Synthetic Populations of Building Office Occupants and Behaviors
The goal of this chapter is to convey a novel approach to overcoming the limitations of case study research of building occupant behavior in workplace settings by pooling samples and creating a synthetic population of building occupants and behaviors. Synthetic populations can be used by researchers and designers of buildings to develop more accurate models of performance and behavior (Andrews et al. 2016). In the example presented here, three disparate field studies of workplace settings are combined into a larger database that is enhanced through the generation of a statistically similar synthetic data set.
KeywordsBuilding occupant behavior Synthetic populations Post-occupancy evaluation Building performance Organizational behavior
This research was supported by the Consortium for Building Energy Innovation, sponsored by the US Department of Energy Award Number DE-EE0004261.
- Andrews, C. J., Senick, J. A., & Wener, R. E. (2012). Incorporating occupant perceptions and behavior into BIM. In S. Mallory-Hill, W. Preiser, & C. Watson (Eds.), Enhancing building performance (pp. 234–246). Oxford, UK: Blackwell Publishing.Google Scholar
- Andrews, C. J., Allacci, M., Senick, J. A., Putra, H. C., & Tsoulou, I. (2016). Using synthetic population data for prospective modeling of occupant behavior during design. Accepted for publication in Energy and Buildings special issue on occupancy behavior.Google Scholar
- de Dear, R. J., Brager, G. S. & Cooper, D. (1997). ASHRAE RP-884 dataset link: http://sydney.edu.au/architecture/staff/homepage/richard_de_dear/ashrae_rp-884.shtml. Accessed 04 May 2016.
- de Dear, R. J., & Brager, G. S. (1998). Towards an adaptive model of thermal comfort and preference. ASHRAE Transactions, 104(1), 145–167.Google Scholar
- Grefenstette, J. J., Brown, S. T., Rosenfeld, R., DePasse, J., Stone, N. T. B., Cooley, P. C., et al. (2013). FRED (a framework for reconstructing epidemic dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations. BioMed Central Ltd. doi: 10.1186/1471-2458-13-940.Google Scholar
- Harland, K., Heppendstall, A., Smith, D., & Birkin, M. (2012). Creating realistic synthetic populations at varying spatial scales: A comparative critique of population synthesis techniques. Journal of Artificial Societies and Social Simulation 15(1):1. http://jasss.soc.surrey.ac.uk/15/1/1.html. Accessed 04 May 2016.
- Langevin, J. (2015). Longitudinal dataset. http://en.openei.org/datasets/dataset/one-year-behavior-environment-data-for-medium-office. Accessed 04 May 2016.
- Nowok, B., Raab, G. M., & Dibben, C. (2015). Synthpop: Bespoke creation of synthetic data in R. Package. vignette http://cran.r-project.org/web/packages/synthpop/vignettes/synthpop.pdf. Accessed 22 Nov 2015.
- Rutgers Center for Green Building. (2016). Cross-sectional dataset (2009–2014). http://en.openei.org/datasets/dataset/ob-commercial-building. Accessed 04 May 2016.