| 引用本文: | 高枫,朱能.寒冷地区办公建筑负荷敏感性差异分析及应用[J].哈尔滨工业大学学报,2020,52(4):180.DOI:10.11918/201901110 |
| GAO Feng,ZHU Neng.Analysis and application of sensitivity difference of office buiding loads in cold regionsn[J].Journal of Harbin Institute of Technology,2020,52(4):180.DOI:10.11918/201901110 |
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| 摘要: |
| 为研究中国寒冷地区气候差异对办公建筑能耗表现的影响,采用Morris法分别对北京、兰州、喀什、拉萨地区的办公建筑室内环境营造负荷的影响因素展开敏感性分析,通过所得负荷分布反映各地的负荷差异,并采用敏感度和相关性描述参数对负荷影响的地域差异,进而根据相关性判定实施负荷优化. 结果表明:拉萨地区负荷水平明显低于其他地区且受辐射相关参数影响显著,其中外墙及屋顶表皮太阳辐射吸收系数的敏感度可接近相应结构的平均导热系数.敏感度排名靠前的室内负荷参数、供暖制冷设定温度,及排名靠后且热活动以导热为主的围护结构参数的最优值在各地相同；而排名居中的SHGC和WWR的最优值多存在地域差异、朝向差异及不确定性,各朝向一致的最优值仅出现在拉萨（SHGC:0.52）和喀什（WWR:0.25）.此外,相比于NSGA-Ⅱ算法（200代,10种群/代）的优化结果,基于Morris敏感性分析的负荷优化不仅在最优解上略优且总耗时可至少节省1/3.该研究显示寒冷地区局域气候差异对建筑负荷影响显著,而Morris法可在设计阶段对此差异进行解析并快速优化能耗表现. |
| 关键词: Morris敏感性分析 多目标优化 建筑负荷 寒冷地区 气候差异 |
| DOI：10.11918/201901110 |
| 分类号:TU111.195 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51338006) |
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| Analysis and application of sensitivity difference of office buiding loads in cold regionsn |
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GAO Feng1,ZHU Neng2
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(1.School of Architecture, Tianjin University, Tianjin 300072, China; 2. School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)
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| Abstract: |
| To investigate the impact of climate difference on the energy performance of office buildings in cold regions of China, Morris method was adopted to conduct a sensitivity analysis on the influence parameters of building indoor environmental construction loads in Beijing, Lanzhou, Kashgar, and Lhasa. Loads distribution was obtained to reflect the load differences among the four cities, and sensitivity and correlation were adopted to describe the regional differences in the influence of parameters on load so as to optimize the building load according to the correlation determination. Results show that the load level in Lhasa was lower than others, and was significantly affected by radiation-related parameters, where the sensitivity of solar absorptance of external wall and roof structures could be close to the average heat conductivity of corresponding structures. High sensitivity ranking parameters (internal loads and set-point temperatures) and some low ranking parameters (structure parameters dominated by heat conduction) had the same optimal value in different regions, while the optimal values of SHGC and WWR generally showed regional difference, orientation difference, and uncertainty. Only Lhasa and Kashgar had the same optimal value for SHGC (0.52) and WWR (0.25) in all orientations. In addition, compared with the optimization solution of NSGA-Ⅱ (200 generations, 10 populations/generation), the optimizagtion of building load based on Morris sensitivity analysis was not only slightly better but could save at least 1/3 of total time consumption. This study reveals that the local climate difference in cold regions has a significant impact on building loads, while Morris sensitivity analysis can analyze this difference and quickly optimize the building energy consumption performance in design stage. |
| Key words: Morris sensitivity analysis multi-objective optimization building load cold region climate difference |
