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寒冷地区既有高校宿舍被动式节能潜力分析——以南开大学八里台为例

Analysis of Passive Energy-saving Potential of Existing University Dormitories in Cold Areas:Taking Nankai University Balitai Campus as an Exmple
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摘要 文章以南开大学八里台校区宿舍为例,探索在低碳背景下寒冷地区既有高校宿舍建筑被动节能改造的潜力,通过实地调研,建立基准模拟,并利用Grasshopper软件搭建参数化平台对基准模型外围护结构热工性能的被动式节能因素进行模拟分析,设计正交试验,模拟不同试验的建筑能耗,通过SPSS分析影响能耗的参数与能耗间的关系,从而确定每个参数对建筑能耗的影响程度,得出不同因素对于建筑能耗的敏感性。结果表明:外墙、屋顶、外窗等部位传热系数与采暖、制冷能耗均呈线性关系;节能优先级依次为:外墙、外窗、屋顶;仅通过改变外围护结构热工性能,最大节能潜力为61.9%,若想实现零能耗,需要使用高能效的主动式设备和最大化利用可再生能源。 Taking the dormitory of Nankai University Balitai Campus as an example,the potential of passive energy-saving renovation of existing university dormitory buildings in cold areas under the background of low carbon was explored,and through field research,benchmark simulation was established,and Grasshopper software was used to build a parametric platform to simulate and analyze the passive energy-saving factors of the thermal performance of the outer envelope of the benchmark model,designed orthogonal tests,simulated building energy consumption in different tests,and analyzed the relationship between parameters affecting energy consumption and energy consumption through SPSS.Therefore,the degree of influence of each parameter on building energy consumption is determined,and the sensitivity of different factors to building energy consumption is obtained.The results show that the heat transfer coefficient of exterior walls,roofs,exterior windows and other parts have a linear relationship with heating and cooling energy consumption.The priority of energy conservation is:exterior wall,exterior window,roof;with a maximum energy savings potential of 61.9%just by changing the thermal performance of the envelope,achieving zero energy consumption requires energy-efficient active equipment and maximizing the use of renewable energy.
出处 《建筑与文化》 2024年第10期56-58,共3页 Architecture & Culture
基金 河北省重点研发计划项目(基金项目:21374502D) 天津市重点研发计划项目(基金项目:18YFZCSF00630)。
关键词 高校宿舍建筑 参数化设计 敏感性分析 节能潜力 线性回归 university dormitory buildings parametric design sensitivity analysis potential for energy savings linear regression
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