Hello,
Have a question regarding the internal calculations performed by eQUEST. Am new to eQUEST and just looking to understand some basics. Trying to figure out if there is some way to modify internal eQUEST calculations so baseline model may be adjusted to fit existing utility bills or if there is no way to modify internal eQUEST calculations and we need various end arounds to fit baseline eQUEST model to fit existing utility bills.
Thanks,
Tom McGovern
If you mean automatically adjusting the parameters of the model inputs to minimize error against the actual utility data by using a basis such as ASHRAE Guideline 14, then no eQUEST does not have that capability built in.
You wouldn?t be changing the internal calculations ? some review of the inputs and outputs should provide insight into what variables are known and which are assumed and may have a reasonable range of values resulting in a better fit to the utility data.
With some on-site data such as what you?d be collecting for energy audits anyway, you can usually get an accurate model to start with, or with few iterations.
There are other code/programs/platforms available that can help optimize, but requires a little more effort up front to either run the programming yourself or using a packaged program to setup the modeling files in a different program and tell the optimization program which variables have which range of values to be allowed.
I hope this helps.
David
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As an energy engineer in the performance contracting side of the industry, a defining skillset for my job is creating and then calibrating models to fit historical utility data. We calibrate our models to a degree of rigor allowing our business to guarantee savings projected from those models (and write shortfall checks when we?re wrong). I don?t generally talk up my background, but I think in this case it helps to know where my voice is coming from to press a nuanced response:
It's possible (again, not built-in) to automate iterative model input manipulation to ?auto-tune? a building energy simulation to match a set of utility bills. You can even get the curves to fit extremely tightly over multiple meters. I?ve gone so far as to build some such tools from scratch, and that experience has taught me some very important lessons I didn?t set out to find. Among them, an ?auto-tuned? model where many inputs are guided by randomization and computer logic can in practice become very difficult to trust for projecting savings, even on a relative ?doesn?t need to be seen on the bills? level.
On the other hand, if you careful to bound ?auto-tuning? techniques to reasonable input ranges, and specifically to address ?unknowable? model inputs which cannot be measured or reasonably estimated/inferred, the results can become much more useful, even enlightening. This ?optimal? usage of the likes of monte carlo analysis, with and without machine learning algorithms, is anything but an ?easy? button.
I use doe2/eQuest as my primary energy simulation platform, however all of the above advice is platform-agnostic and holds true whether you?re crunching degree-day analyses in excel or wielding rooms of supercomputers in the cloud with e+.
If calibration matters, and you?re not doing so just to tick some prescriptive box, best practice during model development is to keep mindful track of which inputs are:
1. Known
* General Hierarchy of ?Known:? Design/Construction Documents < As-Builts < RCx reports < Current field measurements & observations
* Be mindful that construction documents and nameplate data are better than nothing, but commonly do not match reality and may be better considered as ?informed estimates.? Allow some room for doubt.
2. Estimated
* For existing buildings, this most inputs will be ?estimated.?
* If for example you have to define fan power based on scheduled static pressure loss and airflows on the drawings? that?s just aligning your estimate with the designer?s. Actual is probably something different.
* Software defaults you understand are ready to ?own? or explain fall under this category
* This includes anything ?auto-sized?
3. Guesswork
* This includes software defaults that you are relying upon but haven?t yet investigated/understood.
* This includes ?known unknowns? for lack of information / resources.
* A pretty common example is envelope constructions where (a) you have no architectural details/specifications to reference all the layers in the middle and (b) you aren?t budgeted/resourced to tear up a client?s walls to find out what?s inside.
Considering the degree of input complexity for something like an eQuest model, I feel there will always be some blend of all if these input categories for every project and every individual modeler. Experience helps, though as the years pile on, for every new topic I get a lock on measuring/estimating, I feel like I learn about two more issues that were previously not on my radar? ?the more I see the less I know!?
Having rough estimates and unknowns is fine, but the more that you know or else can reasonably estimate, the better your initial calibration results will turn out, and the quicker the process of iteratively ?tuning? a model will go. When you have a good record kept of which inputs are particularly solid vs. estimated/guesswork, you can work your way up the tree, marrying that knowledge to assumed/tested input sensitivity on the results, and plot a course to find your way back to the billed amounts!
Hope this is helpful!
~Nick
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Very well put Nick, and an important perspective from the world where
savings projections are guaranteed. 2 + 2 = 4, but so does 10 - 6
I have never been a fan of those performance contracting energy savings projects and our company has steered clear of them for the reasons listed below. I have done fluid and thermal finite element modeling and energy modeling for about 30 years off and on, and the only thing that can be said for these models is they show the user how something changes as a result of changing certain variables. The models do not predict the future nor do they justify the past.
Sometimes a difficult concept to get across to people.
Kathryn Kerns
Systems Specialist
BCE Engineers, Inc.
| Ph: 253.922.0446 | Fx: 253.922.0896 |
As Niels Bohr said, ?Prediction is very difficult, especially about the future?.
Christopher R Jones, P.Eng.
T+ 1 416-644-0252
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I was going to mention GenOpt as one option, and which actually just showed up in another thread on bldg.-sim mailing list.
However, the "standard" GenOpt installation can optimize one function, so as a calibration tool there would be an additional step to develop a CV (RMSE) and/or mean bias error for the results compared to the real utility data.
The autotune methods such as a presentation from Dr. New at ORNL given to IBPSA-USA and ASHRAE, or optimization platforms give a better control over the parameters to be varied. The code that you can get from GitHub to do this though is for EnergyPlus, not eQUEST or DOE2.2.
With GenOpt I also don't believe you'd be able to supply ranges for all the variables - so the program would minimize error but a solution could include nonsensical parameters if that satisfied the error function.
David
All,
Thanks for all your help with this, you have given me a much better picture
of the field.
Curious to find out one more thing. Wonder what confidence interval between
model output and utility consumption data may be expected with reasonable
effort of 10-12 modeling hours for straightforward 100,000 square foot
building if we have two different scenarios:
1. Good as-built drawings for a 15 yr old building.
2. No historical drawings and mostly estimated/guesswork for 100 year
old building.
All reasonable assumptions welcome.
Thanks again,
Tom
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Dependent upon the energy modelers experience. Budget adequate field survey time for scenario 2.
Josh
Hi,
As built drawings from 10 years ago will facilitate some stuff but how your building actually operates will be completely different, and equipment could have already started to fail or be used a lot differently than planned for (eg bypassing controls to run on manual because controls were too complicated for the operator) .
Hell, even as built from 6 months ago will just help you get equipment capacities and locations served. Buildings never operate as designed, especially without deep thorough commissioning which isn?t the norm today, at all.
I also don?t think there?s such a thing as a straightforward building once they have been operating for a while. Sure, some are more complicated than others, but from my experience even a simple multi family building can hold true wonders/abominations (I?ve often seen some with dozens of broken exhaust fans, leaking steam boilers and traps etc). That?s the stuff you?ll never guess before you get on site and that you need to address.
You?ll need thorough field work in both cases, with measurements.
And I don?t think 10-12 hours of modeling will amount to anything even remotely resembling actual operations. You?ll spend a bunch of that time getting your geometry right and perhaps have enough time to start on your systems. Then you?ll be likely quite far off the utility bills and start tuning the input parameters that you do know or can guess the right ballpark for.
10-12 hours is what the calibration process only should take, assuming you have already entered all known parameters.
Energy modeling is a complicated process, and requires understanding how HVAC systems do work, that?s one reason why all energy modelers should start by actually spending time looking at actual buildings before they get their hand on a modeling software.
It?s GIGO: garbage in, garbage out.
Obviously that?s just my two cents. And it does depend on your budget and what level of quality you really are looking for, but keep in mind that modeling is helpful only if you do try to do it right to begin with, otherwise you?re better off addressing only the retrocomissioning items with your limited budget.
Best,
Julien
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