TY - JOUR KW - Mobility KW - Sensors KW - Big data KW - Occupant behavior KW - Machine learning KW - Energy modeling KW - Urban data KW - Energy in buildings KW - Energy in cities AU - Flora D Salim AU - Bing Dong AU - Mohamed Ouf AU - Qi Wang AU - Ilaria Pigliautile AU - Xuyuan Kang AU - Tianzhen Hong AU - Wenbo Wu AU - Yapan Liu AU - Shakila Khan Rumi AU - Mohammad Saiedur Rahaman AU - Jingjing An AU - Hengfang Deng AU - Wei Shao AU - Jakub Dziedzic AU - Fisayo Caleb Sangogboye AU - Mikkel Baun Kjærgaard AU - Meng Kong AU - Claudia Fabiani AU - Anna Laura Pisello AU - Da Yan AB -

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.

BT - Building and Environment DA - 10/2020 DO - 10.1016/j.buildenv.2020.106964 LA - eng N2 -

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.

PY - 2020 EP - 106964 ST - Building and Environment T2 - Building and Environment TI - Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey VL - 183 SN - 03601323 ER -