Simulating occupants' impact on building energy performance at different spatial scales

Abstract It is not evident that modeling the stochastic nature of occupants' presence and behavior at the building level is as important as at the room level. Given this line of inquiry, the question is: how should occupants be modeled at different spatial resolutions? How much difference does it make to annual building energy use predictions if: (1) industry assumptions or dynamic models are used, and (2) probabilistic or deterministic models are used? This paper explores the reliability of predicting lighting energy use and the ability to quantify uncertainty at different scales. To this end, the impacts of occupancy and lighting and window shade use behaviors on lighting use predictions at various scales are studied. For occupant modeling, stochastic and rule-based models and custom and standard schedules are used. The simulation results indicated that the whole-building lighting energy use predictions with the stochastic models approached a consistent value of 2.3 kWh/m2 with office buildings larger than 100 offices. It shows that the impact of individual occupants diminishes as the office building size increases. This research concludes that the required office building size to provide a good approximation of the population is highly dependent on the occupant modeling approaches. The results reveal that the deterministic models (i.e. rule-based models and custom schedules) can reasonably represent occupants' impact on building energy performance at larger scales.

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

[2]  Ardeshir Mahdavi,et al.  A preliminary study of representing the inter-occupant diversity in occupant modelling , 2017 .

[3]  Ardeshir Mahdavi,et al.  The sensitivity of building performance simulation results to the choice of occupants’ presence models: a case study , 2017 .

[4]  Frédéric Haldi,et al.  A Probabilistic Model To Predict Building Occupants’ Diversity Towards Their Interactions With The Building Enveloppe , 2013, Building Simulation Conference Proceedings.

[5]  Dirk Müller,et al.  Modelling diversity in building occupant behaviour: a novel statistical approach , 2017 .

[6]  Ian Beausoleil-Morrison,et al.  Development and implementation of an adaptive lighting and blinds control algorithm , 2017 .

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

[8]  Kristina Orehounig,et al.  Variability between domestic buildings: the impact on energy use , 2016 .

[9]  Ardeshir Mahdavi.  Patterns and Implications of User Control Actions in Buildings , 2009 .

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

[11]  H. Burak Gunay,et al.  The contextual factors contributing to occupants' adaptive comfort behaviors in offices – A review and proposed modeling framework , 2014 .

[12]  Tarja Häkkinen,et al.  Barriers and drivers for sustainable building , 2011 .

[13]  Ardeshir Mahdavi,et al.  Predicting people's presence in buildings: An empirically based model performance analysis , 2015 .

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

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

[16]  Kevin J. Lomas,et al.  Sensitivity analysis techniques for building thermal simulation programs , 1992 .

[17]  Ian Beausoleil-Morrison,et al.  Development and implementation of a thermostat learning algorithm , 2018 .

[18]  P. Boyce.  Observations of the manual switching of lighting , 1980 .

[19]  Christoph F. Reinhart,et al.  Monitoring manual control of electric lighting and blinds , 2003 .

[20]  Guy R. Newsham,et al.  Do LEED-certified buildings save energy? Yes, but ... , 2009 .

[21]  Darren Robinson,et al.  The impact of occupants' behaviour on building energy demand , 2011 .

[22]  Angela Lee,et al.  The impact of occupants’ behaviours on building energy analysis: A research review , 2017 .

[23]  David J. Spiegelhalter,et al.  Introducing Markov chain Monte Carlo , 1995 .

[24]  William O'Brien,et al.  A preliminary study of occupants’ use of manual lighting controls in private offices: A case study , 2018 .

[25]  Jason Bell,et al.  Machine Learning: Hands-On for Developers and Technical Professionals , 2014 .

[26]  William O'Brien,et al.  In Situ Approaches to Studying Occupants , 2018 .

[27]  Darren Robinson,et al.  Adaptive actions on shading devices in response to local visual stimuli , 2010 .

[28]  D.R.G. Hunt,et al.  The use of artificial lighting in relation to daylight levels and occupancy , 1979 .

[29]  Amir Nezamdoost,et al.  Using a passive design toolset to evaluate low-cost cooling strategies for an industrial facility in a hot and dry climate , 2018 .

[30]  D.R.G. Hunt,et al.  Predicting artificial lighting use - a method based upon observed patterns of behaviour , 1980 .

[31]  Shady Attia,et al.  Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs , 2012 .

[32]  Ian Beausoleil-Morrison,et al.  Measured end-use electric load profiles for 12 Canadian houses at high temporal resolution , 2012 .

[33]  Dirk Saelens,et al.  Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices – a review-based integrated methodology , 2011 .