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

Tue Jul 23 02:17:07 PDT 2024

Hi Julien,

This is all useful info, but my query was almost the opposite. I’ve not even thought of using a tool at this point (my desk is under piles of paper with tick-tallies scribbled over anything from crib lists to screenshots of hvac models). It’s more about focusing on the highest confidence features first before approaching refinement.

On reflection I’ve asked the same question that’s been answered (correctly) many a time with the Donald Rumsfeld quote! https://en.wikipedia.org/wiki/There_are_unknown_unknowns

I like the idea of optimisation tools to goal seek lower errors. Although we know a low error doesn’t automatically mean a valid model. I’m using IES at the moment and it doesn’t have an open “back end” to tap into as well. They may be able to do this with their own parametric tools, but I’m not sure if this accesses the HVAC modelling.

Cheers

Chris

From: Julien Marrec <julien.marrec at gmail.com> 
Sent: Tuesday, July 23, 2024 8:40 AM
To: Chris Yates <chris.malcolm.yates at gmail.com>
Cc: bldg-sim at onebuilding.org
Subject: Re: [Bldg-sim] "Maslow's" hierarchy of calibration

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 <http://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> ) : 

Le mar. 23 juil. 2024 à 09:06, Chris Yates via Bldg-sim <bldg-sim at lists.onebuilding.org <mailto: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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