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I don't think the primary cause of discrepancy between simulations
and measured data are the algorithms (although they do play a part),
but much more the input assumptions. I also agree that more
calibration should be done, not so much to tune an individual model,
which can easily be taken to a meaningless excess, but more to gain
insight into which input assumptions or defaults are most
questionable and need revision of the "conventional wisdom". A case
in point is how much shading from drapes, blinds, and other
obstructions should be assumed? For many years, I've seen and used
a SHADING-FRACTION of 0.60, i.e., 40% of the incident solar is being
obstructed, but is that really the best average value ? (this will
likely vary by simulation program).<br>
<br>
When this issue of how reliable are simulations was raised at a
DOE-sponsored workshop in Jan. 2012 following the ASHRAE conference
in San Antonio, and someone put up the proverbial plot of
simulations vs measured for LEED buildings showing a huge cloud, I
had suggested that we should tackle the calibration process in
steps:<br>
1. original ASHRAE-90.1 model vs measured<br>
2. 1 from above corrected to as built and operated (including
actual weather!) vs measured<br>
3. 2 from above with end-use metered data vs measured, plus
tuning the model<br>
1 would be what we're doing now. 2 is eminently doable by onsite
visit to the actual building. 3 would require much more
instrumentation and manpower, and only doable as a research project.
<br>
What I would like to see is how much do the correlations improve
going to 2 and then 3. My gut feeling is that the standard
deviations would be halved each time, i.e., from 40% to 20% to 10%.<br>
<br>
I've actually thought of turning this idea into an ASHRAE WS (RFP),
and might still do it, time and energy permitting. What I envision
is for some contractor to get a healthy number of ASHRAE 90.1 models
(~30-40), possibly submitted for LEED, and then apply the same
methodology to get 2, and pick a few (~5) that would be monitored
for 3. If there are any ASHRAE members or friends interested to
flush out such a WS, please send me an e-mail. <br>
<br>
Joe (currently Chair ASHRAE TC 4.7 on Energy Calculations)<br>
<pre class="moz-signature" cols="90">Joe Huang
White Box Technologies, Inc.
346 Rheem Blvd., Suite 205A
Moraga CA 94556
<a class="moz-txt-link-abbreviated" href="mailto:yjhuang@whiteboxtechnologies.com">yjhuang@whiteboxtechnologies.com</a>
<a class="moz-txt-link-freetext" href="http://weather.whiteboxtechnologies.com">http://weather.whiteboxtechnologies.com</a> for simulation-ready weather data
(o) (925)388-0265
(c) (510)928-2683
"building energy simulations at your fingertips"
</pre>
<div class="moz-cite-prefix">On 5/27/2015 2:34 PM, Justin Spencer
wrote:<br>
</div>
<blockquote
cite="mid:CAB+zL_nppzsxhu=epvNc7DyWn5qnRrcB11-oE9c59QNvOqC0aw@mail.gmail.com"
type="cite">
<meta http-equiv="Context-Type" content="text/html; charset=UTF-8">
<div dir="ltr">Models are never going to agree, but that doesn't
mean they aren't powerful tools for decision-making. Look at the
ASHRAE 140 results for highly simplified geometries with
different engines. Things easily vary by 20%. Hell, things vary
by 20% just between some versions of the same tool. I didn't see
it mentioned that the underlying models just aren't going to
give you the "right" answer.
<div><br>
</div>
<div>That's why we need to calibrate. The models are not
generally actually calculating things based on first
principles. They use shortcuts for computation time and ease
of specification everywhere. That's why it's no less real for
me to impose some sort of somewhat goofy setpoint schedule to
get things to work out well in the calibration of my model.
Ideally, we'd calibrate to one set of bills and then run it
against a second set of bills to see if we over-calibrated the
models (the same as over-fitting a regression model). Instead
of arguing whether or not the models are right, we should be
going back to the fundamental question of whether or not the
models provide useful decision-making information, i.e.
whether or not they get the first order impacts right for
different kinds of design choices in new construction.
Remember that uncertainty around each point and move ahead
making the design decisions you need to make. </div>
<div><br>
</div>
<div>There's still plenty of room to develop better analysis
practices that will improve the design decisions we make. I
would love to see a study looking at normalized consumption
and key characteristics of 5-year-old commercial buildings,
and then again at 10-year-old commercial buildings. You could
do this with LEED submittals and regress against the ECMs
included and other characteristics. You wouldn't necessarily
get to the why of how the buildings failed to deliver without
a lot of extra work, but I think you could get the what just
by looking at bills and data. You could also do this on the
residential side. We have some utility clients who run
commercial or residential new construction programs.
Theoretically, we could get bills and characteristics for a
large number buildings and regress. I know that would work on
the residential side. On the commercial side, I don't know if
there is enough participant data. Others should consider
doing the same. It would be a fascinating study and would make
for a great energy nerd parlor game. Which of these measures
just didn't deliver? </div>
<div><br>
</div>
<div>My gut says that failures in fancy control systems are to
blame for the most egregious differences. There are so many
failure points -- sensors, valves, dampers, actuators, not to
mention the likelihood of a mistake in the controls sequence.
You need somebody on top of monitoring for each of those
potential failure points and correcting them. My gut also says
that good solid basic design will deliver the savings. Cutting
down on west-facing solar gains will save cooling energy.
Installing more efficient HVAC equipment, given solid ratings
information, will also work. More efficient lighting designs
will work. Really fancy stuff will fail some of the time. And
some of the time it will work! And we need to celebrate those
pieces that work! </div>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Wed, May 27, 2015 at 3:02 PM, Fred
Betz <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:fbetz@aeieng.com" target="_blank">fbetz@aeieng.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote">Just catching up with this
thread so I'm sorry if I'm repeating anything that's already
been mentioned.<br>
<br>
Take a look at the paper from Pam Berkeley et al published
last year at SimBuild.<br>
<a moz-do-not-send="true"
href="https://www.ashrae.org/membership--conferences/conferences/ashrae-ibpsa-usa-papers"
target="_blank">https://www.ashrae.org/membership--conferences/conferences/ashrae-ibpsa-usa-papers</a><br>
<br>
10 experienced energy modelers modeling the same building in
a 3hr period. Fascinating results.<br>
<br>
<br>
There are emerging methods to do more rigorous QC using a
variation on Monte Carlo for energy models to calculate a
confidence interval for the model rather than fully rely on
modeler experience and 3rd party QC. Georgia Tech has
integrated this into their version of e+, which I hope gets
integrated into a future version of e+. It's computationally
intense so cloud computing is probably the right way to do
this, which I believe e+ is heading towards.<br>
<br>
<br>
Fred<br>
<br>
<br>
FRED BETZ PhD., LEED AP ®BD+C<br>
SENIOR SUSTAINABLE<br>
DESIGN CONSULTANT<br>
<br>
AEI | AFFILIATED ENGINEERS, INC. <br>
5802 Research Park Blvd. | Madison, WI 53719<br>
<br>
P: <a moz-do-not-send="true" href="tel:608.236.1175"
value="+16082361175">608.236.1175</a> | F: <a
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value="+16082382614">608.238.2614</a> <br>
<a moz-do-not-send="true" href="mailto:fbetz@aeieng.com">fbetz@aeieng.com</a>
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target="_blank">www.aeieng.com</a> <br>
<div class="HOEnZb">
<div class="h5"><br>
<br>
-----Original Message-----<br>
From: Eurek, John S NWO [mailto:<a
moz-do-not-send="true"
href="mailto:John.S.Eurek@usace.army.mil">John.S.Eurek@usace.army.mil</a>]<br>
Sent: Wednesday, May 27, 2015 11:28 AM<br>
To: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: Re: [Bldg-sim] Comparing ASHRAE 90.1 App G
Models to Real Buildings (UNCLASSIFIED)<br>
<br>
Classification: UNCLASSIFIED<br>
Caveats: NONE<br>
<br>
This is a slightly different question:<br>
How close do you expect 2 energy models to be created by
2 different modelers (using the same program) if you
give them the same plans and information?<br>
<br>
(Ask Pablo Picasso, Salvador Dali and Rembrandt to draw
a tree)<br>
<br>
I assume most companies don't double up on the energy
modeling efforts which would show how consistent or
non-consistent energy models are. (assuming the energy
modelers are experienced and competent.)<br>
<br>
Somebody who teachers energy modeling may be able to
provide insight and good examples.<br>
<br>
As far as an energy model matching the actual utilities
bills..... If you have a 1000 modelers, making models on
1000 computers for 1000 years......<br>
<br>
<br>
<br>
-----Original Message-----<br>
From: Bldg-sim [mailto:<a moz-do-not-send="true"
href="mailto:bldg-sim-bounces@lists.onebuilding.org">bldg-sim-bounces@lists.onebuilding.org</a>]
On Behalf Of Jacob Dunn<br>
Sent: Friday, May 22, 2015 2:37 PM<br>
To: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: [EXTERNAL] Re: [Bldg-sim] Comparing ASHRAE 90.1
App G Models to Real Buildings<br>
<br>
Thank you all for your thoughtful insight on this
matter! It’s an important debate – both in
understanding the capabilities/limitations of our craft
as energy modelers, but also to communicate our value to
the community at large.<br>
<br>
<br>
<br>
To clarify slightly, my question revolved around how the
specific modeling protocol of Appendix G could account
for the “performance” gap between modeled and actual
use. Thus, the fact that buildings aren’t operated as
the energy model specified and the lack of building
commissioning, while true and important, are not
inherent to the intent of Appendix G modeling. The most
interesting question is, “If you model a LEED App G
model perfectly according to protocol, AND the building
was operated according to the modeled schedules, will it
predict the right number?” Lots of your responses lent
insight into this question, thanks again!<br>
<br>
<br>
<br>
I’ve revised my list below based on your responses:<br>
<br>
<br>
<br>
Added:<br>
<br>
- Plug load values are assumed, which can have a huge
impact on overall energy (Thanks Christoph and Chris
Hadlock for the insight)<br>
<br>
- Insulation values are largely specified without
thought to thermal bridging<br>
<br>
- HVAC controls simulation is often simplified<br>
<br>
- Performance curves are often not simulated due to
increased effort and unavailability of performance data
from manufacturers<br>
<br>
<br>
<br>
Original:<br>
<br>
- Appendix G does not take into account
external shading, which can be critical in urban
environments for accurate energy predictions<br>
<br>
- Schedules are typically not created with the
intent of being predictive. Overall building hours are
adhered to, but detailed schedule creation is not
usually in the scope of a LEED model (or is it, in your
experience?). For instance, typical plug load base
values during unoccupied times are .3, this is a pretty
big assumption.<br>
<br>
- The App G model uses a TMY weather file,
which can vary from the current weather year (I wonder
on average by how much?)<br>
<br>
- Infiltration values are assumed, unless
blower door testing has been done (which is rare for
commercial buildings).<br>
<br>
- Thermostat values are modeled as consistent
across the building, which is rarely the case in an
actual operating building<br>
<br>
Cheers,<br>
<br>
<br>
<br>
Jacob Dunn LEED AP BD+C<br>
<br>
<br>
<br>
ESKEW+DUMEZ+RIPPLE, APC<br>
<br>
2014 AIA National Architecture Firm Award<br>
<br>
<br>
<br>
365 Canal Street Suite 3150<br>
<br>
New Orleans LA 70130<br>
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<br>
<br>
<br>
From: Bldg-sim [mailto:<a moz-do-not-send="true"
href="mailto:bldg-sim-bounces@lists.onebuilding.org">bldg-sim-bounces@lists.onebuilding.org</a>]
On Behalf Of Chris Hadlock<br>
Sent: Friday, May 22, 2015 1:08 PM<br>
To: Christoph Reinhart<br>
Cc: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: Re: [Bldg-sim] Comparing ASHRAE 90.1 App G
Models to Real Buildings<br>
<br>
<br>
<br>
All,<br>
<br>
<br>
<br>
I would agree with all of the factors mentioned that
absolutely can result in deviations between actual and
modeled building performance. I would also echo the
sentiment that following modeling rules shouldn't
necessarily preclude us from attempting to better
predict actual building performance through the LEED
process. Applying careful attention to important details
and a healthy dose of experience (bringing together real
life building performance knowledge as it relates to the
grey areas - namely schedules, equipment controls,
occupant behavior, etc) can truly help close the gap. At
the end of the day, a rating system should be attempting
to reward buildings that actually perform well, not
theoretically perform well (and as modeler's we should
take a leading role in making good (i.e. fair)
assumptions).<br>
<br>
<br>
<br>
My colleague (Janine Vanry) has recently completed
research (to be published soon) for her masters thesis
at the University of Waterloo (Ontario, Canada) which
studied how LEED certified academic buildings in
southwestern Ontario performed in comparison to
government energy intensity benchmarks, campus-wide
energy intensities, and in general how LEED (modeled)
results compare to actual building performance (as
measured through M&V). Consistent with Dr
Samuelson's (et al.) research findings, the
discrepancies between the modeled results and the actual
energy intensities showed that there was an
under-prediction anywhere from 2% to 44%.<br>
<br>
<br>
<br>
While energy modeling professionals understand (as is
evident by this thread) that there will be differences
between the documented EAc1 energy savings and actual
building energy usage, this isn't always communicated
and understood by the building owners and the
professionals we work with.<br>
<br>
<br>
<br>
Chris<br>
<br>
<br>
<br>
On Thu, May 21, 2015 at 10:56 AM, Christoph Reinhart
<<a moz-do-not-send="true"
href="mailto:tito_@mit.edu">tito_@mit.edu</a>>
wrote:<br>
<br>
Dear Jacob,<br>
<br>
<br>
<br>
This is an eternal debate and there are many
reasons for moving away from the use of 90.1 Appendix G
to evaluate the performance of a building designs. To
answer your question directly, we worked a few years ago
with Enermodal in Canada on a comparison between design
phase building energy models (BEM) prepared for LEED
Canada certification (slightly different to Appendix G)
to calibrated BEM and measured energy use for 18
buildings. The main findings are quoted below:<br>
<br>
<br>
<br>
Analysis of a Simplified Calibration Procedure
for 18 Design-Phase Building Energy Models<br>
<br>
H W Samuelson, A Ghorayshi and C F Reinhart<br>
<br>
Journal of Building Performance Simulation, DOI:
<a moz-do-not-send="true"
href="http://dx.doi.org/10.1080/19401493.2014.988752"
target="_blank">http://dx.doi.org/10.1080/19401493.2014.988752</a>
<<a moz-do-not-send="true"
href="http://dx.doi.org/10.1080/19401493.2014.988752"
target="_blank">http://dx.doi.org/10.1080/19401493.2014.988752</a>><br>
<br>
This paper evaluates the accuracy of 18
design-phase building energy models, built according to
LEED Canada protocol, and investigates the effectiveness
of model calibration steps to improve simulation
predictions with respect to measured energy data. These
calibration steps, applied in professional practice,
included inputting actual weather data, adding
unregulated loads, revising plug loads (often with
submetered data), and other simple updates. In sum, the
design-phase energy models underpredicted the total
measured energy consumption by 36%. Following the
calibration steps, this error was reduced to a net 7%
underprediction. For the monthly energy use intensity
(EUI), the coefficient of variation of the root mean
square error improved from 45% to 24%. Revising plug
loads made the largest impact in these cases. This step
increased the EUI by 15% median (32% mean) in the
models. This impact far exceeded that of calibrating the
weather data, even in a sensitivity test using extreme
weather years.<br>
<br>
<br>
<br>
Best,<br>
<br>
<br>
<br>
Christoph<br>
<br>
Christoph Reinhart<br>
<br>
Associate Professor<br>
<br>
Department of Architecture<br>
<br>
Massachusetts Institute of Technology<br>
<br>
77 Massachusetts Ave, Rm 5-418, Cambridge, MA
02139, USA<br>
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From: Bldg-sim [mailto:<a moz-do-not-send="true"
href="mailto:bldg-sim-bounces@lists.onebuilding.org">bldg-sim-bounces@lists.onebuilding.org</a>]
On Behalf Of Brooks, Alamelu<br>
Sent: Thursday, May 21, 2015 10:43 AM<br>
To: Jim Dirkes; Nathan Kegel<br>
Cc: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: Re: [Bldg-sim] Comparing ASHRAE 90.1
App G Models to Real Buildings<br>
<br>
<br>
<br>
I believe Appendix G is not meant to measure the
performance of the existing building. ASHRAE 90.1
Appendix G Technical Committee is the right source to
answer this question. They can clarify the intention of
the APP G modeling methodology.<br>
<br>
<br>
<br>
Best,<br>
<br>
Alamelu<br>
<br>
Alamelu Brooks LEED AP (BD+C), HBDP, BEAP, EIT|
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From: Bldg-sim [mailto:<a moz-do-not-send="true"
href="mailto:bldg-sim-bounces@lists.onebuilding.org">bldg-sim-bounces@lists.onebuilding.org</a>]
On Behalf Of Jim Dirkes<br>
Sent: Thursday, May 21, 2015 10:36 AM<br>
To: Nathan Kegel<br>
Cc: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: Re: [Bldg-sim] Comparing ASHRAE 90.1
App G Models to Real Buildings<br>
<br>
<br>
<br>
I agree fully with all of the above comments and
would like to add these:<br>
<br>
* Even buildings that are commissioned
properly will see their performance erode over time.
There are hundreds of reason for this, ranging from poor
maintenance to well-intentioned maintenance people not
having time to monitor operations well. There is NO
BUILDING that operates well for long.<br>
* Buildings often see changes in
operation, occupancy and schedule. These are oftimes
gradual changes over a period of years, but can be
substantial<br>
<br>
<br>
<br>
On Thu, May 21, 2015 at 10:22 AM, Nathan Kegel
<<a moz-do-not-send="true"
href="mailto:nathan.kegel@iesve.com">nathan.kegel@iesve.com</a>>
wrote:<br>
<br>
Climate files used in the simulations versus the
actual weather.<br>
<br>
<br>
<br>
I’m in the midst of a project that shows a
variance in EUI of up to 200% just by changing the
climate file for the DOE primary school. Full results
to be presented in September.<br>
<br>
<br>
<br>
Add in all the other factors already mentioned,
and if your 90.1 model comes anywhere close the real
buildings’ it’s far more likely that the 90.1 model was
extremely “lucky” than it is that the model used
accurate assumptions.<br>
<br>
<br>
<br>
Regards,<br>
<br>
<br>
<br>
Nathan<br>
<br>
<br>
<br>
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From: Bldg-sim [mailto:<a moz-do-not-send="true"
href="mailto:bldg-sim-bounces@lists.onebuilding.org">bldg-sim-bounces@lists.onebuilding.org</a>]
On Behalf Of Maria-Lida Kou<br>
Sent: Thursday, May 21, 2015 9:17 AM<br>
To: Jacob Dunn<br>
Cc: <a moz-do-not-send="true"
href="mailto:bldg-sim@lists.onebuilding.org">bldg-sim@lists.onebuilding.org</a><br>
Subject: Re: [Bldg-sim] Comparing ASHRAE 90.1
App G Models to Real Buildings<br>
<br>
<br>
<br>
Jacob,<br>
<br>
<br>
<br>
Happy to hear that other people are thinking the
same.<br>
<br>
<br>
<br>
I was into this subject on my own thoughts
recently.<br>
<br>
<br>
<br>
I would like to add in your list: Occupants'
behavior actually which is not in the stage to be
included into the prediction.<br>
<br>
I would add commissioning as well along with
controls simulation and controls operation.<br>
<br>
<br>
<br>
Apologies because I haven't worked with LEED
projects but I think the above applied in general to
"the performance gap".<br>
<br>
<br>
<br>
Really looking forward to hearing more about
this subject as I am not that experienced engineer yet,
but really interested in "the performance" side of
buildings.<br>
<br>
<br>
<br>
Best,<br>
<br>
Maria-Lida Kounadi<br>
<br>
<br>
<br>
<br>
<br>
2015-05-21 15:04 GMT+01:00 Jacob Dunn <<a
moz-do-not-send="true"
href="mailto:jdunn@eskewdumezripple.com">jdunn@eskewdumezripple.com</a>>:<br>
<br>
Bldg-Sim Community –<br>
<br>
<br>
<br>
I’m trying to compile a list of why it
might be inappropriate to compare Appendix G models to
actual consumption data. This comes about because I
recently got into a debate with one of my co-workers
when looking at the infamous NBI chart/study that shows
little correlation to predicted and actual energy values
of LEED buildings. I was trying to explain that the
Appendix G model’s intent is NOT to be compared to
actual consumption, as it is a modeling protocol aimed
at creating consistent relative comparisons for LEED
points.<br>
<br>
<br>
<br>
Here are the reasons thus far that
support this notion (that App G models shouldn’t be
compared to actual data). Does anyone know of any
resources out there that expand upon this? Or can you
think other reasons?<br>
<br>
<br>
<br>
- Appendix G does not take into
account external shading, which can be critical in urban
environments for accurate energy predictions<br>
<br>
- Schedules are typically not
created with the intent of being predictive. Overall
building hours are adhered to, but detailed schedule
creation is not usually in the scope of a LEED model (or
is it, in your experience?). For instance, typical plug
load base values during unoccupied times are .3, this is
a pretty big assumption.<br>
<br>
- The App G model uses a TMY
weather file, which can vary from the current weather
year (I wonder on average by how much?)<br>
<br>
- Infiltration values are
assumed, unless blower door testing has been done (which
is rare for commercial buildings).<br>
<br>
- Thermostat values are modeled
as consistent across the building, which is rarely the
case in an actual operating building<br>
<br>
<br>
<br>
Any additional insight is much
appreciated!<br>
<br>
<br>
<br>
<br>
<br>
Jacob Dunn LEED AP BD+C<br>
<br>
ESKEW+DUMEZ+RIPPLE, APC<br>
<br>
2014 AIA National Architecture Firm
Award<br>
<br>
<br>
<br>
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