基于多维度聚类算法的重庆住宅空调使用特征分析

长江流域夏季炎热、冬季阴冷，全年高湿，室内热环境恶劣，多样化的空调使用习惯对住宅供暖空调能耗有重要影响。大数据技术发展为更大样本、更高精度、更多维度的空调行为监测提供了基础，弥补了现有研究方法误差大和分类指标单一的不足。选取重庆市作为长江流域典型城市的代表，随机抽取2 000台住宅房间空调器样本，从空调使用时长、温度需求及能耗角度，构建空调运行的5个特征参数，采用多维度聚类算法识别出重庆地区空调使用习惯的典型类别，通过深入分析不同使用习惯类别的特征差异，总结出三类典型群体。

Characteristics of occupants' behavior in Chongqing residential air-conditioning based on multi-dimensional clustering algorithm

With a hot summer, cold winter and high humidity climate, residential energy consumption in the Yangtze River Basin is strongly affected by diverse air-conditioning behaviors in such a harsh indoor thermal environment. The development of big data technology provides a basis for larger samples, higher accuracy, and more dimensions of air-conditioning behavior monitoring, which can make up for the current situation of large errors in existing research methods and single classification indicators. By selecting 2 000 samples of residential room air conditioners (RACs) in Chongqing as the representative city: First, five characteristic parameters of air-conditioning operation are constructed from the perspective of air-conditioning using period, temperature demand and energy consumption; Then, a multi-dimensional clustering algorithm was used to identify the typical categories of air-conditioning behavior;Finally,through in-depth analysis of the characteristic differences among the clustering results, three typical air-conditioning behavior groups are summarized for residential buildings in Chongqing.