[1]杨 柳,侯立强,李红莲,等.空调办公建筑能耗预测回归模型[J].西安建筑科技大学学报:自然版,2015,47(05):707-711.[doi:DOI:10.15986/j.1006-7930.2015.05.017]
 YANG Liu,HOU Liqiang,LI Honglian,et al.Regression models for energy consumption prediction inair-conditioned office building[J].J.Xi’an Univ. of Arch. & Tech.:Natural Science Edition,2015,47(05):707-711.[doi:DOI:10.15986/j.1006-7930.2015.05.017]
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空调办公建筑能耗预测回归模型()
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西安建筑科技大学学报:自然版[ISSN:1006-7930/CN:61-1295/TU]

卷:
47
期数:
2015年05期
页码:
707-711
栏目:
出版日期:
2015-10-29

文章信息/Info

Title:
Regression models for energy consumption prediction in
air-conditioned office building
文章编号:
1006-7930(2015)05-0707-05
作者:
杨 柳1侯立强1李红莲12许馨尹2刘加平1
(1. 西安建筑科技大学建筑学院,陕西 西安 710055;2. 西安建筑科技大学信息与控制工程学院,陕西 西安 710055)
Author(s):
YANG Liu1 HOU Liqiang1 LI Honglian12 XU Xinyin2 LIU Jiaping1
(1. School of Architecture, 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 710055, China)
关键词:
建筑能耗办公建筑EnergyPlus模拟敏感性分析预测回归模型
Keywords:
building energy consumption office buildings EnergyPlus simulation sensitivity analysis predictive regression model
分类号:
TU111.3
DOI:
DOI:10.15986/j.1006-7930.2015.05.017
文献标志码:
A
摘要:
建筑能耗预测模型是进行建筑节能设计及节能改造的有力工具,而建筑能耗分析是建立建筑能耗预测模型的基础.本
文建立了重庆地区的空调办公建筑模型,采用EnergyPlus软件模拟分析了该城市建筑各设计参数对暖通空调系统及建筑年总
能耗的影响,选取对建筑能耗影响较大的9项设计参数,建立了重庆地区暖通空调系统及建筑年总能耗的预测回归模型,随
机选取20组数据来评价预测回归模型的准确性.结果表明:各设计参数中窗墙比、设备功率密度、照明功率密度等对暖通空
调系统及建筑年总能耗影响较大,重庆建筑暖通空调系统及年总能耗预测回归模型R2分别为0.960和0.966,估计标准偏差都
为1.122 W/m2;能耗预测值与模拟值的最大偏差分别为-12.813%和-7.063%.
Abstract:
Building energy consumption prediction model is a powerful tool for energy-saving design and reformation, and building
energy analysis is the basis for the establishment of building energy consumption prediction model. In this paper, air-conditioned
office building models was established in Chongqing. Impact of the city building design parameters on HVAC and building energy
consumption was analyzed using the simulation tool EnergyPlus. And it established Chongqing building HVAC and annual energy
use predicted regression model using 9 design parameters which have a great impact on building energy consumption, then evaluated
the predictive accuracy of the regression model with 20 sets of data which were selected randomly. The results showed that:Window
to wall ratio, device power density, lighting power density and so on of all design parameters have greater impact on building energy
consumption; R2 of Chongqing building HVAC and annual energy predicted regression model were 0.960 and 0.966, estimated
standard deviation both were 1.122 W/m2; the maximum deviation building annual energy consumption predicted values and simulated
values were -12.813% and -7.063%.

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备注/Memo

备注/Memo:
收稿日期:2015-05-08 修改稿日期:2015-10-07
基金项目:“十二五”国家科技支撑计划课题项目(2014BAJ01B01);国家自然科学基金项目(51308435);国家杰出青年科学基金项目(51325803)
作者简介:杨柳(1970-),女,博士,教授,建筑气候与建筑节能.E-mail: yangliu@xauat.edu.cn
更新日期/Last Update: 2015-12-14