Development of a Library for Building Surface Layout Simulator

Available building simulation tools resort to using fixed schedules for modeling occupant behavior (OB), which does not accurately capture its nature. A significant aspect of OB is the movement and sequence of actions with regards to their surroundings. This requires some coherence about the surface layout, including the placement of furniture and the occupant’s interaction with it. There is a need for understanding vital information about the different attributes of the furniture, such as the placement and order of importance. Until now, there exists no such library with this kind of granularity in information. This paper explores the questions with regard to the development of such a library. This includes the description of the type of variables associated with different kinds of furniture, along with the occupant interaction under typical scenarios. The results from this study can be used to integrate the resulting library with building simulation tools and to better understand and develop occupant behavior models.

[1]  Caroline M. Clevenger,et al.  Demonstrating the Impact of the Occupant on Building Performance , 2014, J. Comput. Civ. Eng..

[2]  Ali Malkawi,et al.  Simulating multiple occupant behaviors in buildings: An agent-based modeling approach , 2014 .

[3]  Christoph F. Reinhart,et al.  Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control , 2006 .

[4]  Tianzhen Hong,et al.  Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .

[5]  Dino Bouchlaghem,et al.  Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .

[6]  Yi Jiang,et al.  A novel approach for building occupancy simulation , 2011 .

[7]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[8]  Ardeshir Mahdavi,et al.  IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .

[9]  P Pieter-Jan Hoes,et al.  User behavior in whole building simulation , 2009 .

[10]  Yan Da,et al.  Indoor occupant behaviour monitoring with the use of a depth registration camera , 2019, Building and Environment.

[11]  Cathy Turner,et al.  Green Building Performance Evaluation: Measured Results from LEED-New Construction Buildings , 2008 .

[12]  F. Descamps,et al.  A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison , 2014 .

[13]  Da Yan,et al.  Occupant migration monitoring in residential buildings with the use of a depth registration camera , 2017 .

[14]  Elie Azar,et al.  A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings , 2012 .

[15]  David Lee,et al.  ENERNET: Studying the dynamic relationship between building occupancy and energy consumption , 2012 .