A review of earlier research efforts that focused on the uptake of simulation in building design (de Wilde, 2004) listed a number of plausible barriers to the integration of building simulation in building design.
Recent publications demonstrate that many of these remain in place today. McElroy et al (2007) reiterate that the main issues facing the application of building simulation in design practice are the training of the
‘simulationists’, trust in the accuracy of models, (mis)interpretation of results, and the role of uncertainties. Bazjanaz (2005) reports progress in the development of data exchange (interoperability), but concedes that some of the current software is not
Source: Sheppard Robson, Hufton and Crow
Figure 4.3 Kingspan Lighthouse: Concept sketch (left) and finalized building (right)
compatible or that existing interfaces need fixing. At the same time, Schwede (2007) notes that current simulation tools still view buildings in a simplified manner and do not allow for a full ‘simulative investigation’.
Yet, given the complexity of modern buildings, as described in the previous section, energy modelling needs to be a crucial instrument in the engineering of sustainable buildings. Indeed, it is already used in many projects and now plays a major role in the work of services engineers, energy systems designers and building physics consultants. The energy modelling that specialized firms provide or architectural firms do in house is diverse. It ranges from early conceptual design
support, relying mostly on the expertise and inventiveness of the energy consultant, to detailed simulations in the final stages of the design. In early design the emphasis is on creating meaningful schematic models of the proposed design concept and managing different criteria by which suggested design options can be judged. This work is not well supported by simulation tools; rather, the early stage requires deep insights and expertise supported by mostly simple calculations. In later design stages the emphasis shifts towards deep inspection of all the systems and their dynamic interactions. This requires expert skills in simulation, reflecting a shift from insight informed by simple schematics towards brute force simulations that replicate Figure 4.4 Macro-energy modelling stage for energy code compliance
physical model behaviour as accurately as possible in the computer. The energy simulation models represent the physical behaviour of components and their dynamic interactions, recognizing all intricate phenomena in these interactions. Interpreting the outcomes and aggregating them into meaningful measures that support the dialogue with the design team becomes the crucial expert-driven after-stage of the simulation. Both types of energy modelling and their intermediate manifestations require different types of expert knowledge and, indeed, very different tools to support them. It is often stated that the early conceptual stages lack adequate tool support, which is then always declared a critical deficiency because of the far-reaching consequences of early decisions through ensuing design evolution. This raises the question of where a concerted effort to generate energy models that support conceptual design should focus. The authors of this chapter argue that true conceptual design support requires one to show which design option has a statistically high(er) chance of impacting upon the energy performance of the eventually resulting final design. Very few, if any, of the past research efforts in this area have framed the objective of their research in this way. A renewed effort is therefore needed to predict energy performance as an
approximation of the probability distribution of the relevance of different design options. The underlying energy models that are needed to achieve this will be mostly normative, putting less emphasis on quantification and more on explanation. It should be stressed that the best intermediary between conceptual design decisions and an energy assessment is the trained mind of the energy modelling expert. This puts the burden of better early design support squarely on the shoulders of the educators who stand at the cradle of the emerging guild of ‘energy design modellers’.
In the meantime few, if any, buildings are delivered without a detailed energy model and simulation of the energy consumption of the consumers in the building (heating, cooling, fans, pumps, hot water, lighting, appliances). Yet, at the same time, there are also many instances of buildings not living up to the expectations of the clients and design teams. Often, actual buildings require more energy to run than anticipated during the design stage (Bordass et al, 2001), and complaints about occupant discomfort persist (Karjalainen and Koistinen, 2007).
A couple of observations can be made. First of all, energy simulation models are typically based on
‘idealizations’ that assume that buildings are built and
Figure 4.5 Example of monthly cooling demand calculation with uncertainty
operated according to specification. Moreover, they assume ‘perfect knowledge’ about physical properties, occupant and operator intervention, operating schedules, etc. This is hardly realistic, however, when one realizes that buildings exist in an unpredictable environment, where deteriorating systems, bad workmanship, unforeseen use and adaptations are the rule, rather than the exception.
Researchers have started to look at the role that these uncertainties play in predicting energy performance. Figure 4.5 shows an example of a small study into the effect on monthly cooling demand of a single office space (Augenbroe et al, 2008). As the figure shows, the expected mean average is around 140kWh/month; but the uncertainty ranges from 120 to 165, roughly plus or minus 20 per cent. In this case, only a subset of all uncertainties was taken into account, whereas user behaviour was not included.
Other deficiencies of current energy modelling result from the fact that our models are in some cases only abstractions of real behaviour. Advanced control strategies, for example, can look good on paper and their dynamic simulation can be carried out cleanly.
However, their actual implementation is complex and plagued by all kinds of practical issues such as sensor errors, cycle times, activation delays, etc. Not surprisingly, it can take up to a year to get the building controls to perform close to expectations. In general, it is not uncommon that buildings underperform the predicted energy performance by as much as 30 per cent. The main reason is deviation from the idealized assumptions embedded in the energy model, unexpected circumstances, malfunctioning of system components and bad workmanship. Continuous commissioning is often seen as a way of restoring the energy performance of a building to its expected levels;
but it should be well understood that a large part of the discrepancy may originate from too optimistic assumptions in the first place. Only the explicit modelling of uncertainties in model parameters and model assumptions will reveal the extent (probability) to which the energy model outcomes predict reality.
When looking at the role of energy modelling in building engineering, it is also important to note that the building design process is itself changing. The use of digital media is leading to new approaches to design.
In these approaches, not only do designers work with new ways of representing the developing building
design, but they also develop new ways of generating forms, and they increasingly analyse the performance of their designs (Oxman, 2006). In developing new tools and design systems, it therefore is important to ensure a fit with the cognitive way in which designers work (based on design reasoning and thinking), while at the same time being aware that the design practice (tools, products and process) might change in the future (Kalay, 2006). The current – as of 2008 – status in thermal building engineering practice, as informally conveyed by numerous consultants active in the industry in the UK, is one of buoyancy. Performance requirements for energy efficiency are becoming increasingly stringent, boosting the work volume of the consultancy companies. And with events such as the 2012 London Olympics driving a multitude of construction projects, skilled modelling and simulation workers are in high demand. However, this situation leaves little room for a fundamental review of the practices in the consultancy office and for improving the state of the art from within the industry.