%0 Journal Article
%K Mobility
%K Sensors
%K Big data
%K Occupant behavior
%K Machine learning
%K Energy modeling
%K Urban data
%K Energy in buildings
%K Energy in cities
%A Flora D Salim
%A Bing Dong
%A Mohamed Ouf
%A Qi Wang
%A Ilaria Pigliautile
%A Xuyuan Kang
%A Tianzhen Hong
%A Wenbo Wu
%A Yapan Liu
%A Shakila Khan Rumi
%A Mohammad Saiedur Rahaman
%A Jingjing An
%A Hengfang Deng
%A Wei Shao
%A Jakub Dziedzic
%A Fisayo Caleb Sangogboye
%A Mikkel Baun Kjærgaard
%A Meng Kong
%A Claudia Fabiani
%A Anna Laura Pisello
%A Da Yan
%B Building and Environment
%D 2020
%G eng
%P 106964
%R 10.1016/j.buildenv.2020.106964
%T Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey
%V 183
%8 10/2020
%! Building and Environment
%X <p>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.</p>