检索结果-南通市图书馆

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

building Simulation 2021年第1期14卷 25-41页

作者： Xing Lu Fan Feng Zhihong Pang Tao Yang Zheng O’Neill Department of Mechanical Engineering Texas A&M UniversityCollege StationTX77843USA

building occupancy,one of the most important consequences of occupant behaviors,is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community.With the vast development of information technologies in the era of the internet-of-things,occupant sensing and data acquisition are not limited to a single node or traditional approaches.The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time.In this paper,we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks.The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media.On top of that,the typical building occupancy schedules are extracted with assumed people counting rules.The second approach utilizes the processed Global Positioning System(GPS)tracking data provided by social networking service companies such as Facebook and Google Maps.Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules.The results show that the extracted building occupancy schedules from different data sources(Twitter,Facebook,and Google Maps)share a similar trend but are slightly distinct from each other and hence may require further validation and corrections.To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media(TOSSM),data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM.The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.

关键词： occupancy schedule social media building energy modeling data-driven models

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

DeST 3.0:A new-generation building performance simulation platform

引用

building Simulation 2022年第11期15卷 1849-1868页

作者： Da Yan Xin Zhou Jingjing An Xuyuan Kang Fan Bu Youming Chen Yiqun Pan Yan Gao Qunli Zhang Hui Zhou Kuining Qiu Jing Liu Yan Liu Honglian Li Lei Zhang Hong Dong Lixin Sun Song Pan Xiang Zhou Zhe Tian Wenjie Zhang Ruhong Wu Hongsan Sun Yu Huang Xiaohong Su Yongwei Zhang Rui Shen Diankun Chen Guangyuan Wei Yixing Chen Jinqing Peng building energy Research Center School of ArchitectureTsinghua UniversityBeijing 100084China School of Architecture Southeast UniversityNanjingJiangsu 210096China School of Environment and energy Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China College of Civil Engineering Hunan UniversityChangshaHunan 410082China School of Mechanical Engineering Tongji UniversityShanghai200092China Beijing Key Lab of Heating Gas SupplyVentilating and Air Conditioning EngineeringBeijing University of Civil Engineering and ArchitectureBeijing 100044China China State Construction Technology Center Beijing 101320China School of Architecture Harbin Institute of TechnologyHarbinHeilongjiang 150090China School of Architecture Xi'an University of Architecture and TechnologyXi'anShaanxi 710055China School of Architecture South China University of TechnologyGuangzhouGuangdong 510641China Institute of building Environment and energy China Academy of Building ResearchBeijing 100013China Beijing Key Laboratory of Green Built Environment and energy Efficient Technology Beijing University of TechnologyBeijing 100124China Key Lab of Built Environment and energy of Tianjin School of Environmental Science and EngineeringTianjin UniversityTianjin 300350China School of energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Civil Engineering Guangzhou UniversityGuangzhou 510006China Faculty of Computing Harbin Institute of TechnologyHarbinHeilongjiang 150001China Beijing Glory PKPM Technology Co. Ltd.Beijing 100013China Beijing Tangent Software Co. LtdBeijing 100081China Shanghai Huadianyuan Information Technology Co. LtdShanghai200092China Luoyang Hongye Information Technology Co. Ltd.LuoyangHenan 471822China

buildings contribute to almost 30%of total energy consumption worldwide.Developing building energy modeling programs is of great significance for lifecycle building performance assessment and optimization.Advances in novel building technologies,the requirements of high-performance computation,and the demands for multi-objective models have brought new challenges for building energy modeling software and platforms.To meet the increasing simulation demands,DeST 3.0,a new-generation building performance simulation platform,was developed and released.The structure of DeST 3.0 incorporates four simulation engines,including building analysis and simulation(BAS)engine,HVAC system engine,combined plant simulation(CPS)engine,and energy system(ES)engine,connected by air loop and water loop balancing iterations.DeST 3.0 offers numerous new simulation features,such as advanced simulation modules for building envelopes,occupant behavior and energy systems,cross-platform and compatible simulation kernel,FMI/FMU-based co-simulation functionalities,and high-performance parallel simulation architecture.DeST 3.0 has been thoroughly evaluated and validated using code verification,inter-program comparison,and case-study calibration.DeST 3.0 has been applied in various aspects throughout the building lifecycle,supporting building design,operation,retrofit analysis,code appliance,technology adaptability evaluation as well as research and education.The new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization.

关键词： DeST building performance simulation building energy modeling

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

引用

building Simulation 2022年第8期15卷 1385-1400页

作者： Kaiyu Sun Tianzhen Hong Janghyun Kim Barry Hooper Lawrence Berkeley National Laboratory BerkeleyCAUSA National Renewable energy Laboratory GoldenCOUSA San Francisco Deportment of the Environment San FranciscoCAUSA

building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in energyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.

关键词： model calibration building energy modeling energyPlus building performance simulation monthly energy use

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

引用

building Simulation 2021年第1期14卷 3-24页

作者： Cheng Fan Da Yan Fu Xiao Ao Li Jingjing An Xuyuan Kang Sino-Australia Joint Research Center in BIM and Smart Construction College of Civil and Transportation EngineeringShenzhen UniversityShenzhenChina building energy Research Center School of ArchitectureTsinghua UniversityBeijingChina Department of building Services Engineering The Hong Kong Polytechnic UniversityHong KongChina School of Environment and energy Engineering Beijing University of Civil Engineering and ArchitectureBeijingChina

buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

关键词： advanced data analytics big-data-driven building energy modeling building operational data building performance

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：