Mapping Residential Occupancy: Understanding Sociodemographic Influences on Occupancy Patterns Using the American Time Use Survey
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
Residential buildings in the US are substantial energy consumers, accounting for 39% of the country’s electricity usage and 22% of its total energy consumption. The dynamics of this consumption are intricately linked to the presence and activities of occupants. As such, a precise analysis of occupancy patterns is vital to gaining an informed understanding of the changing trends in energy use. This study harnesses data from the American Time Use Survey (ATUS) to delve into the influence of sociodemographic features on individuals’ occupancy patterns throughout the day. Employing statistical methods and exploratory machine-learning techniques, this study aims to map American occupancy patterns and investigate the impact of various demographic features on these patterns. Six key features that have a predominant effect on occupancy patterns are identified as age, gender, employment status, family income, household type, and day of the week. A predictive model has also been developed to model occupancy patterns of individuals based on the identified features using artificial neural networks (ANN). Comprehending how these features shape residential occupancy is crucial for devising specific energy-conservation strategies for residential buildings. The contributions of this research extend the current understanding of energy-efficient architecture design, providing valuable insights for stakeholders and policymakers in the energy sector.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 6 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 15, 2024
Accepted: Jun 17, 2024
Published online: Aug 23, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 23, 2025
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