A comprehensive review of time use surveys in modelling occupant presence and behavior: Data, methods, and applications

Context-aware module for evaporative cooling in the outdoor built environment

“…This study is inspired by the expanding field of research on indoor climate control incorporating human behaviour. 26 It relates to approaches addressing indoor climate control through the use of sensing devices and computational techniques. 28,30 And it develops a computational technique specific to EC systems in outdoor environments by formulating a particular computational context and introducing a three-phase algorithm.…”

“…25 The growing body of research demonstrates that a better understanding of occupant behaviour can help improve buildings' energy consumption. 26 Indoor environments have well-established boundaries that enable easier control of climatic parameters, influencing both occupant comfort and energy consumption. 27 Studies addressing indoor climates are more feasible because of the controllability and predictability of climatic conditions and are more common as most people work and spend more time indoors.…”

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

“…OB is a major source of uncertainty in building energy demand modeling because energyconsuming appliances are generally operated to meet people's daily needs in response to activities performed by occupants, and building energy systems and indoor environments are adjusted by occupants for comfort [1]. Various methods have been applied to time use data integrated with additional survey data that cover social, economic, and building aspects, to develop representative OB models [2]. However, a significant gap exists between simulation and reality [3] owing to (1) the use of oversimplified assumptions, such as a fixed schedule rather than a dynamic schedule; (2) assumptions on when and how residents use appliances and building systems; and (3) ignorance of inter-occupant diversity [4].…”

“…Various methods have been applied to time use data integrated with additional survey data that cover social, economic, and building aspects, to develop representative OB models [2]. However, a significant gap exists between simulation and reality [3] owing to (1) the use of oversimplified assumptions, such as a fixed schedule rather than a dynamic schedule; (2) assumptions on when and how residents use appliances and building systems; and (3) ignorance of inter-occupant diversity [4]. Although some studies have attempted to address the first two gaps, inter-occupant diversity, particularly in terms of spatial variation, has not been thoroughly investigated [5,6].…”

Indoor Positioning Simulation for Examination and Correction of Occupancy Density Limits in Architectural Design

“…The monitoring of occupant activity and interaction with building systems provides additional information for research on how buildings are used and managed [6]. There is a fast-growing interest in acquiring data on occupant activity in indoor environments, in order to inform the operation of building systems for lighting, heating, ventilation, and air-conditioning, and make them responsive to occupants' needs [7,8]. The recent research aims to reduce energy consumption and provide comfort by learning user behaviour [9], and suggests that it is key to closing the performance gap between building design and operation [10].…”