So the thermal spatial mapping class (already chronicled here, here and here) is more or less a wrap. Which begs the question whether we achieved what we set out to do- mainly to investigate if dynamic spatial visualization of E+ report variables does indeed improve architectural performance cognition during the design process.
The short answer to this is, maybe not surprisingly, a resounding “yes”, but it comes with a few caveats attached- the biggest one being that even if advanced visualizations are available, they exist in an ecosystem of related representations that also accompany the performance design process, e.g. your standard bar chart, daylight mapping etc., and those representations are mutually supportive. It still became rapidly apparent to me that students would more easily understand “classic” charts once they were pointed to “where and what to look for” in the maps- quite a nice result. A post-class survey additionally revealed that 100% of participants felt that they had learned new things about building performance simulation through dynamic spatial visualizations. Full results of all this will be published in an upcoming paper, which I will upload here as soon as I can.
The first class result I’m sharing with you here maps seasonal performance for a house in Waratah Bay, Australia. It’s an interesting one because the house actually exists, and hence student Sophie Barker had quite a good feeling for when simulation results corresponded with reality (and when not). The main goal of the mapping exercise was to visualize annual air temperature comfort ranges and to determine the projected winter night heating energy consumption in bedroom zones. Summer natural ventilation vs. closed building states were also mapped, revealing that as already experienced in the existing structure, summer natural ventilation is enough to keep the main living spaces relatively comfortable. Download the full presentation PDF (13 MB).
What was neat about working with Mr.Comfy in this particular project is its ability to allow designers to think in temporal scenarios and ask specific questions such as “what’s the lowest hourly temperature in the bedroom zones in winter?” or “does it get uncomfortably hot in the living room during summer afternoons?”. That’s the sort of questions designers tend to ask and want answered in a visual fashion; hence I’m pretty happy with the results Sophie obtained in her project.
Another direct result from the class is that I’ve now also put in climate-based daylight mapping capabilities into the tool, since students often requested to be able to map those in a hybrid fashion, too, instead of relying on other software. So here’s the proof of concept blog post for that. Thanks for reading!