4方面目标赋以相同的权重进行了评价,得到了综合效果最优的围护结构低碳设计参数组合,为围护结构的低碳设计提供了参考。在帕累托前沿解集中,为满足不同建筑设计需求,筛选出了对单方面目标优化效果最佳的围护结构低碳设计参数组合,可辅助设计者依据对某一目标需求的倾向进行设计决策。

In order to better promote the development of low-carbon architectural design,according to the design of the envelope structure,taking the office building in cold region as an example,in assure the indoor thermal comfort,the natural lighting effect and the control of construction cost of envelope structure,at the same time reduce the carbon emissions in the operation stage of the building,to explore the low-carbon design process of envelop structure based on multi-objective optimization.By setting variable parameters,selecting evaluation indicators,determining optimization objectives and using optimization algorithms,the optimized Pareto frontier solution set was obtained.The low-carbon design parameters corresponding to this part of the optimization solution set can not only take into account indoor thermal comfort,natural lighting effect and construction cost,but also reduce the carbon emission in the operation stage of the building by 11.7%~14.0%compared with the reference building.TOPSIS method was used to screen the Pareto front solution set,and the same weight was assigned to the four objectives to evaluate,so as to obtain the optimal combination of low-carbon design parameters of the envelope structure,which provided reference for the low-carbon design of the envelope structure.In the Pareto frontier solution set,in order to meet different architectural design requirements,the optimal combination of lowcarbon design parameters for the envelope structure with the best optimization effect was selected to assist designers to make design decisions based on the tendency of a certain goal demand.