[Equest-users] eQUEST Calculations

Tue May 22 08:54:58 PDT 2018

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

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From: Equest-users <equest-users-bounces at lists.onebuilding.org> on behalf of Jones, Christopher via Equest-users <equest-users at lists.onebuilding.org>
Sent: Tuesday, May 22, 2018 10:28:37 AM
To: Kathryn Kerns; Aaron Powers; Nicholas Caton
Cc: equest-users at onebuilding.org
Subject: Re: [Equest-users] eQUEST Calculations

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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From: Equest-users [mailto:equest-users-bounces at lists.onebuilding.org] On Behalf Of Kathryn Kerns via Equest-users
Sent: Tuesday, May 22, 2018 11:19 AM
To: Aaron Powers <caaronpowers at gmail.com>; Nicholas Caton <Nicholas.Caton at schneider-electric.com>
Cc: equest-users at onebuilding.org
Subject: Re: [Equest-users] eQUEST Calculations

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 |

From: Equest-users <equest-users-bounces at lists.onebuilding.org<mailto:equest-users-bounces at lists.onebuilding.org>> On Behalf Of Aaron Powers via Equest-users
Sent: Monday, May 21, 2018 5:30 PM
To: Nicholas Caton <Nicholas.Caton at schneider-electric.com<mailto:Nicholas.Caton at schneider-electric.com>>
Cc: equest-users at onebuilding.org<mailto:equest-users at onebuilding.org>
Subject: Re: [Equest-users] eQUEST Calculations

Very well put Nick, and an important perspective from the world where savings projections are guaranteed.  2 + 2 = 4, but so does 10 - 6

On Mon, May 21, 2018 at 7:13 PM, Nicholas Caton via Equest-users <equest-users at lists.onebuilding.org<mailto:equest-users at lists.onebuilding.org>> wrote:

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.

  1.  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”

  1.  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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Nick Caton, P.E., BEMP

  Senior Energy Engineer
  Regional Energy Engineering Manager

  Energy and Sustainability Services
  Schneider Electric

D  913.564.6361
M  785.410.3317
F  913.564.6380
E  nicholas.caton at schneider-electric.com<mailto:nicholas.caton at schneider-electric.com>

15200 Santa Fe Trail Drive
Suite 204
Lenexa, KS 66219
United States

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From: Equest-users [mailto:equest-users-bounces at lists.onebuilding.org<mailto:equest-users-bounces at lists.onebuilding.org>] On Behalf Of David Eldridge via Equest-users
Sent: Saturday, May 19, 2018 2:08 PM
To: equest-users at onebuilding.org<mailto:equest-users at onebuilding.org>
Subject: Re: [Equest-users] eQUEST Calculations

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

David S. Eldridge, Jr., P.E., LEED AP BD+C, BEMP, BEAP, HBDP

Associate

Direct: (847) 316-9224 | Mobile: (773) 490-5038

Grumman/Butkus Associates | 820 Davis Street, Suite 300 | Evanston, IL 60201

Energy Efficiency Consultants and Sustainable Design Engineers

grummanbutkus.com<http://grummanbutkus.com/> | Blog<http://grummanbutkus.com/blog> | Facebook<https://www.facebook.com/pages/GrummanButkus-Associates/1385285015032526> | Twitter<https://twitter.com/grummanbutkus>

From: Equest-users [mailto:equest-users-bounces at lists.onebuilding.org] On Behalf Of Tom McGovern via Equest-users
Sent: Friday, May 18, 2018 4:45 PM
To: equest-users at lists.onebuilding.org<mailto:equest-users at lists.onebuilding.org>
Subject: Re: [Equest-users] eQUEST Calculations

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

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