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校园建筑能耗巨大,且因其用能管理粗放,建筑用能行为存在很大浪费。然而,校园建筑内使用者用能行为特征及其对节能的量化研究却非常缺乏。本项目在某高校内选取了宿舍建筑进行调研,分析了宿舍内照明、空调、电脑、热水器的使用特征。建立了冬夏两季单人日均用电的简化计算模型,并利用K-均值聚类分析,分析了奢侈型、适度性、节约型等3种类型的用能行为特征。调研结果表明:宿舍内照明和热水器的使用比较节俭;空调的逐时使用比例也较小,但空调温度设定及空调温度控制方式欠科学;通过减少学生离开时的电脑开机和待机比例,在一定程度上可降低电耗。聚类分析表明,奢侈型样本在冬夏两季个人日均用电量为6.81 k W·h/(人·d),适度型样本为3.56 k W·h/(人·d),节约型样本为1.16 k W·h/(人·d)。三类用能行为样本在冬夏季空调的使用时间上差别很大,可见空调的使用时长对用能行为聚类有重要贡献,而照明、电脑、热水器的使用对聚类的影响不大。高校建筑用能行为特征分析可为绿色校园建设提供基础数据支持。
Campus building energy consumption is huge, and because of its extensive use of energy management, there is a big waste of building energy behavior. However, the user behavior characteristics of buildings in campus buildings and their quantitative research on energy saving are very scarce. The project selected a dormitory building in a university to conduct research, analysis of the dormitory lighting, air conditioning, computers, water heaters use characteristics. A simplified calculation model of average daily electricity consumption per person in winter and summer is established, and K-means clustering analysis is used to analyze the energy use behavior characteristics of luxury, moderation and economy. The results show that: dormitory lighting and water heaters use more frugal; hourly use of air conditioning ratio is also small, but the air conditioning temperature setting and air conditioning temperature control less scientific; by leaving students to leave the computer boot and standby ratio, in the To some extent, reduce power consumption. Cluster analysis showed that the daily average daily electricity consumption of luxury samples in winter and summer was 6.81 k W · h / (person · d) and that of moderate samples was 3.56 k W · h / (person · d). Economical samples 1.16 k W · h / (person · d). The three types of behavioral samples vary greatly in the use time of air conditioners in winter and summer. It can be seen that the use duration of air conditioners has an important contribution to energy behavior clustering. However, the use of lighting, computers and water heaters has little effect on the clustering. The analysis of building energy behavior behavior in colleges and universities can provide basic data support for green campus construction.