摘要
近年来,高校宿舍建筑不断增多,然而高校宿舍能耗管理却没有得到改善,使得能源浪费严重。分析高校宿舍的能耗现状,提出合适的能耗评价指标,是改善高校宿舍能源浪费问题的关键所在。以湘潭某高校的宿舍楼为例,以日单位面积能耗和日人均能耗作为基础能耗评价指标,对学年各季节的综合能耗、分项能耗分别进行评价,找出各栋宿舍的节能潜力。同时运用SPSS软件以各项能耗影响因素作为自变量,以日人均平均能耗作为因变量进行多元回归分析,结果表明人均居住面积在湘潭地区高校宿舍能耗评价中是不可忽视的影响因素,能耗评价中需进行修正。该结论可有效指导湘潭地区宿舍节能工作的开展,同时也可为其他地区高校宿舍节能工作的开展提供理论基础。
In recent years,the number of college dormitory buildings is increasing.However,efficient dormitory energy consumption management has not been improved,which makes energy waste seriously.Analyzing the energy consumption of dormitory and putting forward the appropriate energy consumption evaluation index are the key to improve the energy waste of dormitory.The dormitory of a university in Xiangtan is analyzed,the daily energy consumption per unit area and daily energy consumption per capita are considered as the basic energy consumption evaluation index,aiming to find out the energy-saving potential of each dormitory by evaluating the comprehensive energy consumption and itemized energy consumption in each season of the academic year.Meanwhile,SPSS software is used to conduct multiple regression analysis with various energy consumption influencing factors as independent variables and daily average energy consumption per capita as dependent variables.The results show that per capita living area is an influencing factor that cannot be ignored in the energy consumption evaluation of college dormitories in Xiangtan,and it needs to be corrected in the energy consumption evaluation.The development of dormitory energy conservation can be guided effectively from this conclusion locally,and also a theoretical basis could be provided for the development of university dormitory energy conservation in other areas.
作者
李小华
方清清
王平
LI Xiao-hua;FANG Qing-qing;WANG Ping(Department of Building Engineering,Hunan Institute of Engineering,Xiangtan 411104,Hunan,China)
出处
《建筑节能(中英文)》
CAS
2022年第12期53-58,共6页
Building Energy Efficiency
关键词
高校宿舍
能耗评价
节能潜力
多元回归分析
college dormitory
energy consumption evaluation
energy saving potential
multiple regression analysis