From the Design Quarterly: Digitally modeling cost-effective sustainable solutions
September 11, 2019
September 11, 2019
Using the latest in digital tools, designers and engineers can achieve sustainable building performance on a budget
Designing for sustainability today is all about achieving return on investment. Today’s design decisions reflect a web of interactions that are far too complex to represent and manipulate in two dimensions. Recent innovations in digital modeling tools allow designers, engineers, and consultants to quantify complicated dependencies and represent them more accurately.
When used skillfully, these tools become the platform in which design teams compare different solutions not only for energy, thermal comfort, or daylighting performance but for initial investment, capital, operational, and life-cycle cost. These tools make it possible for designers to dial in the appropriate and most cost-effective sustainable strategies early in the design process.
Today, parametric whole building simulations (or whole-building energy simulations if you like) enable designers to find the most cost-effective solutions for achieving energy-efficient or low-carbon buildings. These digital tools have advanced and are becoming more integrated, enabling us to do parametric energy simulations anywhere we do energy modeling on a project.
With whole-building modeling, we work backwards from the targets—usually energy performance and cost—to find that most effective solution to meet a specific target. We look at cost to determine available solutions.
Parametric analysis and tools can represent the optimal solution out of thousands of possibilities in multidimensional visual form.
What metric are we targeting for sustainability? In the Passive House approach, the targets are thermal energy demand and total energy use intensity. If we are targeting carbon intensity, we would design around a certain greenhouse gas emissions target, or zero emissions over a year.
With those targets in mind, we can simulate all combinations of all the design features that are on the table. And then combine that information with cost differences, not just initial cost but also maintenance, life expectancy of equipment, and associated end-of-life costs. All those metrics can be figured into our model. Then you can pick the performance metric that the building needs to meet at the lowest total cost.
For a developer, that might mean looking at total capital cost investment. While institutions that own and operate buildings are interested in total cost of ownership over time.
But this isn’t just a spreadsheet that spits out a number. It’s visual.
Parametric analysis and tools can represent the optimal solution out of thousands of possibilities in multidimensional visual form. We can see what’s possible in a building within the numbers we’ve been provided. This makes for a well-informed design decision.
If you’re planning to use the same HVAC system as last time, why spend time and money modeling and looking at engineering options early in design? A waste of time?
On a recent project, we convinced the team to allow us to explore different HVAC options in terms of energy, area needed on plant rooms and core, and cost and the different performance of each system in relation to different façade options (window wall ratio, glass solar heat gain coefficient, external shading, daylighting, and R-value) due to a change on the energy code cycle. The modeling results showed that if we kept the same HVAC system as before, the project would require a pricey triple glass façade with darker glass to hit targets. Modeling revealed another option: decouple ventilation and use water-based internal units that would save on costs and energy use.
In another case, a design team was looking at incorporating solar thermal panels to produce hot water on a multi-residential tower in the Northwest. However, after modeling the project and analyzing different systems we deemed it more energy- and cost-efficient to incorporate air-source heat pumps driven by photovoltaic panels. This changed the overall mechanical system, reduced plumbing systems, and achieved project goals.
Read and download the Design Quarterly Issue 06 | Destination Zero
Employing these tools is a form of digital collaboration—one that’s most effective if it’s done from day one.
Projects benefit greatly from creating models and solar analysis early, so that decisions are informed by the data, not just gut feelings, personal preferences, or business as usual. For this reason, a feasibility study for sustainability is worth the time and expense. It can be a challenge, but increasingly projects need to invest more in concept development to reap the rewards of energy modeling.
The digital tools used in energy modeling are beginning to function like the tools for architectural parametric simulations. Designers using Rhino, Grasshopper, and plug ins for parametric architecture can not only test form and massing of buildings, they can connect to energy modeling software like Energy+. We can run through a wide range of features of a design concept in Rhino—that’s where engineering performance and architecture are coming together in the early stages.
Based on an academic paper that proved the concept, Stantec customized free, open-source software to create our own parametric energy-modeling software, which we have utilized on projects such as evolv1, Canada’s first Net Positive Energy design certified, commercial building.
At early stages of design, we may look at weather data with Climate Consultant. We might use Sefaira Architecture in concept design to provide an early reaction to some design gestures and check early intuitions. We also like to compare façade performance not only on the context of annual energy demands or daylight penetration but peak loads, solar radiation, or glare. For more advanced analysis, we utilize tools like Autodesk FenestraPro, Diva for Rhino. For detailed analysis, once most systems and components have been decided, we might use Open Studio for Energy+ or Therm. Finally, for renewable feasibility we might employ PVWatts for information on decisions around solar or to design a full PV array.
Pushing performance for buildings requires an artful balancing act between lofty goals and budget reality. But there’s another aspect to digital modeling that may hold the key to affordable low-carbon buildings—industrial manufacturing methods. If we apply our model and manufacture components as we do automotive components, we tend to find that that prefabricated, modular components are higher quality and lower cost than conventional components.
For clarity, this doesn’t mean cookie-cutter mass production. This kind of bespoke manufacturing also allows us to make buildings with thousands of unique façade panels. Digitally designed pre-fab components could mean more affordable high-performance buildings.