Building simulation involves developing a computer model of the building that is then subjected to virtual weather and conditioning profiles, resulting in a theoretical output of building behaviour. The major advantages of simulation are that they enable the analysis of
“situations that are difficult to examine experimentally” (Hitchin, Delaforce & Martin 1993), and that similar buildings can be simulated under a multitude of different conditions relatively quickly. Simulation can indicate how alterations to the building envelope may affect energy consumption under imposed conditions, and can also be used to compare different evaluation methods. It is often used in addition to other techniques, such as long term monitoring or in situ testing. These real-world data collecting areas are often used to validate the simulation program and provide support for conclusions drawn from alternate scenarios being simulated.
34 Simulation software, while powerful, do have their limitations. Each relies on assumptions made within the basic algorithms, and about the treatment of real world cause and effect in the virtual realm. Lomas et al. (1997) used two small, highly understood buildings (a one room construction and a two room construction), and their known element values including actual performance readings under certain conditions, and ran the same simulation through 25 different combinations of software packages and users from around the world. They discovered variability between the simulated and real results, and between the results from different software packages. This highlighted the potential issues in relying on modelling utilising only plan data as a definitive way of assessing actual levels of housing performance.
Validating simulation models is complex and costly (Lomas et al. 1997), and not without problems. Hitchin, Delaforce & Martin (1993) described validation methods for making measurements in the building, and attempting to reproduce them in the model, as “weak”
and providing “little information to the model builder or user of which components of a model are strong, and which are weak”. In this way, Hitchin, Delaforce & Martin (1993) suggest that due to the inherent complexity of building thermal behaviour, simple validations of a building model cannot ensure that changes to the building model resulting in reduced energy use will translate in to real-world energy savings. In addition, Hitchin, Delaforce & Martin (1993) found that:
…the widely-used ‘perfect-mixing’ assumption not only breaks down, but does so in a manner which has serious consequences when predicting transient thermal response: the actual air temperature response is much slower than that predicted.
Despite these shortcomings, either in validating the models or the assumptions the model makes, simulation tools are immensely powerful tools of evaluation, and are widely used for research and determining compliance with building standards.
Use of Simulation Software for Building Evaluation
The more prominent use of building simulation serves to evaluate building performance, rather than test an evaluation method. Simulation for building evaluation purposes is used in two ways: either to evaluate building design to determine if it meets minimum performance standards for compliance, such as NatHERS in Australia, or to use multiple variations to determine how changes to a building may affect performance. These may be
35 physical changes to the thermal shell, such as different materials or orientations, changes to the building’s environment, or changes to occupant behaviour. Used in this way, simulation software demonstrates in a short amount of time, and at minimal cost, how effective different changes to a building can be.
It is important to realise that software used in rating homes under NatHERS are not intended to predict energy use for the home once occupied. The intention is to provide a consistent approach to evaluating design of the thermal envelope, and to do this certain assumptions must be made. It should come as no surprise that homes that are used differently use different levels of energy, but the purpose of the scheme is to evaluate the building. To do this with some measure of consistency for the entire building stock, some standard assumptions regarding comfort levels and behaviour patterns must be made. If done correctly, it would seem using standard assumptions would not be an issue. However, Ambrose et al. (2013) suggest some of these assumptions may not represent reality. This is reflected in the fact that no improvements were observed for summer, despite an improvement in the summer portion of the rating calculation. If this is the case, it is possible that the evaluation inflates the rating of some homes, and deflates the performance of others. Other studies, such as Ryan & Sanquist (2012), also suggest that assumptions surrounding occupant behaviour are potential sources of error in building simulation.
There are many examples of simulation software being used for studying the effects of building alterations, and in some respects this is the staple method for building research.
When evaluating low-cost approaches to overcoming increases in Australia’s energy efficiency standards, Morrissey, Moore & Horne (2011), and McLeod & Fay (2010), designed simulation experiments to showcase low cost influences on the star rating. Morrissey, Moore & Horne (2011) found that across 81 different designs, the standards were easier to reach in smaller homes, and that passive design features (such as optimising orientation) are effective and low cost measures that can assist in reaching these standards. McLeod & Fay (2010), focusing on a case study in Hobart, did not evaluate the effects of differing orientations, but they showed that house types other than standard brick veneer could achieve the same levels of performance for a reduced cost, though they speculate that
“higher levels of thermal performance than the ones presented could be obtained if the floor plan was altered”. In both cases, no real world validation was used, but Morrissey,
36 Moore & Horne (2011) highly recommended it for future research. Other examples of simulated experiments without real world validation include Depecker et al. (2001) determining the effect of building ‘compactness’ on energy use for cold and mild climates, Kim & Moon (2009) evaluating effects of insulation, also in cold and mild climates in the US, and Pan et al. (2012) also evaluating insulation, but in cool, mild and warm climates in control over all variables. Each experiment is repeatable, with individual factors easily isolated. For Morrissey, Moore & Horne (2011) to conduct research on 81 different designs in the real world, creating an environment where all experienced exactly the same conditions, inputs and loads, is near impossible and highly costly.
Additional examples of simulated analysis of either building performance, or effects of building elements on performance include, Gasparella et al. (2011), Krüger & Givoni (2004), Persson, Roos & Wall (2006) and Wang, Chen & Ren (2010).
Combining Simulation with in situ Evaluation
Simulation may also be combined with in situ evaluations, generally for the purposes of validating the modelling software or technique so that it can be used confidently in future research. The processes are straightforward and almost always involve a small test cell. The values of in situ testing and monitoring are in providing information about the thermal characteristics of the materials, and how the building responds under certain stimulus. The thermal information is used by the model, and the conditions then simulated. This is broadly the basis for the BESTEST certification process (Lomas et al. 1997) which was in turn used by Henninger, Witte & Crawley (2004) for evaluating EnergyPlus, and how Hitchin, Delaforce &
Martin (1993) evaluated the shortcomings of parts of the simulation calculation. The empirical validation of AccuRate by Dewsbury (2011) continued this in Australia, and Ambrose et al. (2013) might be said to have used a similar philosophy. However, Ambrose et al. (2013) was not concerned with replicating conditions in a test cell, but with whether efficient building elements rewarded by NatHERS, and represented by higher star ratings,
37 translate into real world performance on the large scale. Other examples include Blomsterberg et al. (1999), Carrillo, Dominguez & Cejudo (2009), Belleri, Lollini & Dutton (2014), and Raftery, Keane & O’Donnell (2011).
Summary
Simulation tools allow for conduct of a significant amount of research in a short time and at minimal cost. They are, with the notable exception of the PassivHaus design tool, the best way of evaluating building design, and therefore catching any design flaws early. They are exceptionally useful when evaluating how different buildings will respond to different stimuli, without requiring a huge number of physical buildings or large amounts of time to monitor them. Getting design right early is a major part of successfully constructing energy efficient housing. Being able to evaluate changes to the building in the design phase reduces the risk of having to retrofit additional energy saving technologies, and saves time and money over the lifetime of the building.
The issue with using simulation is that of the performance gap. Comparisons between designed performance and actual performance show that analysing building design using modelling software underestimates heating and cooling loads. This may undermine efforts to reduce energy use via energy efficiency regulations that rely on a simulation tool for compliance. This may also be a substantive issue for any mandatory disclosure program.
Field testing is therefore a vital part of building evaluation. It would be expected that in situ testing can provide some level of quality assurance that the finished construction has all the characteristics of the design, and can provide some data with which to calibrate the model.