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

Flora D. Salim; Bing Dong; Mohamed Ouf; Qi Wang; Ilaria Pigliautile; Xuyuan Kang; Tianzhen Hong; Wenbo Wu; Yapan Liu; Shakila Khan Rumi; Mohammad Saiedur Rahaman; Jingjing An; Hengfang Deng; Wei Shao; Jakub Dziedzic; Fisayo Caleb Sangogboye; Mikkel Baun Kjærgaard; Meng Kong; Claudia Fabiani; Anna Laura Pisello; Da Yan

doi:10.1016/j.buildenv.2020.106964

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

Flora D. Salim^*, Bing Dong, Mohamed Ouf, Qi Wang, Ilaria Pigliautile, Xuyuan Kang, Tianzhen Hong, Wenbo Wu, Yapan Liu, Shakila Khan Rumi, Mohammad Saiedur Rahaman, Jingjing An, Hengfang Deng, Wei Shao, Jakub Dziedzic, Fisayo Caleb Sangogboye, Mikkel Baun Kjærgaard, Meng Kong, Claudia Fabiani, Anna Laura PiselloDa Yan

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

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.

Original language	English
Article number	106964
Journal	Building and Environment
Volume	183
Number of pages	20
ISSN	0360-1323
DOIs	https://doi.org/10.1016/j.buildenv.2020.106964
Publication status	Published - Oct 2020

Keywords

Big data
Energy in buildings
Energy in cities
Energy modelling
Machine learning
Mobility
Occupant behaviour
Sensors
Urban data

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10.1016/j.buildenv.2020.106964

Open Access VersionAccepted manuscript, 1.77 MBLicence: CC BY-NC-ND

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Salim, F. D., Dong, B., Ouf, M., Wang, Q., Pigliautile, I., Kang, X., Hong, T., Wu, W., Liu, Y., Rumi, S. K., Rahaman, M. S., An, J., Deng, H., Shao, W., Dziedzic, J., Sangogboye, F. C., Kjærgaard, M. B., Kong, M., Fabiani, C., ... Yan, D. (2020). Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey. Building and Environment, 183, Article 106964. https://doi.org/10.1016/j.buildenv.2020.106964

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

abstract = "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.",

keywords = "Big data, Energy in buildings, Energy in cities, Energy modelling, Machine learning, Mobility, Occupant behaviour, Sensors, Urban data",

author = "Salim, {Flora D.} and Bing Dong and Mohamed Ouf and Qi Wang and Ilaria Pigliautile and Xuyuan Kang and Tianzhen Hong and Wenbo Wu and Yapan Liu and Rumi, {Shakila Khan} and Rahaman, {Mohammad Saiedur} and Jingjing An and Hengfang Deng and Wei Shao and Jakub Dziedzic and Sangogboye, {Fisayo Caleb} and Kj{\ae}rgaard, {Mikkel Baun} and Meng Kong and Claudia Fabiani and Pisello, {Anna Laura} and Da Yan",

year = "2020",

month = oct,

doi = "10.1016/j.buildenv.2020.106964",

language = "English",

volume = "183",

journal = "Building and Environment",

issn = "0360-1323",

publisher = "Elsevier",

}

Salim, FD, Dong, B, Ouf, M, Wang, Q, Pigliautile, I, Kang, X, Hong, T, Wu, W, Liu, Y, Rumi, SK, Rahaman, MS, An, J, Deng, H, Shao, W, Dziedzic, J, Sangogboye, FC, Kjærgaard, MB, Kong, M, Fabiani, C, Pisello, AL & Yan, D 2020, 'Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey', Building and Environment, vol. 183, 106964. https://doi.org/10.1016/j.buildenv.2020.106964

TY - JOUR

T1 - Modelling urban-scale occupant behaviour, mobility, and energy in buildings

T2 - A survey

AU - Salim, Flora D.

AU - Dong, Bing

AU - Ouf, Mohamed

AU - Wang, Qi

AU - Pigliautile, Ilaria

AU - Kang, Xuyuan

AU - Hong, Tianzhen

AU - Wu, Wenbo

AU - Liu, Yapan

AU - Rumi, Shakila Khan

AU - Rahaman, Mohammad Saiedur

AU - An, Jingjing

AU - Deng, Hengfang

AU - Shao, Wei

AU - Dziedzic, Jakub

AU - Sangogboye, Fisayo Caleb

AU - Kjærgaard, Mikkel Baun

AU - Kong, Meng

AU - Fabiani, Claudia

AU - Pisello, Anna Laura

AU - Yan, Da

PY - 2020/10

Y1 - 2020/10

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.

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.

KW - Big data

KW - Energy in buildings

KW - Energy in cities

KW - Energy modelling

KW - Machine learning

KW - Mobility

KW - Occupant behaviour

KW - Sensors

KW - Urban data

U2 - 10.1016/j.buildenv.2020.106964

DO - 10.1016/j.buildenv.2020.106964

M3 - Journal article

AN - SCOPUS:85089836732

SN - 0360-1323

VL - 183

JO - Building and Environment

JF - Building and Environment

M1 - 106964

ER -

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

Abstract

Keywords

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