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.
Get full access to this article
View all available purchase options and get full access to this article.
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.
References
Ahmed, M., R. Seraj, and S. M. S. Islam. 2020. “The k-means algorithm: A comprehensive survey and performance evaluation.” Electronics 9 (8): 1295. https://doi.org/10.3390/electronics9081295.
Akoglu, H. 2018. “User’s guide to correlation coefficients.” Turk. J. Emerg. Med. 18 (3): 91–93. https://doi.org/10.1016/j.tjem.2018.08.001.
ATUS (American Time Use Survey). 2022. An introduction to explainable. Washington, DC: US Bureau of Labor Statistics.
Bäcklund, K., M. Molinari, P. Lundqvist, and B. Palm. 2023. “Building occupants, their behavior and the resulting impact on energy use in campus buildings: A literature review with focus on smart building systems.” Energies 16 (17): 6104. https://doi.org/10.3390/en16176104.
Bauman, A., M. Bittman, and J. Gershuny. 2019. “A short history of time use research; implications for public health.” Supplement, BMC Public Health 19 (S2): 607. https://doi.org/10.1186/s12889-019-6760-y.
Brambilla, A., C. Candido, I. Hettiarachchi, L. Thomas, O. Gocer, K. Gocer, M. Mackey, N. Biloria, T. Alizadeh, and S. Sarkar. 2021. “The potential of harnessing real-time occupancy data for improving energy performance of activity-based workplaces.” Energies 15 (1): 230. https://doi.org/10.3390/en15010230.
Cabeza, L. F., L. Rincón, V. Vilariño, G. Pérez, and A. Castell. 2014. “Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review.” Renewable Sustainable Energy Rev. 29 (Jan): 394–416. https://doi.org/10.1016/j.rser.2013.08.037.
Chahardoli, S., M. Khakzand, M. Faizi, and M. Siavashi. 2022. “Numerical analysis of the effect of roof types and porch on particle dispersion and deposition around a low-rise building.” J. Build. Eng. 53 (Aug): 104533. https://doi.org/10.1016/j.jobe.2022.104533.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. San Francisco: Association for Computing Machinery.
Ding, Y., W. Chen, S. Wei, and F. Yang. 2021. “An occupancy prediction model for campus buildings based on the diversity of occupancy patterns.” Sustainable Cities Soc. 64 (Jan): 102533. https://doi.org/10.1016/j.scs.2020.102533.
Duan, Y., and B. Dong. 2014. “The contribution of occupancy behavior to energy consumption in low-income residential buildings.” In Proc., Int. High Performance Buildings Conf. West Lafayette, IN: Purdue University Press.
EIA (Energy Information Administration). 2022. How much energy is consumed in U.S. buildings? Washington, DC: EIA.
Fabi, V., R. V. Andersen, S. P. Corgnati, and B. W. Olesen. 2013. “A methodology for modelling energy-related human behaviour: Application to window opening behaviour in residential buildings.” Build. Simul. 6 (4): 415–427. https://doi.org/10.1007/s12273-013-0119-6.
Fry, R., and R. Kochhar. 2016. The shrinking middle class in U.S. metropolitan areas: 6 key findings. Washington, DC: Pew Research Center.
Liang, X., T. Hong, and G. Q. Shen. 2016. Improving the accuracy of energy baseline models for commercial buildings with occupancy data. Berkeley, CA: Lawrence Berkeley National Laboratory.
Liu, X., S. Hu, and D. Yan. 2023. “A statistical quantitative analysis of the correlations between socio-demographic characteristics and household occupancy patterns in residential buildings in China.” Energy Build. 284 (Apr): 112842. https://doi.org/10.1016/j.enbuild.2023.112842.
Malekpour Koupaei, D., K. S. Cetin, and U. Passe. 2022. “Stochastic residential occupancy schedules based on the American time-use survey.” Sci. Technol. Built Environ. 28 (6): 776–790. https://doi.org/10.1080/23744731.2022.2087536.
Marutho, D., S. H. Handaka, and E. Wijaya. 2018. “The determination of cluster number at k-mean using elbow method and purity evaluation on headline news.” In Proc., 2018 Int. Seminar on Application for Technology of Information and Communication, 533–538. New York: IEEE.
Mitra, D., Y. Chu, and K. Cetin. 2021a. “Cluster analysis of occupancy schedules in residential buildings in the United States.” Energy Build. 236 (Apr): 110791. https://doi.org/10.1016/j.enbuild.2021.110791.
Mitra, D., Y. Chu, and K. Cetin. 2022. “COVID-19 impacts on residential occupancy schedules and activities in U.S. Homes in 2020 using ATUS.” Appl. Energy 324 (Oct): 119765. https://doi.org/10.1016/j.apenergy.2022.119765.
Mitra, D., Y. Chu, K. Cetin, Y. Wang, and C. Chen. 2021b. “Variation in residential occupancy profiles in the United States by household income level and characteristics.” J. Build. Perform. Simul. 14 (6): 692–711. https://doi.org/10.1080/19401493.2021.2001572.
Mitra, D., Y. Chu, N. Steinmetz, K. Cetin, P. Kremer, and J. Lovejoy. 2019. “Defining typical occupancy schedules and behaviors in residential buildings using the American time use survey.” ASHRAE Trans. 125: 382–390.
Mitra, D., N. Steinmetz, Y. Chu, and K. S. Cetin. 2020. “Typical occupancy profiles and behaviors in residential buildings in the United States.” Energy Build. 210 (Mar): 109713. https://doi.org/10.1016/j.enbuild.2019.109713.
Olawale, O. W., B. Gilbert, and J. Reyna. 2022. “Residential demand flexibility: Modeling occupant behavior using sociodemographic predictors.” Energy Build. 262 (May): 111973. https://doi.org/10.1016/j.enbuild.2022.111973.
Rusek, R., J. Melendez Frigola, and J. Colomer Llinas. 2022. “Influence of occupant presence patterns on energy consumption and its relation to comfort: A case study based on sensor and crowd-sensed data.” Energy Sustainability Soc. 12 (1): 13. https://doi.org/10.1186/s13705-022-00336-6.
Schober, P., C. Boer, and L. A. Schwarte. 2018. “Correlation coefficients: Appropriate use and interpretation.” Anesth. Analg. 126 (5): 1763–1768. https://doi.org/10.1213/ANE.0000000000002864.
Sekar, A., E. Williams, and R. Chen. 2016. “Heterogeneity in time and energy use of watching television.” Energy Policy 93 (Jun): 50–58. https://doi.org/10.1016/j.enpol.2016.02.035.
Vosoughkhosravi, S., L. Dixon-Grasso, and A. Jafari. 2022. “The impact of LEED certification on energy performance and occupant satisfaction: A case study of residential college buildings.” J. Build. Eng. 59 (Nov): 105097. https://doi.org/10.1016/j.jobe.2022.105097.
Vosoughkhosravi, S., and A. Jafari. 2024. “American time use survey in modeling occupant behavior: A systematic review.” Comput. Civ. Eng. 2023: 77–84. https://doi.org/10.1061/9780784485248.010.
Vosoughkhosravi, S., A. Jafari, and Y. Zhu. 2023. “Application of American time use survey (ATUS) in modelling energy-related occupant-building interactions: A comprehensive review.” Energy Build. 294 (Sep): 113245. https://doi.org/10.1016/j.enbuild.2023.113245.
Yoshino, H., T. Hong, and N. Nord. 2017. “IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods.” Energy Build. 152 (Oct): 124–136. https://doi.org/10.1016/j.enbuild.2017.07.038.
Information & Authors
Information
Published In
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
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.