[Bldg-sim] "Maslow's" hierarchy of calibration

[Karen Walkerman]

Chris - one tool I've found very useful is to create a heat-map that
compares modeled data with actual data.  I usually do this with hourly
data.  Then I'll create a grid that shows actual and modeled data and break
it down by hour of the day and day of the week.  I can further filter it by
time of year, or outdoor temperature.  Here's an example of total
electrical demand for the whole year broken down by day and hour of day:
[image: image.png]

In this example, we can see that the model is missing some night-time base
loads, there is better agreement during periods of time with high occupancy
and that the occupancy schedule and building opening / closing schedules
need to be adjusted.

I've found this approach can really help me identify what parameters to
experiment with, and it is also an excellent tool for communicating with
clients.

--
Karen

On Tue, Jul 23, 2024 at 3:41 AM Julien Marrec via Bldg-sim <
bldg-sim at lists.onebuilding.org> wrote:

> Hello Chris,
>
> Apologies if I didn't understand the question correctly, but I understood
> it as how to automate (part of) the calibration process.
>
> I don't think you should do without first taking a hard look at your
> initial model, refine it until almost satisfied, before you can start the
> calibration process itself. One example would be you should probably take a
> close look at your output:variables to ensure that you are supplying the
> right amount of outside air to your zones.
>
> I also don't think you should blindly let some tool touch *any*
> parameters there are in your simulation file. There are things you can
> measure or reasonably approximate, and those should be defined in your
> model. Then there are things that are hard to guess, or hard to guess
> right, and those should be what you play on.
>
> These caveats aside, there are a number of tools and algorithms that can
> help you automate the calibration process itself.
>
> One example of such is LBNL's GenOpt
> <https://simulationresearch.lbl.gov/projects/genopt> which is software
> agnostic (a front-end for it that I have used in the past with both eQuest
> and EnergyPlus is ExcaliBEM, from Canada's SIMEB, not sure if still out
> there or not)
>
> You basically inform the software of the parameters you want to vary and
> how (for eg, infiltration from 0.3 to 1.0 ACH, R-value +- 30%, heating
> system efficiency range, etc), and define an objective function, which can
> be a python script. I've done one that basically tries to minimize the
> CV(RMSE) and NMBE %, and penalizes heavily if not meeting the thresholds of
> Guideline 14. The tool will run simulations and try to minimize the
> objective function.
>
> The OpenStudio Analysis Framework
> <https://github.com/NREL/OpenStudio-server> is a great candidate if
> you're working with the OpenStudio ecosystem.
>
> JEPlus+EA <http://www.jeplus.org/wiki/doku.php> can do that for
> EnergyPlus (and is (was?) bundled in Design Builder if I recall correctly)
>
> There are a variety of similar questions on unmethours.com
>
> Best,
> Julien
> --
> Julien Marrec, EBCP, BPI MFBA
> Owner at EffiBEM <http://www.effibem.com>
> T: +33 6 95 14 42 13
>
> LinkedIn (en <https://www.linkedin.com/in/julienmarrec>) *| *(fr
> <https://fr.linkedin.com/in/julienmarrec/fr>) :
> <http://www.linkedin.com/in/julienmarrec>
>
>
> Le mar. 23 juil. 2024 à 09:06, Chris Yates via Bldg-sim <
> bldg-sim at lists.onebuilding.org> a écrit :
>
>> Dear bldg-sim’rs,
>>
>>
>>
>> I’m interested in how people save time and improve accuracy when it comes
>> to calibrated simulation.
>>
>>
>>
>> Calibrated simulation attempts to model a vast amount of parameters. In
>> comparison to regression methods it’s completely overspecified. We do it in
>> the hope of creating a model (a “digital twin”) that gives us many more
>> what-if? scenarios than regression could on its own.
>>
>>
>>
>> However, the process is often fraught with dead ends. Of course it is!
>> There are too many details. We can waste a lot of time on getting one
>> parameter right, only to find it has little effect. Even worse, we could
>> overlook something major and compensate with our own erroneous assumptions.
>> Or, we can work through a whole model and find something very late on,
>> causing us to backtrack over previous work. The act of calibration seems
>> beyond detailed management because of the sheer number of building types,
>> servicing approaches, operational parameters, embedded issues (this is a
>> longer list but I’ve ran out of words…).
>>
>>
>>
>> The exam question is:
>>
>>
>>
>>    - Does anybody follow a simple high level framework (a bit like
>>    Maslow) for calibration?
>>
>>
>>
>> Sorry. I could have asked that at the start of the email 😐
>>
>>
>>
>> All the best
>>
>>
>>
>> Chris
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