Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

被引:64
|
作者
Salim, Flora D. [1 ]
Dong, Bing [2 ]
Ouf, Mohamed [3 ]
Wang, Qi [4 ]
Pigliautile, Ilaria [5 ]
Kang, Xuyuan [6 ]
Hong, Tianzhen [7 ]
Wu, Wenbo [8 ]
Liu, Yapan [2 ]
Rumi, Shakila Khan [1 ]
Rahaman, Mohammad Saiedur [1 ]
An, Jingjing [11 ]
Deng, Hengfang [4 ]
Shao, Wei [1 ]
Dziedzic, Jakub [9 ]
Sangogboye, Fisayo Caleb [10 ]
Kjaergaard, Mikkel Baun [10 ]
Kong, Meng [2 ]
Fabiani, Claudia [5 ]
Pisello, Anna Laura [5 ]
Yan, Da [6 ]
机构
[1] RMIT Univ, Comp Sci & IT, Sch Sci, Melbourne, Vic, Australia
[2] Syracuse Univ, Dept Mech & Aerosp Engn, Syracuse, NY 13244 USA
[3] Concordia Univ, Dept Bldg Civil & Environm Engn, Montreal, PQ, Canada
[4] Northeastern Univ, Dept Civil & Environm Engn, Boston, MA 02115 USA
[5] Univ Perugia, Dept Engn, Perugia, Italy
[6] Tsinghua Univ, Sch Architecture, Beijing, Peoples R China
[7] Lawrence Berkeley Natl Lab, Bldg Technol & Urban Syst Div, Berkeley, CA USA
[8] Univ Texas San Antonio, Dept Management Sci & Stat, San Antonio, TX USA
[9] Norwegian Univ Sci & Technol, Dept Energy & Proc Engn, Trondheim, Norway
[10] Univ Southern Denmark, Ctr Energy Informat, Odense, Denmark
[11] Beijing Univ, Civil Engn & Architecture, Beijing, Peoples R China
基金
美国能源部; 美国国家科学基金会; 澳大利亚研究理事会; 中国国家自然科学基金;
关键词
Big data; Occupant behaviour; Energy modelling; Mobility; Urban data; Sensors; Machine learning; Energy in buildings; Energy in cities; SENSITIVITY-ANALYSIS; RESIDENTIAL SECTOR; BAYESIAN-INFERENCE; MONITORING-SYSTEM; STOCHASTIC-MODEL; THERMAL COMFORT; OUTDOOR COMFORT; BIG DATA; CONSUMPTION; SIMULATION;
D O I
10.1016/j.buildenv.2020.106964
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.
引用
收藏
页数:20
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