在建筑物冷却数据的数据驱动多变量回归分析中，使用详细的占用率调查结果，对四个不同的占用率变量进行了比较。使用不同入住率变量的回归模型的结果与大型建筑的两个模拟的合成数据进行了比较，一个是VAV，另一个是CAV系统。结果表明，一个简单的线性变换将占用率水平视为与照明和设备消耗之间的差异以及该消耗的最小值成线性比例，这与使用详细、要求更高的占用率调查相当。在工作日工作时间内用1表示占用率变量，在工作日工作时间外用0表示占用率变量； 在周末相同的工作时间内为0.33，而在户外为0，结果稍差。通过将所有值除以照明和设备消耗的绝对最大值，使用照明和设备负荷得出的占用率值，或者工作日占用率为1，周末为0，都给出了比占用率调查或其他两种测试方法差得多的结果。引文:ASHRAE Transactions，第114卷，pt。2、盐湖城2008

Four different occupancy variables are compared with use of the results of a detailed occupancy survey in data driven multi-variable regression analysis of building cooling data. The results of regression models using the different occupancy variables are compared with synthetic data from two simulations of a large building, one with VAV and one with CAV systems. The results suggest that a simple linear transformation treating occupancy level as being linearly proportional to the difference between lighting and equipment consumption and the minimum value of this consumption is comparable to using a detailed, more demanding, occupancy survey. Representing the occupancy variable by a value of 1 during weekday business hours, 0 outside these hours; and 0.33 during weekends for the same business hours, and 0 outside gave somewhat poorer results. Use of an occupancy value derived from the lighting and equipment loads by dividing all values by the absolute maximum value of the lighting and equipment consumption, or use of a value of 1 for weekday occupancy and 0 for weekends both gave results that were much poorer than the occupancy survey or the other two approaches tested.