Evaluating diverse patterns of occupant behavior regarding control-based activities in energy performance simulation

Abstract Simulation is recognized as an effective tool for building energy performance assessment during design or retrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performance and a significant portion of this deviation originates from the dynamic nature of occupant behavior. Literature on occupant behavior indicates that occupant behavior is not integrated into building energy performance assessment procedures with appropriate resolution, instead they are accepted as assumed and fixed data sets that usually represent the presence of occupants. This study attempts to evaluate the effect of diverse patterns of occupant behavior on energy performance simulation for office buildings. Diverse levels of sensitivity of occupant behavior on control-based activities such as using lighting apparatus, adjusting thermostat settings, and presence in space are employed through three diverse occupant behavior patterns. These occupancy patterns are correlated with three identical office spaces simulated within a conceptual office building. EDSL Tas is used to run building energy performance simulations. Effects of occupant behavior patterns on simulation outcomes are compared for five sample winter and summer workdays, with respect to heating and cooling loads. Results present findings on how diversity of occupancy profiles influences the consumption outcomes.

[1]  Juan Sebastián Carrizo,et al.  Use of dynamic occupant behavior models in the building design and code compliance processes , 2016 .

[2]  Chuang Wang,et al.  A preliminary research on the derivation of typical occupant behavior based on large-scale questionnaire surveys , 2016 .

[3]  Darren Robinson,et al.  A bottom-up stochastic model to predict building occupants' time-dependent activities , 2013 .

[4]  Darren Robinson,et al.  A generalised stochastic model for the simulation of occupant presence , 2008 .

[5]  David Coley,et al.  Sensitivity analysis of the effect of occupant behaviour on the energy consumption of passive house dwellings , 2013 .

[6]  G. R. Newsham.  Manual Control of Window Blinds and Electric Lighting: Implications for Comfort and Energy Consumption , 1994 .

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

[8]  Stéphane Ploix,et al.  Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings , 2015, ArXiv.

[9]  K. K. Andersen,et al.  Survey of occupant behaviour and control of indoor environment in Danish dwellings , 2007 .

[10]  Cheol-Soo Park,et al.  Correlation between occupants and energy consumption , 2016 .

[11]  Pieter de Wilde,et al.  The gap between predicted and measured energy performance of buildings: A framework for investigation , 2014 .

[12]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

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

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

[15]  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 .

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

[17]  Tianzhen Hong,et al.  Advances in research and applications of energy-related occupant behavior in buildings ☆ , 2016 .

[18]  Rui Neves-Silva,et al.  Stochastic models for building energy prediction based on occupant behavior assessment , 2012 .

[19]  P. Gurian,et al.  Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices , 2015 .

[20]  Ian Richardson,et al.  A high-resolution domestic building occupancy model for energy demand simulations , 2008 .

[21]  Evangelos Grigoroudis,et al.  A multi-objective decision model for the improvement of energy efficiency in buildings , 2010 .

[22]  Sebastian Herkel,et al.  Towards a model of user behaviour regarding the manual control of windows in office buildings , 2008 .

[23]  K. Steemers,et al.  Time-dependent occupant behaviour models of window control in summer , 2008 .

[24]  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 .

[25]  Hongsan Sun,et al.  An occupant behavior modeling tool for co-simulation , 2016 .

[26]  Tatjana Vilutiene,et al.  Modelling the Effect of the Domestic Occupancy Profiles on Predicted Energy Demand of the Energy Efficient House , 2013 .

[27]  Stefano Paolo Corgnati,et al.  Occupant behaviour and robustness of building design , 2015 .

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

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

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

[31]  Chuang Wang,et al.  A generalized probabilistic formula relating occupant behavior to environmental conditions , 2016 .

[32]  Hugo Hens,et al.  Energy consumption for heating and rebound effects , 2010 .

[33]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .