[1]孙晴,李明海,鲁娟,等.基于多元线性回归方法的高校宿舍建筑能耗分析[J].西安建筑科技大学学报(自然科学版),2018,50(06):919-924.[doi:10.15986/j.1006-7930.2018.06.024]
 SUN Qing,LI Minghai,LU Juan,et al.Energy consumption analysis of college dormitory building based on multiple linear regression method[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2018,50(06):919-924.[doi:10.15986/j.1006-7930.2018.06.024]
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基于多元线性回归方法的高校宿舍建筑能耗分析()
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西安建筑科技大学学报(自然科学版)[ISSN:1006-7930/CN:61-1295/TU]

卷:
50
期数:
2018年06期
页码:
919-924
栏目:
出版日期:
2018-12-31

文章信息/Info

Title:
Energy consumption analysis of college dormitory building based on multiple linear regression method
文章编号:
1006-7930(2018)06-0919-06
作者:
孙晴1李明海2鲁娟1刘敏3
1.西安建筑科技大学 建筑设计研究院,陕西 西安 710055 ;2. 西安建筑科技大学 信控学院,陕西 西安 710055; 3.安徽农业大学 总务处,安徽 合肥,230036
Author(s):
SUN Qing1 LI Minghai2 LU Juan1 LIU Min3
1.Institute of Architecture Design,Xi′an Univ. of Arch. & Tech., Xi′an 710055, China; 2.School of Information and Control Engineering, Xi′an Univ. of Arch. & Tech.,Xi′an 71005, China; 3.Office of General Services, Anhui Agricultural University, Hefei 230036, China
关键词:
高校宿舍多元线性回归DeST能耗分析
Keywords:
college dormitory multiple linear regression DeSTenergy consumption analysis
分类号:
TU111.19+5
DOI:
10.15986/j.1006-7930.2018.06.024
文献标志码:
A
摘要:
为了分析高校宿舍能耗的潜在因素,通过DeST软件建立六层宿舍的简易模型,模拟得到了全年的逐时温度、湿度、总辐射、建筑总能耗、照明电耗、设备电耗、给排水电耗数据,并采用SPSS 22统计软件对数据分析,建立了建筑总能耗与给排水、照明和设备电耗的多元线性回归方程.实验结果表明,该模型拟合较好,宿舍总能耗与给排水、照明和设备电耗的简单相关系数依次是0.872、0.693和0.65,与温度、湿度、总辐射的线性关系不明显,该研究为高校管理者对宿舍能耗预测与管理提供了手段.
Abstract:
In order to analyze the potential factors of college dormitory energy consumption, we build a simple model of the six layer dormitory by DeST software.Through the simulation, we obtain the hourly data of temperature,humidity,total radiation,total building energy consumption,lighting,equipment and water power consumption of the dormitory model.Then we use SPSS 22 statistical software to analyze the data and establish a multiple linear regression equation of the total building energy consumption and water,lighting and equipment power consumption.Experimental results show that the model fits better, the simple correlation coefficients between the dormitory total energy consumption and water,lighting and equipment power consumption are 0.872, 0.693 and 0.65,while the simple correlation coefficients between the dormitory total energy consumption and temperature,humidity,total radiation are not obvious.This study provides a means for university administrators to predict and manage the dormitory energy consumption.

参考文献/References:

[1]中华人民共和国国家统计局,中国能源统计年鉴[M].北京:中国统计出版社.

China Bureau of Statistics. China energy statistics yearbook[M]. Beijing: China Statistical Publishing House. 2008.
[2]何鸣.重庆某高校学生宿舍空调工况热环境研究[D].重庆:重庆大学,2014.
HE Ming. Research on air-conditioned condition Indoor thermal environment of university students’ dormitory in chongqing[D]. Chongqing: Chongqing University.2014.
[3]樊丽军.基于多元线性回归模型的建筑能耗预测与建筑节能分析[J].湘潭大学自然科学学报. 2016, 38(1): 123-126.
FAN Lijun. Prediction of building energy consumption and analysis of building energy saving based on multivariate linear regression model[J]. Journal of natural science Xiangtan university. 2016, 38(1): 123-126.
[4]王琳,肖益民.重庆市高校学生宿舍夏季热湿环境与能耗现状调查研究[J].制冷与空调, 2009, 10(23):36-41.
WANG Ling, XIAO Yimin. Study on dormitory indoor environment and energy consumption in chongqing[J]. Refrigeration and Air Conditioning. 2009, 10(23):36-41.
[5]张玲,罗多,李进,等.基于上海地区高校学生宿舍生活热水能耗现状分析及展望[J].建筑节能, 2012,40(8):31-38.
ZHANG Ling, LUO Duo, LI Jin, et al. Energy consumption of hot water system for campus dormitory in shanghai[J]. Building Energy Conservation. 2012,40(8):31-38.
[6]ZHANG Yufeng, CHEN Huimei, MENG Qinglin. Thermal comfort in buildings with split airconditioners in hothumid area of China[J].Building and Environment. 2013,64(6): 213-224.
[7]周晨,冯宇东,肖匡心, 等.基于多元线性回归模型的东北地区需水量分析[J].数学的实践与认识, 2014,44(1):118-123.
ZHOU Chen, FENG Yudong, XIAO Kuangxin, et al. Research on water requirement in northeast area based on multiple linear regression Model[J]. Mathematics IN Practice And Theory. 2014,44(1):118-123.
[8]张文彤,董伟.SPSS统计分析高级教程[M].北京:高等教育出版社,2013.
ZHANG Wentong, DONG Wei. Advanced textbook for SPSS statistical analysis[M]. Beijing. Higher Education Press,2013.
[9]骆方,刘红云,黄崑.SPSS数据统计与分析[M].北京:清华大学出版社,2011.
LUO Fang, LIU Hongyun, HANG Kun. SPSS Statistical Analysis[M]. Beijing:Tsinghua University Press. 2011.
[10]江亿.DeST用户使用手册[K].北京:清华大学建筑技术科学系,2013.
JING Yi. DeST User Manual[K]. Beijing: Department of Building Technology and Science, School of Architecture, Tsinghua University, 2013.
[11]TARDIOLI G, KERRIGAN R, OATES M, et al. Data driven approaches for prediction of building energy consumption at urban level[J]. Engery Procedia, 2015, 78:3378-3383.
[12]MATHEW P. A., DUNN L.N., SOHN M.D., et al. Big-data for building energy performance: lessons from assembling a very large national database of building energy use[J]. Appl Energy, 2015,140:85-93.
[13]WEI L, TIAN W, SILVA E.A, et al. Comparative study on machine learning for urban building energy analysis[J]. Procedia Engineering, 2015,121:285-292.
[14]杨松.建筑环境中基于既有数据和能耗模型的敏感性分析[D].天津:天津大学,2017.
YANG Song. Sensitivity analysis in building environment based on existing data and energy model[D]. Tianjin: Tianjin University.2017.

备注/Memo

备注/Memo:
收稿日期:2016-08-29修改稿日期:2018-11-19
基金项目:教育部高等学校绿色发展研究基金重点资助项目(2016-11); 基于大数据的智慧校园节能优化策略研究(2016-07).
第一作者:孙晴(1983-),男,工程师,主要从事建筑智能化方向的研究.E-mail:182180783@qq.com
通信作者:李明海(1971-),博士,教授级高级工程师,从事建筑智能化和建筑节能研究.E-mail:1119003471@qq.com
更新日期/Last Update: 2019-02-16