| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
