Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

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

[2]  Tianzhen Hong,et al.  Occupant Behavior: Impact onEnergy Use of Private Offices , 2013 .

[3]  Ardeshir Mahdavi,et al.  Occupants' operation of lighting and shading systems in office buildings , 2008 .

[4]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[5]  S. Goodman.  Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy , 1999, Annals of Internal Medicine.

[6]  Aldomar Pedrini,et al.  A methodology for building energy modelling and calibration in warm climates , 2002 .

[7]  F. James.  Statistical Methods in Experimental Physics , 1973 .

[8]  David E. Claridge,et al.  Calibration Procedure for Energy Performance Simulation of a Commercial Building , 2003 .

[9]  Yi Jiang,et al.  IISABRE: An integrated building simulation environment , 1997 .

[10]  K. Zimmermann,et al.  Overtime Work and Overtime Compensation in Germany , 1999, SSRN Electronic Journal.

[11]  Darrell E. Carr.  Overtime Work: An Expanded View , 1986 .

[12]  Y Motohashi,et al.  Increases in body mass index and waist circumference as outcomes of working overtime. , 1998, Occupational medicine.

[13]  Gang Wu,et al.  Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai , 2007 .

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

[15]  Rongxin Yin,et al.  Energy modeling of two office buildings with data center for green building design , 2008 .

[16]  Yi Jiang,et al.  DeST—An integrated building simulation toolkit Part II: Applications , 2008 .

[17]  P Pieter-Jan Hoes,et al.  Gebruikersgedrag in gebouwsimulaties - van eenvoudig tot geavanceerd gebruikersgedragmodel:een gevoeligheidsanalyse voor het gebruikersgedrag en een onderzoek naar de robuustheid van een gebouw voor de gebruiker , 2007 .

[18]  Tianzhen Hong,et al.  Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data , 2013 .

[19]  Richard E. Brown,et al.  After-hours power status of office equipment in the USA , 2005 .

[20]  Deirdre McCann,et al.  Working time around the world , 2007 .

[21]  Darren Robinson,et al.  Interactions with window openings by office occupants , 2009 .

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

[23]  Yi Jiang,et al.  A new multizone model for the simulation of building thermal performance , 1997 .

[24]  Xiangquan. Zeng,et al.  Working time in transition : the dual task of standardization and flexibilization in China , 2005 .

[25]  Tianzhen Hong,et al.  Building simulation: an overview of developments and information sources , 2000 .

[26]  Tianzhen Hong,et al.  On Variations of Space-heating Energy Use in Office Buildings , 2013 .

[27]  Da Yan,et al.  DeST — An integrated building simulation toolkit Part I: Fundamentals , 2008 .

[28]  Ian Beausoleil-Morrison,et al.  A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices , 2013 .

[29]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .