Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An Analysis of Building Occupancy Patterns Based on Time Use Survey Data
https://orcid.org/http://orcid.org/0000-0001-7742-6067
https://orcid.org/http://orcid.org/0000-0003-4060-256X
https://orcid.org/http://orcid.org/0000-0003-0398-5303
https://orcid.org/http://orcid.org/0000-0003-1886-9137
https://orcid.org/http://orcid.org/0000-0002-8694-7232
Understanding occupant behavior (OB) is crucial for accurate building performance simulations. Most existing tools rely on a single, static set of user schedules, overlooking the inherent variability, and diversity of human behavior. This leads to inaccurate energy predictions and limitations in scaling simulations to urban contexts. This study explores the daily occupancy patterns of individuals across diverse building types using data from the 2013 Italian Time Use Survey. Cluster analysis identifies 18 recurrent patterns distinct for weekdays, Saturdays, and Sundays, further investigated through sociodemographic correlations. This innovative approach focuses on individuals’ daily movements across urban spaces, not just single building types, providing versatile results for diverse case studies and urban scale applications.
An Analysis of Building Occupancy Patterns Based on Time Use Survey Data
https://orcid.org/http://orcid.org/0000-0001-7742-6067
https://orcid.org/http://orcid.org/0000-0003-4060-256X
https://orcid.org/http://orcid.org/0000-0003-0398-5303
https://orcid.org/http://orcid.org/0000-0003-1886-9137
https://orcid.org/http://orcid.org/0000-0002-8694-7232
Understanding occupant behavior (OB) is crucial for accurate building performance simulations. Most existing tools rely on a single, static set of user schedules, overlooking the inherent variability, and diversity of human behavior. This leads to inaccurate energy predictions and limitations in scaling simulations to urban contexts. This study explores the daily occupancy patterns of individuals across diverse building types using data from the 2013 Italian Time Use Survey. Cluster analysis identifies 18 recurrent patterns distinct for weekdays, Saturdays, and Sundays, further investigated through sociodemographic correlations. This innovative approach focuses on individuals’ daily movements across urban spaces, not just single building types, providing versatile results for diverse case studies and urban scale applications.
An Analysis of Building Occupancy Patterns Based on Time Use Survey Data
https://orcid.org/http://orcid.org/0000-0001-7742-6067
https://orcid.org/http://orcid.org/0000-0003-4060-256X
https://orcid.org/http://orcid.org/0000-0003-0398-5303
https://orcid.org/http://orcid.org/0000-0003-1886-9137
https://orcid.org/http://orcid.org/0000-0002-8694-7232
Understanding occupant behavior (OB) is crucial for accurate building performance simulations. Most existing tools rely on a single, static set of user schedules, overlooking the inherent variability, and diversity of human behavior. This leads to inaccurate energy predictions and limitations in scaling simulations to urban contexts. This study explores the daily occupancy patterns of individuals across diverse building types using data from the 2013 Italian Time Use Survey. Cluster analysis identifies 18 recurrent patterns distinct for weekdays, Saturdays, and Sundays, further investigated through sociodemographic correlations. This innovative approach focuses on individuals’ daily movements across urban spaces, not just single building types, providing versatile results for diverse case studies and urban scale applications.
An Analysis of Building Occupancy Patterns Based on Time Use Survey Data
Lecture Notes in Civil Engineering
Berardi, Umberto (Herausgeber:in) / Banfi, Alessia (Autor:in) / Ferrando, Martina (Autor:in) / Malik, Jeetika (Autor:in) / Hong, Tianzhen (Autor:in) / Causone, Francesco (Autor:in)
International Association of Building Physics ; 2024 ; Toronto, ON, Canada
19.12.2024
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| BASE | 2016
Revealing occupancy patterns in an office building through the use of occupancy sensor data
| Online Contents | 2013