建筑用户行为数据中的知识发现
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摘要
建成环境中的用户行为对建筑的实际使用效能具有决定性的影响,是建筑全生命周期能耗中除建筑材料、设备性能等客观条件之外最重要的主观影响因素。然而,对用户行为的研究长期受制于观测手段和分析方法的缺失,并未达到其应有的研究深度。随着用户行为监测技术及数据挖掘算法的日趋成熟,大量的观测数据及其处理方法为研究者提供了将跨学科技术应用于建筑设计领域的可能性。本文结合现有案例及作者自身实践,在用户行为的数据采集、数据处理及知识发现三个方面梳理了建筑用户行为研究的主要流程与关键技术。
Indoor occupant behavior in the built environment, which has a decisive influence on the actual energy efficiency, is the most important subjective influencing factor besides objective conditions such as construction materials and equipment performance in the whole life cycle energy consumption of the building. However, the study of occupant behavior has always been restricted due to the lack of both observation and data analysis technology for a long time. With the advance of behavior monitoring technology and data mining algorithms, vast number of observation data and its processing methods provide researchers with the possibility to apply interdisciplinary technologies in the field of architecture design. This paper makes an overall introduction of the three main steps and key methods of data collection, data processing and knowledge discovery of user behavior, by providing typical cases and the author's own practice.
Indoor occupant behavior in the built environment, which has a decisive influence on the actual energy efficiency, is the most important subjective influencing factor besides objective conditions such as construction materials and equipment performance in the whole life cycle energy consumption of the building. However, the study of occupant behavior has always been restricted due to the lack of both observation and data analysis technology for a long time. With the advance of behavior monitoring technology and data mining algorithms, vast number of observation data and its processing methods provide researchers with the possibility to apply interdisciplinary technologies in the field of architecture design. This paper makes an overall introduction of the three main steps and key methods of data collection, data processing and knowledge discovery of user behavior, by providing typical cases and the author's own practice.
引文
[1]清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2015[R].北京:中国建筑工业出版社,2015.
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[3]王闯.有关建筑用能的人行为模拟研究[D].北京:清华大学,2014.
[4]李哲.中国住宅中人的用能行为与能耗关系的调查与研究[D].北京:清华大学,2012.
[5]李楠.夏热冬冷地区人员行为对住宅建筑能耗的影响研究[D].重庆:重庆大学,2011.
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[8]BRIEN W O, KAPSIS K, ATHIENITIS A K. Manually-operated window shade patterns in office buildings:a critical review[J]. Building and Environment, 2013, 60(2):319-338.
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[11]YANG Rui, WANG Lingfeng. Development of multi-agent system for building energy and comfort management based on occupant behaviors[J]. Energy and Buildings, 2013,56:1-7.
[12]HONG Tianzhen, OCA S D, TURNER W J N, et al. An ontology to represent energy-related occupant behavior in buildings. Part I:Introduction to the DNAs framework[J]. Building and Environment, 2015,92:764-777.
[13]HONG Tianzhen, OCA S D, TAYLOR-LANGE S C, et al. An ontology to represent energy-related occupant behavior in buildings. Part II:Implementation of the DNAS framework using an XML schema[J].Building and Environment, 2015, 94:196-205.
[14]张运楚,韩怀宝,曹建荣.建筑能耗管理与室内空间感知研究进展[J].山东建筑大学学报,2016,31(6):614-621.
[15]IDOUDI M, ELKHORCHANI H, GRAYAA K. Performance evaluation of wireless sensor networks based on zigbee technology in smart home[C]//Anon. Electrical Engineering and Software Applications(ICEESA)2013. New Jersey:IEEE, 2013:1-4.
[16]黄蔚欣.基于室内定位系统(IPS)大数据的环境行为分析初探——以万科松花湖度假区为例[J].世界建筑,2016(4):126-128.
[17]SILVA B, PANG Z,?KERBERG J, et al. Experimental study of UWBbased high precision localization for industrial applications[C]//Anon. 2014 IEEE international conference on ultra-wideband(icuwb).New Jersey:IEEE, 2014:280-285.
[18]ZETIK R, GUOWEI S, THOM?R S. Evaluation of requirements for UWB localization systems in home-entertainment applications[C]//Anon. 2010 International conference on indoor positioning and indoor navigation. New Jersey:IEEE, 2010:1-8.
[19]LI Li, LI Xin, LU Zhihan, et al. Sequential behavior pattern discovery with frequent episode mining and wireless sensor network[J]. IEEE Communications Magazine, 2017, 55(6):205-211.
[20]DONG Jia, LI Li, HUANG Jiaxuan, et al. Isovist based analysis of supermarket layout:verification of visibility graph analysis and multiagent simulation[C]//HEITOR T. Proceedings of the 11th space syntax symposium. Lisboa:Tecnico Lisboa, 2017:155-166.
[2]MASOSO O T, GROBLER L J. The dark side of occupants'behaviour on building energy use[J]. Energy and Buildings, 2010, 42(2):173-177.
[3]王闯.有关建筑用能的人行为模拟研究[D].北京:清华大学,2014.
[4]李哲.中国住宅中人的用能行为与能耗关系的调查与研究[D].北京:清华大学,2012.
[5]李楠.夏热冬冷地区人员行为对住宅建筑能耗的影响研究[D].重庆:重庆大学,2011.
[6]OCA S D, HONG Tianzhen. Occupancy schedules learning process through a data mining framework[J]. Energy and Buildings, 2015, 88:395-408.
[7] SHEN Wei, JONES R, WILDE P D. Driving factors for occupant-controlled space heating in residential buildings[J]. Energy and Buildings,2014, 70(2):36-44.
[8]BRIEN W O, KAPSIS K, ATHIENITIS A K. Manually-operated window shade patterns in office buildings:a critical review[J]. Building and Environment, 2013, 60(2):319-338.
[9]HAQ M A U, HASSAN M Y, ABDULLAH H, et al. A review on lighting control technologies in commercial buildings, their performance and affecting factors[J]. Renewable&Sustainable Energy Reviews, 2014,33(2):268-279.
[10] YU Zhun, FUNG B C M, HAGHIGHAT F, et al. A systematic procedure to study the influence of occupant behavior on building energy consumption[J]. Energy and Buildings, 2011, 43(6):1409-1417.
[11]YANG Rui, WANG Lingfeng. Development of multi-agent system for building energy and comfort management based on occupant behaviors[J]. Energy and Buildings, 2013,56:1-7.
[12]HONG Tianzhen, OCA S D, TURNER W J N, et al. An ontology to represent energy-related occupant behavior in buildings. Part I:Introduction to the DNAs framework[J]. Building and Environment, 2015,92:764-777.
[13]HONG Tianzhen, OCA S D, TAYLOR-LANGE S C, et al. An ontology to represent energy-related occupant behavior in buildings. Part II:Implementation of the DNAS framework using an XML schema[J].Building and Environment, 2015, 94:196-205.
[14]张运楚,韩怀宝,曹建荣.建筑能耗管理与室内空间感知研究进展[J].山东建筑大学学报,2016,31(6):614-621.
[15]IDOUDI M, ELKHORCHANI H, GRAYAA K. Performance evaluation of wireless sensor networks based on zigbee technology in smart home[C]//Anon. Electrical Engineering and Software Applications(ICEESA)2013. New Jersey:IEEE, 2013:1-4.
[16]黄蔚欣.基于室内定位系统(IPS)大数据的环境行为分析初探——以万科松花湖度假区为例[J].世界建筑,2016(4):126-128.
[17]SILVA B, PANG Z,?KERBERG J, et al. Experimental study of UWBbased high precision localization for industrial applications[C]//Anon. 2014 IEEE international conference on ultra-wideband(icuwb).New Jersey:IEEE, 2014:280-285.
[18]ZETIK R, GUOWEI S, THOM?R S. Evaluation of requirements for UWB localization systems in home-entertainment applications[C]//Anon. 2010 International conference on indoor positioning and indoor navigation. New Jersey:IEEE, 2010:1-8.
[19]LI Li, LI Xin, LU Zhihan, et al. Sequential behavior pattern discovery with frequent episode mining and wireless sensor network[J]. IEEE Communications Magazine, 2017, 55(6):205-211.
[20]DONG Jia, LI Li, HUANG Jiaxuan, et al. Isovist based analysis of supermarket layout:verification of visibility graph analysis and multiagent simulation[C]//HEITOR T. Proceedings of the 11th space syntax symposium. Lisboa:Tecnico Lisboa, 2017:155-166.