The control strategy of heating, ventilating, and air-conditioning (HVAC) systems is cost-effective to achieve energy saving in buildings. It is believed that identifying representative behavioral patterns of end-users in multi-zone buildings helps personalize the control parameters of HVAC systems. Thus, this will result in energy saving while minimizing thermal discomfort of end-users. With advanced metering technologies, it is possible to capture how end-users consume energy in their rooms and then categorize the rooms into several meaningful groups based on similar energy use patterns. Unfortunately, it is still unknown how changes in numerical values and dimension of energy use data affect the performance of end-user group categorization. Therefore, this research examines the performance of end-user group categorization across different types of numerical values and dimensions of energy use data. A clustering analysis is conducted using energy use data from 959 rooms of seven dormitory buildings in Seoul, South Korea. The clustering results show that reducing the dimension of energy use data (i.e., data reduction) improves the similarity of end-users within a group. Also, transforming the numerical values of energy use data (i.e., data transformation) makes the group similarity higher. Lastly, when combining both data reduction and transformation during the categorization process, the best clustering method is dependent on the distribution of energy use data. These results indicate that facility managers can provide end-users with thermally comfortable conditions and achieve energy saving across all zones.

供暖，通风和空调（HVAC）系统的控制策略具有成本效益，可实现建筑物的节能。据信，识别多区域建筑物中的最终用户的代表性行为模式有助于个性化HVAC系统的控制参数。因此，这将节省能源，同时将最终用户的热不适降至最低。利用先进的计量技术，可以捕获最终用户如何在其房间中消耗能量，然后根据相似的能源使用模式将房间分为几个有意义的组。不幸的是，仍然未知的是，数值和能源使用数据的尺寸变化如何影响最终用户组分类的性能。因此，这项研究研究了最终用户组分类在不同类型的数值和能源使用数据维度上的表现。使用来自韩国首尔的七个宿舍楼的959个房间的能源使用数据进行聚类分析。聚类结果表明，减少能源使用数据的维度（即数据减少）可以提高组内最终用户的相似性。此外，转换能源使用数据的数值（即数据转换）会使组相似度更高。最后，当在分类过程中同时结合数据缩减和转换时，最佳的聚类方法取决于能源使用数据的分布。