Where Building Simulation Time Really Goes

Part 5 – Unpacking the 3 T’s – Time

Do we ever have enough time? There’s almost always a mad rush to finish the document set and specifications by the project deadline. Then come the addenda, the errors and omissions, and so on.

For the building simulation team, there are usually only small windows in which to make a big impact on a project. Time can’t be squandered rebuilding model geometry, entering hundreds of assumptions, and hunting for the most current climate data, among many other tasks. Yet that is often where much of the time goes. On new designs, it goes to adjusting geometry or zoning to keep up with design iterations. On existing buildings, it goes to entering the basics from a dated set of plans, or to building a model from drone or site-capture data in another tool and then trying to translate it into the simulation tool. The initial setup is often the most time-intensive part of the modeling process.

Making one person responsible for coordinating all early-stage simulation creates a bottleneck. It also misses the chance for multiple stakeholders to collaborate and understand the impact a single decision can have on the project, and the chance to mentor younger modeling staff on a real project. When the platform runs on a single PC, or only the “expert” can access it, opportunities for mentorship and organizational learning are severely limited. And when simulations take a long time to run (especially on large projects), this kind of collaboration becomes nearly impossible within a real project schedule and budget. Time becomes waste instead of value.

Solving for time – iterate, iterate, iterate

Iteration is an essential part of the design decision-making workflow, and iteration requires time. That means not just time for the iterations to run, but time to evaluate them, consider alternatives and tweaks between them, and weigh them against the desired outcomes. There are hundreds, if not thousands, of these points to iterate on. They can compete with one another and change rapidly, and the data behind them is often unreliable, which makes full automation or fully autonomous AI riskier.

Not all of these outcomes are objective, either. How do you set a KPI for a building’s ability to evoke an emotional response? How far do we trust AI to apply judgment when a KPI isn’t driven by data alone? Crunching lots of data has real value, but it doesn’t tell the whole story.

Combining data with human judgment allows more viable and interesting options to be assessed earlier. Instead of spending lots of time setting up the basics, the team can invest its time in understanding the problem and finding solutions. That goes beyond the 50,000-foot directional view, which is where most early-stage analysis stops.

When we study myriad iterations at once, constrain the outputs to desirable outcomes, and put up guardrails that keep the solutions within the required domain, we solve for time.

Solving for time – talent development

Can we use technology as a teaching tool? There’s growing evidence that reading complex, informational material on screens can reduce comprehension and leave readers more confident than they should be.^[1] Other studies find little or no effect, especially for shorter texts and purposeful, focused use.^[2] While the answer is unclear for now, a brief survey of the available research suggests that how the technology is designed and used matters more than the screen itself.

Talent develops largely through hands-on experience with the work itself, so finding ways to develop talent faster will also improve design and operational outcomes. Screens have taken over much of how we work, from “paper-like” displays to tools that convert hand sketches into digital drawings. Designing tools that accelerate and support human learning is therefore an essential part of solving for time, both today and in the future.

The aim is to expand and deepen the talent pool more quickly, not just to run simulations faster.

^[1] Clinton, V. (2019). “Reading from paper compared to screens: A systematic review and meta-analysis.” Journal of Research in Reading, 42(2), 288–325. https://doi.org/10.1111/1467-9817.12269; Delgado, P., Vargas, C., Ackerman, R., & Salmerón, L. (2018). “Don’t throw away your printed books: A meta-analysis on the effects of reading media on reading comprehension.” Educational Research Review, 25, 23–38. https://doi.org/10.1016/j.edurev.2018.09.003

^[2] Clinton-Lisell, V., & Litzinger, C. (2026). “Decoding digital reading: a network meta-analysis of comprehension across devices.” Education and Information Technologies, 31(6), 1611–1643. https://doi.org/10.1007/s10639-025-13843-8; Lukram, H., Baishya, R., & Chakma, S. (2026). “Screen Time Patterns and Their Impact on Academic Performance: A Prospective Study Among Phase 1 MBBS Students.” Cureus. https://doi.org/10.7759/cureus.101440