办公建筑中人员用能行为的分类模式

Pattern classification of energy usage behaviors in an office building

针对人员用能行为的差异性,对天津某办公建筑内不同人员对空调和照明设备的使用习惯展开了问卷调查及现场监测。结果显示,人员对于开启空调和照明的阈值不同,接受的温度和照度范围不同。利用k-means聚类算法将建筑内人员分为敏感型、中等型和不敏感型3类,并利用威布尔三参数模型分别对每类人员进行建模,最终得到设备开启的概率时刻表,以此作为模拟工具的输入,可将建筑能耗模拟的精度提高20%以上。此外,对环境敏感度不同人员的空调和照明设备开启概率的差异性较大,因此在建立人员行为模型之前对人员进行分类十分必要。

For the view of the different occupants’ energy usage behavior, conducts questionnaire survey and on-site monitoring on the usage habits of air conditioning and lighting equipment in an office building in Tianjin. The results show that different thresholds of occupants to turn on air conditioning and lighting lead to diverse accepted temperature ranges and illuminance levels. By k-means clustering algorithm, the occupants can be divided into three types of sensitive type, common type and insensitive type. Uses Weibull models to classify each type of occupants. Obtains the probability schedule of equipment opening as the inputs of the building simulation, which improves the accuracy of building energy consumption simulation by more than 20%. In addition, occupants with different environment sensitivity have a large difference in the opening probability of air conditioning and lighting equipment, so it is necessary to classify occupants before the establishment of human behavior model.