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.

Understanding occupants’ behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

“…Specifically, various data mining (e.g., segmentation 24 , clustering 25 , 26 ) and modelling techniques 27 – 29 could be explored to build prediction models for measuring occupants’ mental state using sensor-based physiological and behavioural recordings in buildings. This could be further used for various applications in future studies: (1) Monitoring signs of disengagement and negative emotions of students 9 , 30 .…”

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

“…For instance, Zhang et al reviewed a modeling technique for urban energy systems at the building cluster level that incorporated renewable-energy-source envelope solutions, in which they highlighted that a high-resolution energy profile, a spatio-temporal energy demand (both building and mobility) and detailed engineering/physical and statistical models are desirable for further development [15]. Salim et al defined occupant-centric urban data and the pipeline to process it in a review paper that also outlined the different sources of urban data for modeling urban-scale occupant behavior: mobility and energy in buildings [16]. Perera et al developed a stochastic optimization method to consider the impact of climate change and extreme climate events on urban energy systems across 30 cities in Sweden by considering 13 climate change scenarios [17].…”

Multi-Agent Simulation of Human Activity for Residential Electrical Load and Demand Forecasting and Its Application to Electric Mobility

“…Particularly, [1] call for an OBM that incorporates social, and psychological science to reveal the intrinsic causes of OB. Similarly, [4] advocate for OBM that integrate domain experts and modeling methods from building science, social science and psychology to simulate the root cause of occupant behavior on energy consumption. Despite their thorough OBM review, [5] still find a lack in the literature for OBM able to explain and predict occupants' behavior and their interactions with their environment based on multiple aspects of human activity (e.g.…”