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Article(id=1149773879826150240, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404782, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1719331200000, receivedDateStr=2024-06-26, revisedDate=1738771200000, revisedDateStr=2025-02-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1752057054694, onlineDateStr=2025-07-09, pubDate=1746633600000, pubDateStr=2025-05-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752057054694, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752057054694, creator=13701087609, updateTime=1752057054694, updator=13701087609, issue=Issue{id=1149773869357167407, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='13', pageStart='5273', pageEnd='5704', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752057052207, creator=13701087609, updateTime=1768456769392, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559268744253990, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559268744253991, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=5579, endPage=5588, ext={EN=ArticleExt(id=1149773880316883813, articleId=1149773879826150240, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Evaluation of Energy Retrofit Plans for Existing Office Buildings in Cold Areas, columnId=1156262729917780302, journalTitle=Science Technology and Engineering, columnName=Papers·Architectural Science, runingTitle=null, highlight=null, articleAbstract=

In the practice of energy renovation of existing buildings, the uncertainty of renovation parameters has a significant impact on the renovation results. To support energy renovation decision-making, a Monte Carlo method combined with Latin hypercube sampling was proposed to evaluate different renovation schemes, and a tree-based Gaussian method was used to screen key variables that affect the renovation process. The results show that uncertainty analysis can quantitatively evaluate renovation schemes at the preliminary design stage. The energy-saving rates of two typical renovation scenarios fluctuate between 32.7%~55.2% and 55.5%~108.4%, respectively, with higher uncertainty in schemes with better energy-saving effects. The cumulative probability distribution was used to assess the probability of renovation success, with the probabilities of meeting renovation targets being 58% and 96.4%, respectively. The integration of renewable energy technologies ensures the renovation results. Sensitivity analysis results show that infiltration rate and equipment power density are the most important factors in office building energy consumption, accounting for 80% of the output variance, which provides a theoretical basis and methodological reference for the selection of more building energy renovation schemes in the future.

, correspAuthors=Li ZHU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wen-yuan WANG, Li ZHU), CN=ArticleExt(id=1149773910822056763, articleId=1149773879826150240, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=寒冷地区既有办公建筑能源改造方案评估, columnId=1156262730517565784, journalTitle=科学技术与工程, columnName=论文·建筑科学, runingTitle=null, highlight=null, articleAbstract=

既有建筑能源改造实践中,改造参数的不确定性对改造结果的影响显著。为了支持能源改造决策,提出结合拉丁超立方抽样的蒙特卡洛方法评估不同改造方案,采用树状高斯方法筛选影响改造过程的关键变量。结果表明,不确定分析可以在改造设计初级阶段量化评估改造方案,两种典型改造场景的节能率分别在32.7%~55.2%和55.5%~108.4%波动,节能效果好的改造方案不确定性高;使用累积概率分布评估改造成功的概率,满足改造目标概率分别为58%和96.4%,可再生能源技术的加入保证了改造结果;敏感性分析结果表明,渗透率和设备功率密度是办公建筑能源消耗的最重要因素,占输出方差的80%。研究结果对建筑节能改造方案的选择提供了理论依据和方法借鉴。

, correspAuthors=朱丽, authorNote=null, correspAuthorsNote=
* 朱丽(1977—),女,汉族,安徽亳州人,博士,教授,博士研究生导师。研究方向:低碳建筑设计与技术集成、低碳城镇能源规划。E-mail:。
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王文远(1988—),女,汉族,河南南阳人,博士,讲师。研究方向:建筑节能改造,绿色建筑。E-mail:。

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王文远(1988—),女,汉族,河南南阳人,博士,讲师。研究方向:建筑节能改造,绿色建筑。E-mail:。

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王文远(1988—),女,汉族,河南南阳人,博士,讲师。研究方向:建筑节能改造,绿色建筑。E-mail:。

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Tianjin: Tianjin Construction Engineering Technology Research Institute, 2014., articleTitle=null, refAbstract=null), Reference(id=1175114635226264039, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, doi=null, pmid=null, pmcid=null, year=2015, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[32], rfOrder=45, authorNames=中华人民共和国住房和城乡建设部, journalName=公共建筑节能设计标准: GB 50189—2015, refType=null, unstructuredReference=中华人民共和国住房和城乡建设部. 公共建筑节能设计标准: GB 50189—2015[S]. 北京: 中国建筑工业出版社, 2015., articleTitle=null, refAbstract=null), Reference(id=1175114635343704552, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, doi=null, pmid=null, pmcid=null, year=2019, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[32], rfOrder=46, authorNames=Ministry of Housing and Urban-Rural Development of the People's Republic of China, journalName=Design Standard for energy efficiency in public buildings: GB 50189—2015, refType=null, unstructuredReference=Ministry of Housing and Urban-Rural Development of the People's Republic of China. Design Standard for energy efficiency in public buildings: GB 50189—2015[S]. 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ArticleFig(id=1175114630788690353, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=EN, label=Table 1, caption=

Two building energy retrofit schemes

, figureFileSmall=null, figureFileBig=null, tableContent=
改造类型 改造措施 方案1 方案2
被动式改造 屋顶保温 P P
墙体保温 P P
窗户系统 P P
遮阳系统 P P
主动式改造 HAVC系统 P P
用能设备更换 P P
安装光伏板 × P
), ArticleFig(id=1175114630893547954, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=CN, label=表1, caption=

两种改造方案

, figureFileSmall=null, figureFileBig=null, tableContent=
改造类型 改造措施 方案1 方案2
被动式改造 屋顶保温 P P
墙体保温 P P
窗户系统 P P
遮阳系统 P P
主动式改造 HAVC系统 P P
用能设备更换 P P
安装光伏板 × P
), ArticleFig(id=1175114630981628339, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=EN, label=Table 2, caption=

Model certainty parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
输入参数 参数值 数据来源
人均面积/(m2·人-1) 10 《公共建筑节能标准》
(GB 50189—2015)
每人新风量/(m3·s-1) 30
供暖设定温度/℃ 20
制冷设定温度/℃ 26
), ArticleFig(id=1175114631103263156, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=CN, label=表2, caption=

模型确定性参数

, figureFileSmall=null, figureFileBig=null, tableContent=
输入参数 参数值 数据来源
人均面积/(m2·人-1) 10 《公共建筑节能标准》
(GB 50189—2015)
每人新风量/(m3·s-1) 30
供暖设定温度/℃ 20
制冷设定温度/℃ 26
), ArticleFig(id=1175114631170372021, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=EN, label=Table 3, caption=

Model uncertainty parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 改造措施 不确定参数及单位 参数取值范围 分布 数据选择依据
1 增加屋顶保温层厚度 Uroof/[W·(m2·K)-1] [0.35,0.5] 离散均匀分布 《天津市公共建筑节能设计标准》(DB-29-153—2014)[29];调研数据
2 增加墙体保温层厚度 Uwall/[W·(m2·K)-1] [0.5,0.8] 离散均匀分布
3 更换窗户类型 Uwin/[W·(m2·K)-1] [1.5,2.7] 均匀分布
4 更换遮阳系统 SC [0.4,0.55] 均匀分布
5 渗透率 Inf μ=0.5, σ = 0.17 截断正态分布
6 更换供暖系统 COP [2.6,4.1,5.8] 三角分布
7 更换制冷系统 EER [2.5,3,3.5] 三角分布
8 更换照明设备 LPD/(W· m - 2) [6,9] 均匀连续
9 更换电器设备 EPD/(W· m - 2) [6,9] 均匀连续
10 屋顶光伏覆盖率 PVcov [25%,75%] 均匀分布
11 光伏板效率 ηPV [16.5%,21.4%] 均匀分布
), ArticleFig(id=1175114631245869494, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=CN, label=表3, caption=

模型不确定参数

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 改造措施 不确定参数及单位 参数取值范围 分布 数据选择依据
1 增加屋顶保温层厚度 Uroof/[W·(m2·K)-1] [0.35,0.5] 离散均匀分布 《天津市公共建筑节能设计标准》(DB-29-153—2014)[29];调研数据
2 增加墙体保温层厚度 Uwall/[W·(m2·K)-1] [0.5,0.8] 离散均匀分布
3 更换窗户类型 Uwin/[W·(m2·K)-1] [1.5,2.7] 均匀分布
4 更换遮阳系统 SC [0.4,0.55] 均匀分布
5 渗透率 Inf μ=0.5, σ = 0.17 截断正态分布
6 更换供暖系统 COP [2.6,4.1,5.8] 三角分布
7 更换制冷系统 EER [2.5,3,3.5] 三角分布
8 更换照明设备 LPD/(W· m - 2) [6,9] 均匀连续
9 更换电器设备 EPD/(W· m - 2) [6,9] 均匀连续
10 屋顶光伏覆盖率 PVcov [25%,75%] 均匀分布
11 光伏板效率 ηPV [16.5%,21.4%] 均匀分布
), ArticleFig(id=1175114631363310007, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=EN, label=Table 4, caption=

Average, Standard Deviation, 95% Interval, 95th Percentile Values, Am and RD for Two Retrofit Schemes

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 改造方案 分布 μ/(kW·h) σ/(kW·h) 95%区间/(kW·h) Am RD/%
Ec 方案一 正态分布 24 251 5 157 (23 536.2,24 965.8) 1 429.7 41.68
方案二 正态分布 24 156 7 050 (23 178.8,25 133.2) 1 954.5 57.20
Eh 方案一 正态分布 18 554 1 921 (18 287.7,18 820.3) 532.6 20.29
方案二 正态分布 16 770 1 908 (16 505.5,17 034.5) 529.0 22.30
ET, bld 方案一 正态分布 263 516 18 105.7 (261 006.3,266 025.7) 5 019.4 13.47
方案二 正态分布 238 363 20 762.5 (235 485,241 241) 5 755.9 17.07
), ArticleFig(id=1175114631426224568, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773879826150240, language=CN, label=表4, caption=

两种改造方案的平均值、标准差、95%区间、95个百分位数、算术平均数和相对偏差

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 改造方案 分布 μ/(kW·h) σ/(kW·h) 95%区间/(kW·h) Am RD/%
Ec 方案一 正态分布 24 251 5 157 (23 536.2,24 965.8) 1 429.7 41.68
方案二 正态分布 24 156 7 050 (23 178.8,25 133.2) 1 954.5 57.20
Eh 方案一 正态分布 18 554 1 921 (18 287.7,18 820.3) 532.6 20.29
方案二 正态分布 16 770 1 908 (16 505.5,17 034.5) 529.0 22.30
ET, bld 方案一 正态分布 263 516 18 105.7 (261 006.3,266 025.7) 5 019.4 13.47
方案二 正态分布 238 363 20 762.5 (235 485,241 241) 5 755.9 17.07
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寒冷地区既有办公建筑能源改造方案评估
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王文远 1, 2 , 朱丽 3, 4, *
科学技术与工程 | 论文·建筑科学 2025,25(13): 5579-5588
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科学技术与工程 | 论文·建筑科学 2025, 25(13): 5579-5588
寒冷地区既有办公建筑能源改造方案评估
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王文远1, 2 , 朱丽3, 4, *
作者信息
  • 1 澳门城市大学创新设计学院, 澳门 999078
  • 2 河南工业职业技术学院建筑工程学院, 南阳 473000
  • 3 天津大学建筑学院, 天津 300072
  • 4 APEC可持续能源中心, 天津 300072

    • 王文远(1988—),女,汉族,河南南阳人,博士,讲师。研究方向:建筑节能改造,绿色建筑。E-mail:。

通讯作者:

* 朱丽(1977—),女,汉族,安徽亳州人,博士,教授,博士研究生导师。研究方向:低碳建筑设计与技术集成、低碳城镇能源规划。E-mail:。
Evaluation of Energy Retrofit Plans for Existing Office Buildings in Cold Areas
Wen-yuan WANG1, 2 , Li ZHU3, 4, *
Affiliations
  • 1 Faculty of Innovation and Design, City University of Macau, Macau 999078, China
  • 2 School of Architecture and Engineering, Henan Polytechnic Institute, Nanyang 473000, China
  • 3 School of Architecture, Tianjin University, Tianjin 300072, China
  • 4 APEC Sustainable Energy Center, Tianjin 300072, China
出版时间: 2025-05-08 doi: 10.12404/j.issn.1671-1815.2404782
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摘要
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既有建筑能源改造实践中,改造参数的不确定性对改造结果的影响显著。为了支持能源改造决策,提出结合拉丁超立方抽样的蒙特卡洛方法评估不同改造方案,采用树状高斯方法筛选影响改造过程的关键变量。结果表明,不确定分析可以在改造设计初级阶段量化评估改造方案,两种典型改造场景的节能率分别在32.7%~55.2%和55.5%~108.4%波动,节能效果好的改造方案不确定性高;使用累积概率分布评估改造成功的概率,满足改造目标概率分别为58%和96.4%,可再生能源技术的加入保证了改造结果;敏感性分析结果表明,渗透率和设备功率密度是办公建筑能源消耗的最重要因素,占输出方差的80%。研究结果对建筑节能改造方案的选择提供了理论依据和方法借鉴。

既有建筑  /  能源改造  /  不确定分析  /  敏感性分析

In the practice of energy renovation of existing buildings, the uncertainty of renovation parameters has a significant impact on the renovation results. To support energy renovation decision-making, a Monte Carlo method combined with Latin hypercube sampling was proposed to evaluate different renovation schemes, and a tree-based Gaussian method was used to screen key variables that affect the renovation process. The results show that uncertainty analysis can quantitatively evaluate renovation schemes at the preliminary design stage. The energy-saving rates of two typical renovation scenarios fluctuate between 32.7%~55.2% and 55.5%~108.4%, respectively, with higher uncertainty in schemes with better energy-saving effects. The cumulative probability distribution was used to assess the probability of renovation success, with the probabilities of meeting renovation targets being 58% and 96.4%, respectively. The integration of renewable energy technologies ensures the renovation results. Sensitivity analysis results show that infiltration rate and equipment power density are the most important factors in office building energy consumption, accounting for 80% of the output variance, which provides a theoretical basis and methodological reference for the selection of more building energy renovation schemes in the future.

existing building  /  energy retrofit  /  uncertainty analysis  /  sensitivity analysis
王文远, 朱丽. 寒冷地区既有办公建筑能源改造方案评估. 科学技术与工程, 2025 , 25 (13) : 5579 -5588 . DOI: 10.12404/j.issn.1671-1815.2404782
Wen-yuan WANG, Li ZHU. Evaluation of Energy Retrofit Plans for Existing Office Buildings in Cold Areas[J]. Science Technology and Engineering, 2025 , 25 (13) : 5579 -5588 . DOI: 10.12404/j.issn.1671-1815.2404782
正文
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建筑物是全球能源消耗者,在中国,建筑物消耗40%以上的最终能源,并造成了约1/3的温室气体(greenhouse gas,GHG)排放[1]。根据2022年《中国建筑节能发展研究报告》[2],中国超过60%的公共建筑库存超过30年且能源效率低下。因此,有必要对既有低能效建筑进行改造干预。
现有研究表明,提高建筑能源效率最可行的方法是能源改造[3]。对于城市中典型的公共办公建筑,它们存量庞大,建筑能耗高,因此可以采取适当改造措施,解决公共建筑脱碳[4]。但在实际改造过程中,由于缺乏知识、设计程序和某些改造参数的随机性[5],规划者和技术人员会依靠直觉和经验提出改造方案,造成改造后的能源消耗被低估[6]。建筑能源改造中第二个关键问题是改造效益的稳定性。对于建筑能源改造过程中,存在多种不确定性来源,这将导致预期改造绩效与实际绩效之间不匹配,因此,在做出改造决策时,对改造方案的节能潜力进行准确预测是非常重要的[7]。Wu等[8] 研究了在中国欠发达地区,农房改造过程存在建筑构件选择、施工质量和改造策略选择的不确定性,利用正交试验和拉丁超立方抽样,构成建筑物能源改造可能场景的任意样本,研究围护结构性能不确定性导致的改造方案不稳健。在主动能源改造技术时,暖通空调系统(heating,ventilation and conditioning,HVAC)系统运行模式的变化会影响模型预测,进而影响改造方案节能效果[9]。研究者们利用基于时间序列的运行数据和模拟建立老旧建筑能源模型,评估建筑节能潜力,确定高性能的改造措施,但无法避免改造过程的不确定性[10]。一些研究创造性地使用数学优化方法调整输入参数的效率,自动优化模拟建筑改造后的建筑性能,利用校准的建筑能源模型进行评估,结果表明,假设的占用数据降低节能措施改造的可靠性,造成节能改造的模拟性能与建筑物中测得的实际性能存在明显差异,无法评估节能措施[11]。因此,建筑进行被动或主动改造,在做出改造决策时,决策过程是一个复杂而动态的过程,只有少数案例关注改造后节能效果的研究。
事实上,改造方案实施过程中,许多改造参数具有一定程度的不确定性,这被认为改造过程的最大技术障碍[12]。现有研究中,围护结构物理参数的不确定性影响得到了很好的研究,如渗透率[13]、屋顶、地板层的厚度等[14-15],尽管施工质量和施工工艺不断改进,但是物理参数的不确定性是不可避免的[16],物理参数的不确定性范围通常在设计值的10%~30%[17],如结合物理参数和用户行为的不确定性导致住宅建筑能耗模拟存在偏差为43.5%[18]。另外,还有建筑场景参数的不确定性以及与建筑运行使用相关的参数,如占用密度、时间表和设定点温度[15],这些参数也极大地影响改造后的建筑性能。在建筑性能优化中,建筑运行和环境也带来很多不确定性,建筑运行的过程依赖于建筑内部负载,如设备功率密度、照明功率密度[19]等。丁志坤等[20]利用蒙特卡洛方法对交叉概率和变异概率进行不确定分析,建立多目标优化模型,对建筑围护结构改造参数进行分析。随着改造的深入,李翥彬等[21]提出构建既有住宅建筑宜居改造效果评价体系,但尚未考虑能源改造方案的不确定性。为此,现提出一种定量评估能源改造方案的新方法,生成改造后能源性能的概率预测,量化不确定输入对改造绩效的影响,找到稳健的建筑改造决策。
1 研究问题和方法
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利用拉丁超立方抽样的蒙特卡洛方法研究不同改造措施对能源性能的影响。如图1所示,工作流程由以下3个阶段组成:第一阶段,针对两种不同改造方案,对被动能源改造措施、主动能源改造措施,以及可再生能源的利用的输入参数进行设置和不确定采样;第二阶段,通过动态模拟计算供热、制冷和总能耗;第三阶段,分析不确定性结果,采用树状高斯方法筛选出改造方案的重要因素。
1.1 蒙特卡洛方法
基于蒙特卡罗(Monte Carlo,MC)的模拟方法是建筑能耗评估领域应用最广泛的不确定性传播方法,与其他不确定性传播方法相比,这种方法更直观且易于实现[22],它可以应用于大多数模拟环境,并处理不同类型的输入变量的概率函数。但蒙特卡罗随机抽样的收敛速度慢,函数计算次数较多,计算代价高。因此采用更高效的拉丁超立方抽样加快结果收敛。拉丁超立方抽样(Latin hypercube sampling,LHS)是建筑能量分析领域常用的抽样方法,它是一种基于近随机数序列的分层采样方法,可以产生比传统随机抽样方法更稳定的结果[23],更适用于包含多个不确定性参数的模型中,抽样样本遵守分析变量的概率分布,以相对较少的迭代次数产生收敛结果[24]。一般情况下,LHS方法的抽样数量建议取计算机实验中变量数量的10倍[25]。因此,研究采用MC方法和LHS方法相结合进行不确定性分析,它通过对输入因子使用不同的分布类型,将通过LHS抽取的改造变量与确定变量结合,生成输入参数矩阵,然后,进行大量场景分析,生成输出结果的概率分布。
1.2 不确定性分析方法
不确定分析旨在确定每种改造方案输出结果的置信区间,量化由各个不确定参数引起的不确定输出,其结果可以采用数值或图形方法表示。数值是描述性统计指标,包括均值、中位数、标准差和百分位数等,使用式(1)计算置信区间的幅度范围,使用式(2)计算95%置信区间的相对偏差[18]。
CIrangei=SLi-ILi
RDαi= s i t ( 1 - α 2 ) ( n - 1 ) x ¯ i×100%
式中:CIrangei为置信区间为95%的范围,kW·h;SLi为第i个置信度为95%分布的上限值,kW·h;ILi为第i个置信度为95%分布的下限值,kW·h;RDα i为置信度为(1-α)的第i个分布的相对偏差;si为第i个分布的标准差; t ( 1 - α 2 ) ( n - 1 )为置信度为1- α 2和自由度为n-1分布的偏差数;α=0.05; x ¯ i为第i个分布的平均值。
图形方法有助于帮助理解结果,常用的有频率分布、Q-Q图、概率密度函数(probability density function, PDF)、累积分布函数(cumulative distribution function, CDF)和箱线图。通过将正态曲线拟合到基于数据的直方图获得PDF和CDF。PDF可用于计算位于平均值周围值的百分比,对于正态分布,通常涵盖95%的值。CDF可用于计算建筑能耗
X大于或等于设定阈值x的概率。使用设计的确定值或标准化数据作为基础,给出改造后不确定性的概率分布,使用CDF展示改造结果成功的概率。
1.3 敏感性分析
建筑能源绩效评估中,通常在给出不确定性分析结果后应用敏感性分析,解释输入变量如何影响输出结果方差,即实验中最重要的参数。在办公建筑改造规划中,规划师通常需要知道不同输入因素的重要性,敏感性分析可以让用户识别改造方案中最有影响力的输入并确定优先级[23],故采用基于元模型的树状高斯过程(treed Gaussian process, TGP)。
TGP方法是将高斯过程和决策树相结合形成的一种新的元模型,它提供完全贝叶斯非平稳和非线性回归模型,以提高预测能力[15]。该方法不仅可以量化输入参数对模型输出的影响,而且所得到的敏感性指数是区间估计值,准确性和可信度更高。在TGP方法中,使用主效应指数和总效应指数作为衡量参数敏感性的两个重要指标。主效应指数反映单个输入参数对模型输出方差的独立贡献,而全效应指数表示某个输入参数及其与其他所有参数的交互作用对模型输出方差的总影响,是衡量输入参数综合影响的最全面指标,利用R平台tgp包进行计算,计算公式如下。
Si= V [ E ( Y | X i ) ] V ( Y )
Ti=1- V [ E ( Y | X ~ i ) ] V ( Y )
式中:Si为主效应;Ti为全效应;Xi为输入参数;X~i为不包括Xi的其余参数;Y为模型输出;E为期望;V为方差。
2 案例研究
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2.1 项目概况
案例建筑位于天津市,属于寒冷地区。所研究的办公楼建于20世纪90年代,就建筑围护结构和能源系统而言,建筑是现有城市办公楼存量的代表,因此,被选择为案例建筑执行能源改造不确定性的建筑性能模拟。建筑由地上5层组成,主立面为南北向,建筑面积为3 500 m2,主要由办公室、开放空间和会议室组成,简化参考模型布局如图2所示。
办公楼的外墙采用240 mm厚黏土实心砖,传热系数为1.97 W/(m2·K),屋顶采用120mm厚的钢筋混凝土砌筑,传热系数为1.86 W/(m2·K),外窗采用铝合金窗框单玻单层玻璃,传热系数为5.7 W/(m2·K),采用窗帘和百叶为主的内遮阳。该建筑所有方向的窗墙比(window-to-wall ratio,WWR)均为0.2,改造前,供暖使用市政热网集中供暖,室内为铸铁散热器,制冷机组采用的是分体空调,冷却系统能效比(energy efficiency ratio,EER)为1.8。大楼内安装有局部排风系统,排除建筑内有害气体,并通过负压渗透获得新鲜空气。办公室的内部荷载较大,工作时间照明荷载为10 W/m2,电气设备负载为14 W/m2。由于所研究办公建筑的单位面积建筑能耗不能满足《天津市公共建筑能耗标准》(J 3913—2017)[27]相关要求,因此需要对建筑进行能源改造。
2.2 建筑性能动态模拟
通过已获得确定和不确定输入参数矩阵,利用R软件快速生成不同样本的仿真模型,将不确定性输入传播到输出结果中。
2.2.1 性能指标
研究选取建筑供热能耗Eh,建筑制冷能耗Ec和建筑总能耗ET,bld作为不确定性分析(uncertainty analysis,UA)和全局敏感性分析(global sensitivity analysis,GSA)的绩效指标,由于办公建筑的能耗以电能为主,这三项指标采用《建筑节能与可再生能源利用规范》(GB 55015—2021)[28]计算,计算公式如下。
ET,bld=Eh+Ec+El+Ee
Eh= Q h η 1 q 1 q 2
Ec= Q c C O P C
式中:Eh和Ec分别为建筑全年采暖能耗和制冷能耗;El和Ee为建筑的照明能耗和设备能耗;Qh和Qc分别为年度建筑累计耗热量和耗冷量;η1为燃煤锅炉供暖系统综合效率,在寒冷地区,取0.81;q1为标准煤热值,取8.14 kW·h/kgce,kgce表示能源消耗量的单位,即千克标准煤;q2为综合发电煤耗,取0.330 kgce/ kW·h;COPC为公共建筑供冷系统综合性能系数,取3.50。
在考虑建筑节能改造效果时,选择建筑综合节能率作为评价建筑能源性能改善的综合标准[29],指标的计算标准采用中国《近零能耗建筑技术标准》(GB/T 51350—2019)[30]。
不含可再生能源发电的建筑能耗综合值计算公式为
EE= ( E h + E c + E l + E e ) f i A
含可再生能源建筑能耗综合值计算公式为
E'E=EE- E r , i f i A
ηp= E E - E R E R×100%
式中:ηp为建筑综合节能率;EE为改造后建筑能耗综合值;ER为基准建筑的建筑能耗综合值,kW·h/m2;Er,i为年本体产生的第i类可再生能源发电量;fi为第i类能源的能源换算系数,其中供冷、照明、设备系统等能源消耗形式为电力,转换因子取2.6,供热系统由于集中供热直接消耗热力,转换因子取1.22,可再生能源系统使用光伏可再生能源发电,转换因子取2.6;A为建筑面积。
2.2.2 蒙特卡罗模拟
不确定分析需要大量的模拟运行,为了获得每种改造措施改造后的建筑能耗性能,利用Energyplus软件运行建筑模型。R语言编程可直接Energyplus软件结合,快速生成Energyplus动态模拟所需要的IDF文件,大量的Energyplus模型运行结果可以使用R平台进行收集和分析,可短时间内为不确定性量化提供大量输出结果。
研究两种改造方案,第一种改造方案是以围护结构为主的被动节能改造措施和以提高暖通空调系统、照明办公系统效率为主的主动式节能改造,另外,根据现行住房和城乡建设部的强制性标准《建筑节能与可再生能源利用通用规范》(GB 55015—2021)规定,能源改造过程中,建议增加最低数量的可再生能源,为了满足这一要求,第二种改造方案考虑在办公建筑屋顶上考虑安装太阳能光伏系统,改造措施如表1所示。蒙特卡罗模拟过程中需要确定性参数和不确定性参数,其中确定性参数主要参考《天津市公共建筑节能设计标准》(DB 29-153—2014)[31]和《公共建筑节能标准》(GB 50189—2015)[32],标准符合我国公共建筑的能源使用习惯,如表2所示。
为了研究不确定的改造策略选择,基于项目组的先验知识和经验以及通过文献检索和审查知识对改造过程的不确定参数进行表征,方案一有9个不确定参数,方案二有11个不确定参数,对考虑的不确定性变量,标出设计值和变化范围的上下限,如表3所示。
3 结果分析
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3.1 模型验证
改造前,于2023年12月1日01:00—24:00进行了室内温度和建筑耗热量测试。利用Energyplus对案例建筑进行模拟,将模拟出耗热量结果与实测值进行验证,如图3所示,通过建筑能源模型模拟得到的供暖能耗与实测值相比,满足±20%误差要求。
3.2 不确定性分析
对两种改造方案的进行不确定性量化可以获得改造后能源消耗的概率分布,表4总结了两种改造方案的建筑制冷能耗、供热能耗和年总能耗的不确定结果描述性统计指标,包括平均值μ、中位数、标准差σ、95%百分位数、算术平均数(arithmetic mean,Am)和相对偏差(relative deviation,RD)。利用PDF对其进行拟合,如图4所示,以方案二中的改造后年总能耗为例,频数分布的直方图呈现正态分布形状,Q-Q图验证改造后年总能耗服从正态分布,散点几乎与直线重合,说明拟合效果很好。
如图5所示,对于两种改造方案的不确定性分析结果,对于制冷能耗,方案二的平均值为24 156 kW·h, RD为57.2%,对于供热能耗和总能耗,方案二的平均值均低于方案一,但其RD都要高于方案一,这意味着,加入可再生能源可以满足建筑能源需求,但是可再生能源的使用受到天气和环境参数的影响,因此,其改造结果的不确定性度最高。因此,在早期进行改造规划选择合适的策略可以显著减少潜在的不确定性结果,根据方案的表现,从稳健性和经济性两个角度进行充分比较和选择。
如图6所示,通过计算出两种改造方案的节能率,可以看出方案一的节能率在32.7%~55.2%波动,方案二的节能率在55.5%~108.4%。如图7所示,方案一的组间变化幅度范围为22.5%,满足改造目标的概率为58%,方案二的组内变化幅度范围为52%,满足改造目标的概率为96.4%,因此,方案二有更好的节能潜力,但不稳定度大。在方案二中,某些情况下,可再生能源份额超过了能源需求,产生正的净平衡,一方面,由于办公建筑整体规模较小,人口密度较低,能源消耗规模相对较小;另一方面,所改造的老旧办公建筑为低层建筑,当建筑面积相同时,屋顶可安装太阳能光伏板的面积会更大,产能也会更多,甚至可能会抵消建筑能耗。因此,围护结构上的传统能源改造措施必须有效地与更高效的暖通空调系统或可再生能源的开发,规划者可以通过改造后不确定性量化结果进一步评估改造方案的节能效果。
3.3 敏感性分析
基于TGP方法讨论两种改造方案的不确定变量对供热、制冷和总能耗的全局敏感性分析。在进行GSA之前,需要先分析输入变量的相关性,在输入变量不相关的条件下才能保证分析结果的准确性,经计算,不同输入变量之间的皮尔逊相关系数在-0.09~0.09,所有变量之间相关系数的绝对值小于0.1,表明输入变量不相关。
在方案一中,如图8所示,影响建筑制冷能耗的前两个重要因素是制冷系统的效率和照明功率密度,两者均与制冷能耗呈反比例关系,第三个重要因素是渗透率,它对制冷能耗的影响也是很大的,用TGP方法可以看出,这3种影响因素约占一阶效应输出方差的80%,占总效应的90%左右,而与主动改造措施相比,被动措施影响占比降低,如墙面的保温层厚度对制冷能耗的影响与前3个因素相比较小,而遮阳系数、窗户和屋顶改变对制冷能耗的影响很小。在供暖能耗中,渗透率的影响明显高于其他因素,占一阶效应和总效应输出方差的80%。影响总能耗的前3个重要因素是电器设备功率密度,渗透率和照明功率密度,其他因素的箱线图都有显著的重叠,可以说明这些因素对总能耗的影响效果可能是非常相似的。因此,在改造施工阶段提高照明和设备功率密度,对渗透质量给予足够的关注,提高房屋的气密性,可以有效提高改造效率。
在方案二中,如图9(a)所示,对制冷能耗影响的3个重要变量是制冷系统效率、渗透率和电器设备功率密度,制冷系统效率和电器设备功率密度与制冷能耗成反比,随着制冷系统效率提升和设备功率密度的增加,制冷能耗会减少。另外,具有中等敏感度的变量是照明功率密度、屋顶光伏板的覆盖率和外墙保温层厚度,从TGP分析中可以看到,这6个变量占一阶输入方差的80%以上,接近总效应的90%,屋顶隔热层厚度、窗户传热系数和遮阳系数对制冷能耗没有显著影响,因此,在改造过程中,可以减少这些因素的技术投入。在供暖能耗的影响因素分析过程中,渗透率占据主要地位。渗透率和电器设备功率密度是影响总能耗的重要因素,这两个因素与总能耗呈负相关,占一阶效应输出方差的70%,占总效应输出方差的80%,另外3个重要因素依次是照明设备功率密度光伏板效率和屋顶光伏板的覆盖率,从TGP分析结果可以看出,这5个指标之间的交互作用并不显著,这些交互作用仅产生5%的输出方差。此外,对于所有重要变量,输入和输出之间的关系近似线性,这意味着可以使用线性模型来表述输入和输出之间的关系。因此,提高施工质量来减小围护结构的渗透率,选择更高效的照明设备以及选择高效的光伏组件,会严重影响改造后的节能效果。
总体而言,对比这两个改造方案,影响供热、制冷和总能耗的不确定因素是不同的。对于方案一,供暖和制冷设备功率、渗透率以及照明电器设备功率是影响能耗的主要因素,因此,如果结合被动和主动改造技术,主动技术的改造在降低能耗占很大比例,可以选择在主动能源改造的基础上,权衡得出对围护结构的关键影响因素进行改造,在实践中是一种可行性很强的改造方案。对于方案二节能潜力布局发生明显变化,这主要将屋顶光伏系统纳入改造措施,改造的影响因素从原有的关注建筑材料、照明设备升级等转向光伏模块的效率和屋顶光伏的覆盖面积,因此,如果采取能源改造措施减少能源消耗,那么加入光伏系统进行产能是一种很好的选择。
4 结论
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(1)基于拉丁超立方抽样的蒙特卡罗方法模拟,实现改造措施样本的快速收敛,解决不确定参数对改造计划的影响。
(2)不确定性分析结果表明,方案一的离散度低,频率分布的形状更紧凑,组间变化幅度为22.5%,但改造后的能耗结果高,满足改造目标的概率为58%;方案二的改造措施中加入可再生能源后,组内变化幅度范围为52%,在制冷能耗、供暖能耗和总能耗中表现是最好的,改造后满足改造目标的概率为96.4%,建筑能源需求可由可再生能源满足,但其改造结果的不确定度增高。
(3)敏感性分析结果表明,方案一中,如果使用被动节能技术,渗透率和墙体保温层厚度是导致能源改造结果不可靠的最重要因素,如果使用主动节能技术,在制冷系统中提升冷却系统的效率,供暖期提升供暖系统的效率是最重要的不确定影响因素。对于方案二,节能潜力布局发生明显变化,这主要将屋顶光伏系统纳入改造措施,改造的影响因素从原有的关注建筑材料、照明设备升级等转向光伏模块的效率和屋顶光伏的覆盖面积,如果采取能源改造措施减少能源消耗,那么加入光伏系统进行产能是种很好的选择。因此,在改造方案制定初期,需要对更有影响力的变量采取行动,同时减少其不确定性,限制变量在改造过程中的不确定性。
总体而言,研究结论有助于建立改造决策与不确定性输出之间的相关性,对能源改造方案进行不确定性和敏感性分析,对降低改造风险是非常重要的。在改造初期,通过计划采取适当的风险缓解措施可以更好地控制影响最大的变量,从而降低能源成本,帮助决策者在节能和鲁棒性之间选择最合适的改造方案。
基金
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  • 河南省科技攻关项目基金(242102321190)
文献
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2025年第25卷第13期
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doi: 10.12404/j.issn.1671-1815.2404782
  • 接收时间：2024-06-26
  • 首发时间：2025-07-09
  • 出版时间：2025-05-08
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  • 收稿日期：2024-06-26
  • 修回日期：2025-02-06
基金
河南省科技攻关项目基金(242102321190)
作者信息
    1 澳门城市大学创新设计学院, 澳门 999078
    2 河南工业职业技术学院建筑工程学院, 南阳 473000
    3 天津大学建筑学院, 天津 300072
    4 APEC可持续能源中心, 天津 300072

通讯作者:

* 朱丽(1977—),女,汉族,安徽亳州人,博士,教授,博士研究生导师。研究方向:低碳建筑设计与技术集成、低碳城镇能源规划。E-mail:。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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