@article{44b74434bbd54d5f8f4be12ab791acda,
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",
note = "Funding Information: This paper is an output of IEA EBC Annex 79 SubTask 2. Flora Salim acknowledges the support of the Alexander von Humboldt Foundation and Bayer Foundation for her Humboldt- Bayer Fellowship, and also Australian Research Council Discovery DP190101485. Bing Dong would like to thank the support from the U.S. National Science Foundation CAREER Award (Award No. 1949372). Tianzhen Hong's work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy (Contract No. DE-AC02-05CH11231). Fisayo Caleb Sangogboye and Mikkel Baun Kj{\ae}rgaard would like to acknowledge funding by Danish Energy Agency EUDP (Grant, n. 64018–0558 ). Da Yan acknowledges the support of National Natural Science Foundation of China , grant number 51778321. Funding Information: Location-based service (LBS) data are collected by location-based service providers who usually embed their functions into many smart phone apps. In such a way, these datasets often cover large percentage of populations with decent quantity (e.g., 100 or more records per day) and resolution. LBS data has supported promising new directions of research. However, there are some challenges associated with human mobility data from LBS data. Data acquired from LBS is sparse in nature. Again, data from LBS are generally bias to tech savvy young people [109].This paper is an output of IEA EBC Annex 79 SubTask 2. Flora Salim acknowledges the support of the Alexander von Humboldt Foundation and Bayer Foundation for her Humboldt-Bayer Fellowship, and also Australian Research Council Discovery DP190101485. Bing Dong would like to thank the support from the U.S. National Science Foundation CAREER Award (Award No. 1949372). Tianzhen Hong's work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy (Contract No. DE-AC02-05CH11231). Fisayo Caleb Sangogboye and Mikkel Baun Kj?rgaard would like to acknowledge funding by Danish Energy Agency EUDP (Grant, n. 64018?0558). Da Yan acknowledges the support of National Natural Science Foundation of China, grant number 51778321. Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2020",
month = oct,
doi = "10.1016/j.buildenv.2020.106964",
language = "English (US)",
volume = "183",
journal = "Building and Environment",
issn = "0360-1323",
publisher = "Elsevier",
}