Since I got the tool ready just in time to hold a last mini seminar at my current gig, and a group of former students thankfully declared their willingness to test this out, we’re using Mr.Comfy to spatially map performance results of their past class simulation models. It’s probably going better than I thought it would; the pics below show an interactive mapping session, with the poor person in the hot seat tasked to zip through annual, monthly and hourly results on the fly, and the rest of us asking hard questions à la “Why on earth is it so warm in those particular [insert applicable ones] spaces during winter days?”.
What’s first of all apparent compared to “standard” performance visualization regimes (think Mr.Line Chart) is that we can immediately identify “problem spaces of interest” and dynamically query comfort and energy use scenarios, as in “Now please tell me whether the air temperature is more or less acceptable in the bedrooms during summer nights…”. Having dynamic scheduling certainly helps; folks also immediately appreciated the difference in interpretation between looking at longer time frame averages vs. frequency/percentage of hours condition met plots. “Yes, the average looks fine. No, in fact temperatures are oscillating pretty hard in that building, so not all is dandy.” Having usually looked at less fine-grained-by-default results expressions, I catch myself intermittently being pretty surprised at what I’m seeeing, but that is, in fact, the data. Something to ponder. (go to the next class post)