建筑用户行为节能潜力评估新方法
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摘要
现有建筑用户行为节能潜力评估方法未能充分考虑不同用户之间的差异性,从而显著降低了评估结果的准确性。针对此种不足,提出一种行为节能潜力评估的新方法。通过主成分分析对用户行为影响因素进行降维处理,在此基础上,采用数据挖掘方法中的聚类分析技术对样本用户进行合理分类,并针对不同类用户特征分别进行节能潜力评估。由于该方法既全面考虑了同一用户不同因素的影响,又充分体现了不同用户之间的差异性,可显著提高评估结果的准确性。通过将其应用于湖南省3所高校100名研究生用户电脑待机行为的节能潜力评估表明,该方法有效可行。
Existing energy-saving potential evaluation methods for building occupant behavior improvement do not take occupants' diversity into consideration.This significantly decreases the accuracy of evaluation results.To address this issue,a new energy-saving potential evaluation method has been established.First,principal component analysis is used to reduce the number of influencing factors of occupant behavior.Then,a data mining technique,clustering analysis is used to classify sample occupants into different groups. At last, energy-saving potentials are evaluated in terms of different groups characteristics.This method takes both the impact of various influencing factors and the diversity of occupants into account,and thus could improve the evaluation accuracy significantly.The method was applied to evaluate the energy-saving potential of 100 graduate students' computer standby usage behavior in 3 universities in Hunan province.The results demonstrate its effectiveness and feasibility.
Existing energy-saving potential evaluation methods for building occupant behavior improvement do not take occupants' diversity into consideration.This significantly decreases the accuracy of evaluation results.To address this issue,a new energy-saving potential evaluation method has been established.First,principal component analysis is used to reduce the number of influencing factors of occupant behavior.Then,a data mining technique,clustering analysis is used to classify sample occupants into different groups. At last, energy-saving potentials are evaluated in terms of different groups characteristics.This method takes both the impact of various influencing factors and the diversity of occupants into account,and thus could improve the evaluation accuracy significantly.The method was applied to evaluate the energy-saving potential of 100 graduate students' computer standby usage behavior in 3 universities in Hunan province.The results demonstrate its effectiveness and feasibility.
引文
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[2]HONG T Z,TAYLOR L S C,D’OCA S,et al.Advances in research and applications of energy-related occupant behavior in buildings[J].Energy&Buildings,2015,116:694-702.
[3]SAHIN M C,KOKSAL M A.Standby electricity consumption and saving potentials of Turkish households[J].Applied Energy,2014,114(2):531-538.
[4]CARMENATE T,INYIM P,PACHEKAR N,et al.Modeling occupant-building-appliance interaction for energy waste analysis[J].Procedia Engineering,2016,145:42-49.
[5]AZAR E,ANSARI H.Framework to investigate energy conservation motivation and actions of building occupants:The case of a green campus in Abu Dhabi,UAE[J].Applied Energy,2017,190:563-573.
[6]CHEN S,YANG W,YOSHINO H,et al.Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies[J].Energy&Buildings,2015,104:1-13.
[7]WANG C,YAN D,REN X.Modeling individual's light switching behavior to understand lighting energy use of office building[J].Energy Procedia,2016,88:781-787.
[8]YUN G Y,STEEMERS K.Behavioural,physical and socio-economic factors in household cooling energy consumption[J].Applied Energy,2011,88(6):2191-2200.
[9]KAMILARIS A,NEOVINO J,KONDEPUDI S,et al.A case study on the individual energy use of personal computers in an office setting and assessment of various feedback types toward energy savings[J].Energy&Buildings,2015,104(1):73-86.
[10]SMITH L I.A Tutorial on principal components analysis[J].Information Fusion,2002,51(3):219-226.
[11]韩小孩,张耀辉,孙福军,等.基于主成分分析的指标权重确定方法[J].四川兵工学报,2012,33(10):124-126.HAN X H,ZHANG Y H,SUN F J,et al.Weight determination of indicators based on principal component analysis[J].Journal of Sichuan Ordnance,2012,33(10):124-126.(in Chinese)
[12]孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008,19(1):48-61.SUN J G,LIU J,ZHAO L Y.Clustering algorithms research[J].Journal of Software,2008,19(1):48-61.(in Chinese)
[13]HAN J W,KAMBER M,PEI J.Data mining:concepts and techniques[M].3rd ed.Beijing:China Machine Press,2012.
[14]陈欢,曹承富,张存岭,等.基于主成分-聚类分析评价长期施肥对砂姜黑土肥力的影响[J].土壤学报,2014(3):609-617.CHEN H,CAO C F,ZHANG C L.et al.Princal component-cluster analysis of effects of long-term fertilization on fertility of lime concretion black soil[J]Acta Pedologica Sinica,2014(3):609-617.(in Chinese)
[15]张鹏.基于主成分分析的综合评价研究[D].南京:南京理工大学,2004.ZHANG P.Research on comprehensive evaluation based on principal component analysis[D].Nanjing:Nanjing University of Science&Technology,2004.(in Chinese)
[16]顾文,庄智,谭洪卫.基于聚类分析的酒店建筑分类与用能评价[J].建筑技术,2014,45(11):1026-1029.GU W,ZHUANG Z,TAN H W.Hotel classification badsed on cluster analysis and evaluation of energy consumption[J].Architecture Techology,2014,45(11):1026-1029.(in Chinese)
[17]曾亮,傅丽亚,罗理勇,等.不同品种和花期茶树花挥发性物质的主成分和聚类分析[J].食品科学,2015,36(16):88-93.ZENG L.FU L Y,LUO L Y,et al.Principal component and cluster analyses of volatile components in tea flowers from different cultivars at different stages of bloom[J].Food Science,2015,36(16):88-93.(in Chinese)
[18]公丽艳,孟宪军,刘乃侨,等.基于主成分与聚类分析的苹果加工品质评价[J].农业工程学报,2014,30(13):276-285.GONG L Y,MENG X J,LIU N Q,et al.Evaluation of apple quality based on principal component and hierarchical cluster analysis[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(13):276-285.(in Chinese)
[19]李郡,俞准,刘政轩,等.住宅建筑能耗基准确定及用能评价新方法[J].土木建筑与环境工程,2016,38(2):75-83.LI J,YU Z,LIU Z X,et al.A method for residential building energy benchmarking and energy use evalution[J].Journal of Civil,Architectural and Environmental Engineering,2016,38(2):75-83.(in Chinese)