地铁车站公共区域空调能耗影响因素的敏感性分析

Influential Parameters of Ventilation and Air-conditioning Energy Model for Subway Stations Using Sensitivity Analysis

随着地铁建设规模和能耗的不断增加,地铁车站通风空调系统节能逐渐成为研究热点。采用基于回归、筛选和方差的3种方法对地铁车站通风空调系统能耗的影响因素进行敏感性分析。对比发现,3种方法识别出的重要性排序前25%的参数相同但排序有差异,其中方差和筛选方法排序较一致,回归方法差异较大;回归方法的计算效率最高,方差和筛选方法计算时长分别为前者的25倍和3倍;综合考虑参数排序结果和计算效率,基于筛选的方法在该模型上的适用性较好。进而以长江流域的地下二层典型岛式地铁车站为例,分析重要参数在其可能的变化范围内改变对通风空调系统能耗的影响。结果显示,室外空气参数对能耗的影响程度高达84%;机械新风量、出入口渗风量和屏蔽门渗风量对能耗的影响分别为43%、29%和12%;设备能效对能耗的影响程度达39%;站内及隧道空气参数对能耗的影响分别为37%和33%。研究结果指出节能设计和运营需要关注的重要参数,为车站低能耗运营管理提供指导。

With the continuous expansion of subway scale and energy consumption, energy-saving ventilation and air-conditioning(VAC) systems in subway stations have gradually become a research focus. In this study, the influential parameters of the VAC energy model in subway stations are identified and ranked using three commonly used sensitivity analysis methods(regression-based, screening-based, and variance-based). The results show that the top 25% ranked parameters identified by the three methods are the same, but the orders are different. Among the three sensitivity analysis methods, the rankings of the variance-and screening-based methods are basically the same, whereas the ranking of the regression-based method is quite different from the previous two. In addition, the regression method is the most efficient method considering the computational cost, and the calculation times of the variance-and screening-based methods are 25 and 3 times, respectively. By comparison,it can be concluded that the screening-based method performs best in terms of parameter ranking and computational cost.Furthermore, the quantitative influence of the above-mentioned parameters on the energy consumed by the VAC system in a typical island subway station was studied. The results show that the impact of outdoor air parameters is as high as 84%, the effects of the mechanical fresh air volume, air infiltration volume through the entrances, and air infiltration volume through the platform screen doors are 43%, 29%, and 12%, respectively;the impact of equipment efficiency reaches 39% and is non-linear. The influence of the air parameters inside the station and the tunnel is 37% and 33%, respectively. Ultimately, the outcome of this research points out important variables that must be underlined in the energy conservation design and operation of subway stations, providing guidance for energy-saving operations and the management of stations.