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CalTRACK 2.0 Standing Meeting - Shared screen with speaker view
Charlene
23:01
hi phil, could you please explain why bootstrapping helps you get to the ground-truth? thanks
Steve Schmidt
24:09
Also, please explain again why the figures are so far below 100%…?
bpolly
25:34
How are potential elevation differences between site and weather stations handled?
Charlene
26:52
i can follow up with you offline phil. sorry, my mic on my computer isn't working, hard to talk this through just via chat
msherid
27:49
Can you speak to the data quality of the different sites and how that factors into an algorith to provide robust temperature data.
Eliot Crowe
28:29
Follow on from Steve Schmidt's question - can you articulate what "56%" denotes exactly. I'm still trying to get my head around this metric
Steve Schmidt
43:37
I object!
Eliot Crowe
43:45
mind: blown
bpolly
44:05
Perhaps the results are senstive to the particular ratio of weather dependent load versus weather indepenendent loads for this site
Eliot Crowe
52:23
Question on the weather dependency: results appear to show low sensitivity to using non-local weather for training and prediction period. How about the impact if you want to normalize results to local TMY data? Might be worth some follow up
bpolly
54:37
How do you handle cases where the balance point is on the extreme of the search range?
mchhabra
59:33
I am a little confused about the conclusions and recommendations of the baseline period test to determine impact of baseline measurement period on normalized baseline energy use. Is there more detail on your analysis than what is provided in Github?
msherid
01:02:02
Have you looked at the correlation between values bunched at the extremes of the balance point ranges and loads with little to no temperature sensitivity.
bpolly
01:19:33
By "R-square" you do you mean "adjusted R-square"?
Steve Schmidt
01:21:23
As a P4P program implementer, we do not want any homes “disqualified” by CalTRACK. I think that’s pretty obvious.