Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Numerous occupant behavior (OB) models that simulate occupancy, activity and action at home have been developed to improve the accuracy and quality of energy demand estimations. Previous studies have revealed that the consideration of inter-occupant diversity improves the performance of OB models. However, existing models ignore spatial variation in OBs or partially consider it using a simple method without evaluating whether it is sufficient. Moreover, the modeling method to reproduce the spatial variation is missing. This study aims to develop a modeling method that can effectively reproduce spatial variation in OBs using American time use data. The global Moran's index test confirmed that spatial variations exist in OBs; however, they differ by time of day and activities for studied population. Subsequently, two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Finally, three spatial logistic regression models that consider spatial variations were developed and evaluated. The developed models generated smaller errors and higher interoccupant diversity than the conventional logistic regression models at the state level. The established method is applicable to any country and region. Using higher spatial resolution and richer time use datasets may further improve OB models to model region-specific characteristics of building energy demand.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study aims to develop a modeling method that can effectively reproduce spatial variation in occupant behaviors (OBs) using American time use data. The results show that spatial variations exist in OBs and differ by time of day and activities. Two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Three spatial logistic regression models that consider spatial variations were developed and evaluated, showing smaller errors and higher interoccupant diversity than conventional models at the state level.