基于多维度聚类算法的重庆住宅空调使用特征分析

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

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2000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。

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 2000 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.