A Generalized Approach to Population Synthesis

Müller, Kirill

doi:10.3929/ethz-b-000171586

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It, however, cannot deal with multi-level populations, i.e., households and individuals. Improved versions are called Hierarchical IPF and Iterative Proportional Updating (IPU) (K ( K Müller, 2017;Sun et al, 2018). In contrast, CO-based population synthesis relies on the drawing of individuals from a sample to assess a fitness criterion.…”

Section: Population Synthesis Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Generating synthetic occupants for use in building performance simulation

Putra

Andrews

Hong

2021

Journal of Building Performance Simulation

View full text Add to dashboard Cite

Occupant behavior simulation frameworks can employ synthetic populations to characterize occupancy and behavioral patterns in buildings based on real demographic data at a certain geographical location. Multiple methods are available to generate a synthetic population, with pros-and cons-for different applications and contexts. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. We draw upon the extended version of Drivers-Needs-Actions-Systems (DNAS) framework to guide the selection of variables and data imputation. The resulting synthetic occupant data is evaluated by comparing the joint distributions between the actual and synthetic data sets, % difference, and Standardized-Root-Mean-Squared-Error (SRMSE). Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show the best performance compared to the cases of office and senior housing. Future work includes testing and demonstration of how the synthetic data sets feed to occupant behavior module through co-simulation with building performance simulation tools, such as EnergyPlus.

show abstract

Section: Population Synthesis Methodologymentioning

confidence: 99%

“…To validate the resulting synthetic population generated by using BN, we adopt a metric measuring goodness-of-fit, which is the standardized root mean square errors (SRMSE) (Müller, 2017):…”

Section: Bayesian Networkmentioning

confidence: 99%

Generating synthetic occupants for use in building performance simulation

Putra

Andrews

Hong

2021

Journal of Building Performance Simulation

View full text Add to dashboard Cite

show abstract

“…Our baseline population is limited to just the Greater Melbourne region, home to around 4 million residents in 2011, the second largest cities in Australia, and the capital city of the Victoria state. To generate a baseline synthetic population, we followed a standard synthetic reconstruction procedure which involves two stages: fitting and generation (Müller, 2017).…”

Section: Initialisationmentioning

confidence: 99%

A demographic microsimulation model with an integrated household alignment method

Siripanich¹,

Rashidi²

2020

Preprint

View full text Add to dashboard Cite

Many dynamic microsimulation models have shown their ability to reasonably project detailed population and households using non-data based household formation and dissolution rules. Although, those rules allow modellers to simplify changes in the household construction, they typically fall short in replicating household projections or if applied retrospectively the observed household numbers. Consequently, such models with biased estimation for household size and other household related attributes lose their usefulness in applications that are sensitive to household size, such as in travel demand and housing demand modelling. Nonetheless, these demographic microsimulation models with their associated shortcomings have been commonly used to assess various planning policies which can result in misleading judgements. In this paper, we contribute to the literature of population microsimulation by introducing a fully integrated system of models for different life event where a household alignment method adjusts household size distribution to closely align with any given target distribution. Furthermore, some demographic events that are generally difficult to model, such as incorporating immigrant families into a population, can be included. We illustrated an example of the household alignment method and put it to test in a dynamic microsimulation model that we developed using dymiumCore, a general-purpose microsimulation toolkit in R, to show potential improvements and weaknesses of the method. The implementation of this model has been made publicly available on GitHub, a code sharing platform.

show abstract