The building performance gap: Are modellers literate?

One of the most discussed issues in the design community is the performance gap. In this research, we investigate for the first time whether part of the gap might be caused by the modelling literacy of design teams. A total of 108 building modellers were asked to comment on the importance of obtaining and using accurate values for 21 common modelling input variables, from U-values to occupancy schedules when using dynamic simulation to estimate annual energy demand. The questioning was based on a real building for which high-resolution energy, occupancy and temperature data were recorded. A sensitivity analysis was then conducted using a model of the building (based on the measured data) by perturbing one parameter in each simulation. The effect of each perturbation on the annual energy consumption given by the model was found and a ranked list generated. The order of this list was then compared to that given by the modellers for the same changes in the parameters. A correlation analysis indicated little correlation between which variables were thought to be important by the modellers and which proved to be objectively important. k-means cluster analysis identified subgroups of modellers and showed that 25% of the people tested were making judgements that appeared worse than a person responding at random. Follow-up checks showed that higher level qualifications, or having many years of experience in modelling, did not improve the accuracy of people’s predictions. In addition, there was no correlation between modellers, with many ranking some parameters as important that others thought irrelevant. Using a three-part definition of literacy, it is concluded that this sample of modellers, and by implication the population of building modellers, cannot be considered modelling literate. This indicates a new cause of the performance gap. The results suggest a need and an opportunity for both industry and universities to increase their efforts with respect to building physics education, and if this is done, a part of the performance gap could be rapidly closed. Practical application : In any commercial simulation, the modeller will have to decide which parameters must be included and which might be ignored due to lack of time and/or data, and how much any approximations might perturb the results. In this paper, the judgment of 108 modellers was compared against each other. The results show that the internal mental models of thermal modellers disagree with one another, and disagree with the results of a validated thermal model. The lessons learnt will be of great utility to modellers, and those educating the next generation of modellers.

[1]  Shady Attia,et al.  "ARCHITECT FRIENDLY": A COMPARISON OF TEN DIFFERENT BUILDING PERFORMANCE SIMULATION TOOLS , 2009 .

[2]  Shady Attia,et al.  Simulation-based decision support tool for early stages of zero-energy building design , 2012 .

[3]  V. Carrington.  Literacy in the New Media Age , 2005 .

[4]  Nigel Holt,et al.  Research with People , 2009 .

[5]  Tim Dwyer.  Knowledge is Power: Benchmarking and prediction of building energy consumption , 2013 .

[6]  Ian Walker,et al.  Exploring Mental Representations of Home Energy Practices and Habitual Energy Consumption , 2014 .

[7]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[8]  Arno Schlueter,et al.  Building information model based energy/exergy performance assessment in early design stages , 2009 .

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

[10]  Nigel Holt,et al.  Research with People: Theory, Plans and Practicals , 2009 .

[11]  Matthew Brooke-Peat,et al.  Bridging the domestic building fabric performance gap , 2016 .

[12]  Jim Webster,et al.  Integration of building simulation into the design process of an architectural practice , 2001 .

[13]  F. Sani.  When Subgroups Secede: Extending and Refining the Social Psychological Model of Schism in Groups , 2005, Personality & social psychology bulletin.

[14]  Vitor Sousa,et al.  A framework for combining risk-management and performance-based building approaches , 2010 .

[15]  Darren Robinson,et al.  On the behaviour and adaptation of office occupants , 2008 .

[16]  Terry Williamson.  Predicting building performance: the ethics of computer simulation , 2010 .

[17]  D. van Knippenberg,et al.  Organizational identification after a merger: a social identity perspective. , 2002, The British journal of social psychology.

[18]  C. Reinhart,et al.  DAYLIGHT FACTOR SIMULATIONS – HOW CLOSE DO SIMULATION BEGINNERS ‘ REALLY ’ GET ? , 2009 .

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

[20]  Godfried Augenbroe,et al.  Trends in building simulation , 2002 .

[21]  Shady Attia,et al.  A comparative study of the ‘architect-friendliness’ of six building performance simulation tools , 2010 .

[22]  A. Bryman.  Social Research Methods , 2001 .

[23]  A. Mahdavi,et al.  AN INQUIRY INTO THE BUILDING PERFORMANCE SIMULATION TOOLS USAGE BY ARCHITECTS IN AUSTRIA , 2003 .

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

[25]  Thomas Bednar,et al.  Validation and evaluation of total energy use in office buildings: A case study , 2012 .

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

[27]  Jolanda Jetten,et al.  Playing the game: when group success is more important than downgrading deviants , 2007 .