摘要
以北京某高校学生宿舍热时开空调的行为为例,基于建筑人行为描述的动作模型,针对作为热时开空调行为驱动力的室内温度,开展驱动数据分组方式的探讨研究。在房间室内温湿度、人员在室情况及空调运行情况现场测试和问卷调查的基础上,分析指出传统的以.5℃或.0℃作为温度区间分隔点的数据分组方式有可能会导致不合理的结果,说明驱动数据合理分组的关键在于保证各个温度区间样本数量、进而空调行为概率的准确估计。对此,应用统计学抽样分布理论,计算获得能够可靠估计总体比例分布、也即空调行为概率所需的样本数量,再结合测试仪器精度,从温度区间间隔大小确定和温度区间中间点定位两个层面,分析确定出建筑空调行为分析描述中有关驱动数据合理分组的方法,并进一步结合实测案例,分析说明本研究所讨论驱动数据分组方法的可靠性和有效性。
Taking one kind of behavior of turning on air conditioner when it is hot in one dormitory room in Beijing as an illustration,data grouping of indoor temperature as driving forces of air conditioning behavior is discussed by means of the action model of occupant behavior.According to the field measurements of indoor temperature and humidity,air conditioning usage pattern and occupants’stay indoors,the effects of traditional data grouping methods taking.5℃or.0℃as dividing points of temperature interval on the fitting results of the probability curve of air conditioning behavior are analyzed and compared.It is pointed out that the key is to ensure enough numbers of samples in each temperature interval.In this regard,the statistical sampling distribution method is introduced to identify the number of samples needed to ensure the reliable estimation of the overall proportional distribution,i.e.,the opening probability of air conditioning.On this basis,and considering the precision of the test instruments together,the quantitative criteria for driving data grouping of indoor temperatures are given from the two aspects of temperature interval size and the middle point location of temperature interval.Moreover,the reliability of the method are illustrated by the case of student dormitory in this study.
作者
简毅文
高萌
田园泉
JIAN Yiwen;GAO Meng;TIAN Yuanquan(Beijing University of Technology,Beijing 100022,China)
出处
《建筑科学》
CSCD
北大核心
2019年第2期78-85,共8页
Building Science
关键词
空调行为
驱动力
数据分组
区间间隔
区间中间点
抽样分布
air conditioning behavior
driving force
data grouping
interval size
middle point
sampling distribution
