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

作     者：Cheng Fan Da Yan Fu Xiao Ao Li Jingjing An Xuyuan Kang Cheng Fan;Da Yan;Fu Xiao;Ao Li;Jingjing An;Xuyuan Kang

作者机构：Sino-Australia Joint Research Center in BIM and Smart ConstructionCollege of Civil and Transportation EngineeringShenzhen UniversityShenzhenChina Building Energy Research CenterSchool of ArchitectureTsinghua UniversityBeijingChina Department of Building Services EngineeringThe Hong Kong Polytechnic UniversityHong KongChina School of Environment and Energy EngineeringBeijing University of Civil Engineering and ArchitectureBeijingChina 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2021年第14卷第1期

页      面：3-24页

核心收录：

学科分类：08[工学] 081304[工学-建筑技术科学] 0813[工学-建筑学] 

基　　金：The authors gratefully acknowledge the support of this research by the Research Grant Council of Hong Kong SAR(152075/19E) the National Natural Science Foundation of China(No.51908365) the National Natural Science Foundation of China(No.51778321) 

主　　题：advanced data analytics big-data-driven building energy modeling building operational data building performance 

摘      要：Buildings have a significant impact on global *** the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building ***-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online *** 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 *** 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 *** 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 *** 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 *** review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy ***,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.