Great conversation everyone.<div><br></div><div>I guess I should have started my discussion by differentiating building design from analysis. I definitely see a place where actual energy predictions and characterization of uncertainty is of prime importance - retrofits.</div>
<div><br></div><div>While designing low energy new buildings is great, if we want to really impact the building energy and carbon footprint we need to retrofit the existing building stock as much as possible.</div><div><br>
</div><div>Since most owners look at this as an investment, we really need to given them information like other investments and provide risk analysis. That means probabilistic energy savings predictions and, when coupled with probabilistic cost and future energy costs, a true probabilistic return on investment and value added to the building. Then owners and financiers can look at a retrofit just like any other investment, and (I hope) pull the trigger on more retrofits than they are currently doing.</div>
<div><br></div><div>I'm not sure of a way to generate real ROI probability without stochastic analysis. The underlying model doesn't necessarily have to reflect the true physics (or even full systems of the building) as long as it captures the monthly energy use right and enough of the physics that when we make changes to the building, the model correctly reflects the energy savings and uncertainty in that savings. </div>
<div><br></div><div><br></div><div><br></div><div><br></div><div><br clear="all">Ralph T Muehleisen<br>PhD, PE, LEED AP, INCE Board Certified, FASA<br>Principal Building Scientist<br>Argonne National Lab<br>9700 S. Cass Ave, Bldg 221<br>
630-252-2547, <a href="mailto:rmuehleisen@anl.gov" target="_blank">rmuehleisen@anl.gov</a><br><br>
<br><br><div class="gmail_quote">On Fri, Jul 27, 2012 at 8:50 PM, Joe Huang <span dir="ltr"><<a href="mailto:yjhuang@whiteboxtechnologies.com" target="_blank">yjhuang@whiteboxtechnologies.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><u></u>
<div text="#000000" bgcolor="#ffffff">
Ralph, others,<br>
<br>
I think it all depends on the purpose of the simulation. Leaving
aside the issue of design sizing for the moment, use of energy
simulations to predict, or more often, match observed energy usage
is typically concerned with annual or long-term energy usage. In
that case, stochastic variations around a mean doesn't provide much
useful information, because the concern is not variability, but
bias, in the results. It reminds me of when I saw that the
uncertainty given for the modeled solar illuminance in the TMY2
weather files was less than 3%. That seemed wrong to me, since I
know that for any particular hour the differences between modeled
and measured solar could be quite large. However, when I read the
documentation, it made sense because the uncertainty indicates not
the stochastic variability, but the "mean bias error", i.e.,
systematic variations, between the modeled and measured solar. <br>
<br>
How does that relate to the value of stochastic modeling? If we're
only concerned in getting the annual totals to match, then whether
or not we capture the stochastic variations from hour to hour seems
to be minor importance. I'm also having difficulties in understand
what<br>
additional information is output from "stochastic modeling", except
having error bars on each hour, which undoubtedly will be large.<br>
<br>
My last comment is that although weather patterns are stochastic,
the data on the weather files is quite deterministic, if we allow
that the weather stations are doing a reasonably good job in
measuring temperatures, wind speeds, etc. The question of adding
precision to the DOE-2 weather files is not an issue of stochastic
behavior, but simply that of round-off errors.<div class="im"><br>
<br>
Joe<br>
<pre cols="90">Joe Huang
White Box Technologies, Inc.
346 Rheem Blvd., Suite 108D
Moraga CA 94556
<a href="mailto:yjhuang@whiteboxtechnologies.com" target="_blank">yjhuang@whiteboxtechnologies.com</a>
<a href="http://www.whiteboxtechnologies.com" target="_blank">www.whiteboxtechnologies.com</a>
(o) <a href="tel:%28925%29388-0265" value="+19253880265" target="_blank">(925)388-0265</a>
(c) <a href="tel:%28510%29928-2683" value="+15109282683" target="_blank">(510)928-2683</a>
"building energy simulations at your fingertips"
</pre>
<br></div><div><div class="h5">
On 7/27/2012 1:42 PM, Ralph Muehleisen wrote:
<blockquote type="cite">
<div>Chris, Joe, others,</div>
<div><br>
</div>
The dramatic effects of simply rounding off/ceiling the
temperature data in weather files is a perfect example of why we
need to, as an industry, move from deterministic energy
predictions to stochastic energy predictions.
<div>
<br>
</div>
<div>Weather and occupancy are inherently stochastic and when we
couple that stochasticity with our uncertainty in so many of the
actual building parameters (at least uncertainty as to their
what their values will be when installed), it seems to me that
far more meaningful energy predictions could be made using
stochastic methods. </div>
<div><br>
</div>
<div>While you wouldn't necessarily want to use stochastic methods
during much of the design iteration, stochastic estimation
should be a standard procedure for comparing major iterations
and for final energy predictions. </div>
<div><br>
</div>
<div>For all us researchers, there is plenty of work to be done in
properly quantifying the stochasticity of weather, occupancy and
other stochastic parameters and in developing uncertainty
profiles for important parameters that are not stochastic.
There is also opportunities for industry to develop the
wrappers to take the info and create the DOE2 wrappers.</div>
<div><br>
</div>
<div>Am I alone on this or do others feel the same way?</div>
<div>
<div><br>
Ralph T Muehleisen<br>
PhD, PE, LEED AP, INCE Board Certified, FASA<br>
Principal Building Scientist<br>
Argonne National Lab<br>
9700 S. Cass Ave, Bldg 221<br>
<a href="tel:630-252-2547" value="+16302522547" target="_blank">630-252-2547</a>, <a href="mailto:rmuehleisen@anl.gov" target="_blank">rmuehleisen@anl.gov</a><br>
<br>
<br>
<br>
<div>On Thu, Jul 26, 2012 at 2:31 PM, Joe Huang <span><<a href="mailto:yjhuang@whiteboxtechnologies.com" target="_blank">yjhuang@whiteboxtechnologies.com</a>></span>
wrote:<br>
<blockquote>
<div> Chris, others,<br>
<br>
Since as you've raised the question of how significant
would be adding extra precision to the weather data in
DOE-2, I was sent a copy of a recent paper by
Annie-Claude Lachapelle of the Univ. of Calgary given at
eSim Canada 2012 on this exact topic, "DOE2 Dry-Bulb
Temperature Precision Level Impact on Sensible
Economizer Performance". With the author's permission,
I've attached the paper with this post. <br>
<br>
Joe<br>
<pre>Joe Huang
White Box Technologies, Inc.
346 Rheem Blvd., Suite 108D
Moraga CA 94556
<a href="mailto:yjhuang@whiteboxtechnologies.com" target="_blank">yjhuang@whiteboxtechnologies.com</a>
<a href="http://www.whiteboxtechnologies.com" target="_blank">www.whiteboxtechnologies.com</a>
(o) <a href="tel:%28925%29388-0265" target="_blank">(925)388-0265</a>
(c) <a href="tel:%28510%29928-2683" target="_blank">(510)928-2683</a>
"building energy simulations at your fingertips"
</pre>
<br>
On 7/25/2012 10:45 AM, Joe Huang wrote:
<blockquote type="cite">Chris, <br>
<br>
My attention on this issue was first raised about 15
years ago when I was working with non-US weather data
, i.e., the rest of the world, that are all reported
in 0.1 C. I've noticed since that US stations have
also moved to the use of metric units, i.e., 0.1 C for
temperature. The DOE-2 weather format is still in
integer F, which leads to three unfortunate effects:
(a) hourly records can be off by as much as 0.5 F, (b)
clumping of the temperature distribution, and (c)
statistics such as degree-days will be off by a
percent or two compared to the original data. Now, one
can say that all this is immaterial in the bigger
picture of things, which has been the default attitude
so far, but since it's really quite simple to fix, why
not get it right, i.e., doesn't it feel much better to
see the same temperatures in the DOE-2 outputs as in
the original weather data? <br>
<br>
BTW, all the weather data that I've looked at are
records of conditions on the hour, not the average
over the hour, except for solar radiation. <br>
<br>
Joe <br>
<br>
<br>
On 7/25/2012 9:53 AM, Chris Jones wrote: <br>
<blockquote type="cite">Joe <br>
Given that the time steps are an hour, and the fact
that weather data is averaged over an hour, plus the
fact that the building local will have variations
from the weather station local, would an extra
decimal point provide more useful information? <br>
<br>
<br>
At 07:43 AM 25/07/2012, Joe Huang wrote: <br>
<blockquote type="cite">This is not possible at
present without changing the DOE-2.2 source code
to read a weather input file with decimal values.
When DOE-2 was first designed in the early 1980's,
memory was a big concern, so the weather data was
reduced to integers and then packed, which is why
the DOE-2 *.BIN file is so small (146K). I have
actually developed a modified file format for
*.BIN where I save an extra digit of precision,
i.e., temperatures to 0.1F instead of 1 F, but the
source code would also need to be changed slightly
to read this extra information. I've mentioned
this to the developer of eQUEST/DOE-2.2 and will
be experimenting with making this change to the
source code. If and when it's proven to work and
gets incorporated into DOE-2.2, I'll let everyone
know. I welcome anyone who thinks this is a useful
modification to send me an e-mail. It might spur
me on to do something! Joe On 7/23/2012 2:44 AM,
è”¡æ˜€èŠ wrote: > Hi, everyone: > > We
know that eQUEST can edit personal weather data.
> But the dry-bulb and wet-bulb temperature in
weather data can only > enter integers. > Is
it possible to have more precise temperature to
decimal place? > Thank you very much. > >
>
_______________________________________________
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Rheem Blvd., Suite 108D Moraga, CA 94556 (o) <a href="tel:%28925%29388-0265" target="_blank">(925)388-0265</a>
(c) <a href="tel:%28510%29928-2683" target="_blank">(510)928-2683</a> <a href="http://www.whiteboxtechnologies.com" target="_blank">www.whiteboxtechnologies.com</a>
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</blockquote>
<br>
>> <br>
Christopher Jones, P.Eng. <br>
Suite 1801, 1 Yonge Street <br>
Toronto, ON M5E1W7 <br>
Tel. <a href="tel:416-203-7465" target="_blank">416-203-7465</a> <br>
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