摘要
为揭示当代寒区大型商场多中庭形态对碳排放的影响机理,以哈尔滨某集中式中庭的商场为例,采用BP神经网络预测结合敏感性分析和相关性分析的方法,探究中庭数量(n)、主次中庭面积比(r)、主次中庭距离(d_(0))、天窗面积占比(S_(0))、中庭面积占比(S_(1))五个要素对碳排放的影响机制。研究结果表明,S_(0)、S_(1)、r、d_(0)、n对碳排放的影响程度依次递减,其中S_(0)、S_(1)、r、d_(0)为关键影响要素,且均与总碳排放呈正向关系,适当减小中庭面积占比和天窗面积占比、增加中庭聚集度和面积均匀度有利于建筑减碳。
A shopping mall with centralized atrium in Harbin is taken to study the influence mechanism of multiple atrium forms on carbon emissions of large shopping malls in cold areas,in which BP neural network prediction combined with sensitivity analysis and correlation analysis are adopted.The influence mechanism of five factors on carbon emission is explored,including number of atriums(n),area ratio of primary and secondary atriums(r),distance between primary and secondary atriums(d_(0)),skylight area ratio(S_(0)),and area ratio of atrium(S_(1)).The results show that the influence of S_(0),S_(1),r,d_(0) and n on carbon emission decreases successively,in which S_(0),S_(1),r and d_(0) are the key influencing factors,and all of them are positively correlated with total carbon emission.It is conducive to building carbon reduction by reducing the proportion of atrium area and skylight area and by increasing the concentration and uniformity of atrium area.
作者
安雪男
李铁军
史小蕾
高枫
AN Xuenan;LI Tiejun;SHI Xiaolei;GAO Feng(School of Architecture,Harbin Institute of Technology,Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology,Ministry of Industry and Information Technology,Harbin 150006,China;Architecture Design and Research Institute of HIT,Harbin 150090,China;Research Center for Architecture and Environment in Cold Land of HITAD,Harbin 150001,China;Harbin University of Science and Technology Institute of Civil Engineering and Architecture,Harbin 150086,China)
出处
《低温建筑技术》
2024年第2期5-10,共6页
Low Temperature Architecture Technology
基金
黑龙江省重点研发计划项目(2022ZX01A33)
黑龙江省博士后科学基金项目(LBH-Z22190)。
