[Bldg-sim] Lies, darn lies, and statistics

Fri Mar 17 13:49:18 PDT 2023

BTW:

After reading this thread, again, I realized that I had responded mostly about simulation, and a bit about RP1050 and RP1093. Then I realized I left out a whole universe of papers that discussed the evaluation of the very best inverse models, namely the ASHRAE predictor competitions, old age I guess:

Miller, C., Balbach, C., Haberl, J. 2020. “The ASHRAE Great Energy Predictor III Competition: Overview and Results”,  Science and Technology for the Built Environment, Vol 26, No. 10, ESL-PA-20-07-02 (August).

 Haberl, J., Thamilseran, S. 1998. “Predicting Hourly Building Energy Use: The Great Energy Predictor Shootout II: Measuring Retrofit Savings,” ASHRAE Journal, Vol. 40, No. 1, pp. 49 - 56 ESL-TR-95-09-02(January).

Kreider, J. Haberl, J. 1994. “Predicting Hourly Building Energy Usage: The Results of the 1993 Great Energy Predictor Shootout Identify the Most Accurate Method for Making Hourly Energy Use Predictions,” ASHRAE Journal, Vol. 35, No. 3, pp. 72 – 81 ESL-PA-94-06-02 (June).

Now I know this is not fair talking about publications from 20+ years ago, but I'll be happy to send copies to those who request them.

In the first Predictor Competition in 1994, Jan Kreider (now passed away) and I proposed, developed and delivered a new idea that the internet could be used to have a competition. If you recall, this is before Google, so I was a bit of a issue. We had to use FTP sites, and emails to distribute the material for the competition.

The winner was amazing, Dr. David McKay from Cavendish labs in the UK. It is even more amazing because he was not even in the HVAC field, he was an astronomer. His submission smashed all the rest with its answer, because he REALLY  knew what he was doing with his methods. In the competition, one year of hourly data for the Zachry building at TAMU was prepared so that every other week was included in the "testing" file, as well as another test regarding weather data. All contestants were asked to develop the inverse model and submit their results for the missing data. I collected the data and Jan ran the analysise. We then published the results in several ASHRAE publication, including a Journal paper and Transactions papers, as well as Transactions papers from each of the contestants. The reactions at the ASHRAE meeting were shocking. Lots of questions from members who were interested in learning something new, and lots of questions about members, one in particular, from members who would stand up and say "you mean you can predict the energy use of an institutional building with a regression model without visiting the building...utter nonsense...".

In the second competition right after the first, the same building was revisited and the rules changes slightly to see how well the models predicted results AFTER a retrofit was applied to the building. The winner was Robert Dodier and Gregor Henze at the University of Colorado. Results also included an ASHRAE Journal article and Transactions papers.

In the third competition, developed by Clayton Miller, Chris Balbach, myself and others in 2020, the idea was expanded to include 100s of data sets and a world-wide search for the best model. The winner was Matthew Motoki and Isamu Yamashita from Japan. Their model used an ensemble of models, including Light GBM and MLP models trained on different subsets of the data. Results also published in the ASHRAE STBE.

So, inconclusion, there's a bit out there in the ASHRAE publications going back 25+ years. However, there is lots to learn from those who are searching. Clever inverse models have their place, but only in the hands of master craftsmen (or craftpersons).

Jeff

Jeff S. Haberl, Ph.D., P.E.inactive, FASHRAE,FIBPSA               We are like fluttering leaves on the branches of trees

Department of Architecture                                                       in the forests of the landscape that surrounds us.

Texas A&M University                                                                  If we could, for just a moment, flutter together,

College Station, TX 77845-3581                                                 We could lift the earth up to be a better place.  JSH 2022

Office: 979-845-6507, Lab: 979-845-6065

Fax 979-862-2457

jhaberl at tamu.edu,www.esl.tamu.edu<mailto:jhaberl at tamu.edu,www.esl.tamu.edu>

________________________________
From: Bldg-sim <bldg-sim-bounces at lists.onebuilding.org> on behalf of Haberl, Jeff via Bldg-sim <bldg-sim at lists.onebuilding.org>
Sent: Wednesday, March 15, 2023 4:35 PM
To: Justin Spencer <jspencer17 at gmail.com>; Chris Yates <chris.malcolm.yates at gmail.com>
Cc: bldg-sim at lists.onebuilding.org <bldg-sim at lists.onebuilding.org>
Subject: Re: [Bldg-sim] Lies, darn lies, and statistics

Thanks Chris: FYI, if anybody has problems finding the articles about RP150, RP1093, or other items I mentioned please let me know. I'll be happy to send them to you if you ask me. BTW, in addition to the 4 STBE articles on "Origins",
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Thanks Chris:

FYI, if anybody has problems finding the articles about RP150, RP1093, or other items I mentioned please let me know. I'll be happy to send them to you if you ask me.

BTW, in addition to the 4 STBE articles on "Origins", we also developed two papers on peak load calculations, which serve a useful purpose of reviewing the history of "loads" calculations, which for those of you born AC (after computers), were the other ½ of the analysis of a building, namely the peak heating / cooling load to size the HVAC system, as well as the annual energy use prediction, which used to be a rough estimate:

Mao, C., Baltazar, J.C, Haberl, J. 2018. “Comparison of Building Envelop Peak Load Design Methods”, Science and Technology for the Built Environment, Vol. 25, Issue 2 (October).
17.

Mao, C., Baltazar, J.C., Haberl, J.S., 2018. “Literature Review of Building Peak Cooling Load Methods in the United States”, Science and Technology for the Built Environment, Volume 24, No. 3, pp. 228-237, ESL-PA-18-03-01 (March).

Otherwise, feel free to visit the ESL's website, where we have over 8,000 publications on-line, and lots of other stuff.

Energy Systems Laboratory (tamu.edu)<https://esl.tamu.edu/>

Jeff

Jeff S. Haberl, Ph.D., P.E.inactive, FASHRAE,FIBPSA               We are like fluttering leaves on the branches of trees

Department of Architecture                                                       in the forests of the landscape that surrounds us.

Texas A&M University                                                                  If we could, for just a moment, flutter together,

College Station, TX 77845-3581                                                 We could lift the earth up to be a better place.  JSH 2022

Office: 979-845-6507, Lab: 979-845-6065

Fax 979-862-2457

jhaberl at tamu.edu,www.esl.tamu.edu<mailto:jhaberl at tamu.edu,www.esl.tamu.edu>

________________________________
From: Bldg-sim <bldg-sim-bounces at lists.onebuilding.org> on behalf of Chris Yates via Bldg-sim <bldg-sim at lists.onebuilding.org>
Sent: Wednesday, March 15, 2023 4:19 PM
To: Justin Spencer <jspencer17 at gmail.com>
Cc: bldg-sim at lists.onebuilding.org <bldg-sim at lists.onebuilding.org>
Subject: Re: [Bldg-sim] Lies, darn lies, and statistics

I've had more responses back than I bargained for! Whilst I was hoping for an easy answer, I think the takeaway is that it's not simple and it will require some perseverance. Thank you to everybody. Massimiliano, I do use ECAM and I
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I've had more responses back than I bargained for! Whilst I was hoping for an easy answer, I think the takeaway is that it's not simple and it will require some perseverance. Thank you to everybody.

Massimiliano, I do use ECAM and I highly recommend it. I'm surprised it doesn't feature on this forum much. I use it a lot, but I don't use a lot of it. I use it mainly for graphing (the box plot is excellent). I haven't got the M&V tools working for me. The I-P unit-only restriction is a problem for me. I don't know if this I-P unit restriction is why I cannot run some of the models... negative degrees celsius might throw it. Here's a 3-minute movie for anybody who hasn't used it: https://youtu.be/3-KcEF5VbGo<https://urldefense.com/v3/__https://youtu.be/3-KcEF5VbGo__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLm4idf9Cc$>

I did take a look at the BPA book a while ago and perhaps it's time to revisit it. I recall it was clearly laid out and wasn't too dense a read (for a statistics book).

Shawn, CV is something I've never heard about. Now I've taken a primer on youtube. It's beyond me right now, but I'm sure it will grow in use. I always thought ML meant some kind of chat GPT thing but can see that it's more about remixing model sources and test data and undertaking more iterations using various options. We know iterations can make a big difference.

I didn't realize this repository had been developed: https://onebuilding.org/<https://urldefense.com/v3/__https://onebuilding.org/__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLmfzV5Q3w$> Thanks again, Jeff.

Chris

On Wed, Mar 15, 2023 at 3:02 PM Justin Spencer via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:
Hi folks -- I have a few more comments. Building simulation models are good for extrapolating in time (meaning to different weather conditions) and space (when you make a change to a building design that cannot be observed).

1. If you're just trying to extrapolate in time, a mathematical regression approach or machine-learning approach may make more sense. My old team used this approach to build out residential end use load shapes in Massachusetts from metered data and we used a regression approach (with some terms that reflected our building science knowledge) with cross-validation and a value function, like what Shawn mentioned. We were focused on peak demand impacts in this case, as well as overall energy consumption, so we weighted our errors accordingly, to focus on peak demand. You can see the results of this work here: RES-1-Residential-Baseline-Study-Ph4-Comprehensive-Report-2020-04-02.pdf (ma-eeac.org)<https://urldefense.com/v3/__https://ma-eeac.org/wp-content/uploads/RES-1-Residential-Baseline-Study-Ph4-Comprehensive-Report-2020-04-02.pdf__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLm6EUaft4$>

2. If you're trying to extrapolate in space, e.g. to a different building design, then a building simulation model helps you out a lot, especially if you start out with a measured stake in the ground for the things you care most about, e.g. if you care about AC load during summer peaks or heating load during winter peaks, then you need measurements of those to do a good job. Frequently, we invest way more in complex data modeling and analysis, when measurement and a simpler model or even a simplified calculation would help much more. A basic model of (Measured data) * % savings from engineering-based algorithm works well for single dimensional building improvements, provided your savings estimation algorithm is decent.

On Wed, Mar 15, 2023 at 6:34 AM Smita Chandra Thomas via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:
Hi Shawn,

Your ideas of bringing in gaming capabilities to improve building simulations are fascinating. And I completely agree on the need for cross-validation. Garbage-in-garbage-out sounds cute, but why do we even allow that garbage?

Have you considered applying for DOE funding to bring your concepts into E+? Or perhaps collaborate with the creator of Ladybug suite of tools… which I believe is also in the public domain?

Sincerely,
Smita
—
Smita Chandra Thomas
Founder and Principal
Energy Shrink, LLC
202-556-3369

On Mar 14, 2023, at 10:37 PM, Shawn Shi via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:

Hello Jeff,

Thank you kindly for the long reply and history lesson, it's interesting to know BES originated from bomb shelters, there is one still operating not far from where I live :)

As a gamer growing up, I've always wondered why BES is not voxel-based like minecraft or why we are not using real-time ray-tracing from GPUs for solar insolation computations.

Anyways, back to model training/calibration. Loss function (in ML training) or cost function (in optimization) is the measuring stick telling the optimization program how to assess the performance of the model. Usually MSE/RMSE is used but it has its limitations, that's why in ML we sometimes use log errors<https://urldefense.com/v3/__https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLmlNkbMrA$> to deal with outliers. Another example, in fraud detection, we customize loss functions to penalize false negatives harsher than false positives.

Actually many years ago, someone mentioned custom cost function to deal with peak loads for BES calibration, using ExcaliBEM: https://unmethours.com/question/13182/genoptenergyplus-best-algorithm-for-peak-load-reduction-load-shift/<https://urldefense.com/v3/__https://unmethours.com/question/13182/genoptenergyplus-best-algorithm-for-peak-load-reduction-load-shift/__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLm-f0PuLg$>

Now on cross-validation<https://urldefense.com/v3/__https://medium.com/@soumyachess1496/cross-validation-in-time-series-566ae4981ce4__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLmtypU0ZQ$> (CV), it is a wrapper around each model training process to make sure the model is not over-fitting. It is usually the first thing I check when seeing people applying ML models.
In theory these two practices should not interfere with the bounded parameter space.

Thanks,
Shawn

On Tue, Mar 14, 2023 at 9:05 PM Haberl, Jeff <jhaberl at tamu.edu<mailto:jhaberl at tamu.edu>> wrote:
Thanks Shawn:

Good thoughts. Maybe outside my expertise, but I'll take a crack.

First, BES are constrained models in that they only allow the user to change cerain parameters and not others. Furthermore, the BES is a preconfigured model of a building that tries to include all the relevant heat transfer properties to the ambient conditions and the SYSTEM and PLANT reactions to these loads using user-defined inputs.

This situation only gets worse when on considers that even EP+ was influenced by the original LSPE architecture that was suggested for the Post Office program in the early design of the TACS program. Although EP+ has evolved way beyond the constraints of LSPE BES programs, the underlying code still has ghosts of this architecture in its algorithms. We recently published one of three new articles for STBE that updates the previous "Origins" articles we published. The first one is "Origins of whole-building energy simulations for high-performance commercial buildings: Contributions of NATEOUS, SHEP, TACS, CP-26, and RESPTK programs", by Jounghwan Ahn and myself. Two more articles to follow soon in STBE.

In the first article, thanks in part to Jason Glazier for dropping-off a pickup truck's worth of books when Robert Henniger retired, we outline how the early simulation programs can be traced to simulations of bomb shelters in the 1960s by Tamami Kusuda and Metin Lokmonhekim and the earliest FORTRAN programs that codified response factors by Tamami Kusuda (RESPTK). All of these books are now scanned and can be found on the onebuilding.org<https://urldefense.com/v3/__http://onebuilding.org__;!!KwNVnqRv!Extkc8mlflex-ZmK3txGZ0ReHX_CJE2pRAniSViBkBoOrG5enQeyAFqmZeoOetpfCe2qJ-xkNJmHhb3svmLmAQDrVAg$> website.

In these books you see that what was done in the early days was done because of constraints in the computing hardware in the 1960s and into the 1970s. Unfortunately, ghosts of this kind of thinking still exist today, with a few exceptions, when we try to define a "thermal zone" as a node that maintains a known temperature given varying internal inputs and external R-C networks that connect the node to the exterior. So, although we've come a long way, we still have a long way to go to "cut all the stings" to the old LSPE constraints.

Second, there are prototypes of how to move forward into the future, but their "secrets" are highly guarded, which is not good for the general BES community. However, it is a fact of life and we have to live with it and put our minds to use to reverse engineer what the proprietary codes are doing so we can publish in the public domain so all can see what is done. EP+ is one effort that stands alone it what it has accomplished AND has accomplished in the public domain -- bravo.

However, there are spectacular proprietary codes that are doing breathtaking simulations that are hidden "behind the wall", and so it goes.

Examples include combined CFM and BES -- a daunting task. Another is full force raytracing and BES programs. Yet another is Urban Scale Building Energy Modeling (UBEM). All are paths into the future that are critical for BES modelers to learn and incorporate into the work. Finally, there are robo-simuilations that are now being attached to BIM, which is the future for BES. Yet, there is little discussion about public code, where to get it and whether or not it is worth a hoot.

So, I think BLDG-SIM has a role in providing a public space where "issues" such as the above can be discussed, and vetted in the public domain, so those who have the development money can get a second opinion about how they are spending state or federal money.

BLDG-SIM can also be seed-corn for student thesis topics since these are like hens teeth when thinking them up and then following through to publication

Just some thoughts.

Jeff

Jeff S. Haberl, Ph.D., P.E.inactive, FASHRAE,FIBPSA               We are like fluttering leaves on the branches of trees

Department of Architecture                                                       in the forests of the landscape that surrounds us.

Texas A&M University                                                                  If we could, for just a moment, flutter together,

College Station, TX 77845-3581                                                 We could lift the earth up to be a better place.  JSH 2022

Office: 979-845-6507, Lab: 979-845-6065

Fax 979-862-2457

jhaberl at tamu.edu,www.esl.tamu.edu<mailto:jhaberl at tamu.edu,www.esl.tamu.edu>

________________________________
From: Bldg-sim <bldg-sim-bounces at lists.onebuilding.org<mailto:bldg-sim-bounces at lists.onebuilding.org>> on behalf of Shawn Shi via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>>
Sent: Tuesday, March 14, 2023 7:19 PMGood
To: Dru Crawley <dbcrawley at gmail.com<mailto:dbcrawley at gmail.com>>
Cc: bldg-sim at onebuilding.org<mailto:bldg-sim at onebuilding.org> <bldg-sim at onebuilding.org<mailto:bldg-sim at onebuilding.org>>
Subject: Re: [Bldg-sim] Lies, darn lies, and statistics

Hello all, Just a couple thoughts from a random passerby. It’s rare to see this email group this active :p Now please bear with my 2 cents: One thing that always bother me when working on energy model calibration in the past was: why was cross
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Hello all,

Just a couple thoughts from a random passerby. It’s rare to see this email group this active :p

Now please bear with my 2 cents:

One thing that always bother me when working on energy model calibration in the past was: why was cross validation (CV) not used?

One fundamental technique in Machine Learning to avoid overfitting is through cross validation. Fitting a complex first-principle model is similar to training a complex black-box model: high degree of freedom, risks of overfitting. CV can be adopted to avoid incorrect local minima during model training. There are multiple CV approaches for timeseries model training.

And regarding predicting peak loads, etc. When training a model, the loss function (equation that describes model accuracy) dictates how you want the model to behave. Usually RMSE is used but you can customize it to make the model behave in a certain way better. For example, something like a compound loss function that combines prediction errors from both overall predictions as well as peak prediction. You can adjust the weight for each component to dictate which part is more important than another.

Thanks,
Shawn Shi

On Tue, Mar 14, 2023 at 7:41 PM Dru Crawley via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:
Yep, not me. But I use it a lot.

On Tue, Mar 14, 2023, 4:19 PM Vikram Sami via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:

George Box coined that one I think

From: Bldg-sim <bldg-sim-bounces at lists.onebuilding.org<mailto:bldg-sim-bounces at lists.onebuilding.org>> On Behalf Of Jim Dirkes via Bldg-sim
Sent: Tuesday, March 14, 2023 12:13 PM
To: chris.malcolm.yates at gmail.com<mailto:chris.malcolm.yates at gmail.com>
Cc: bldg-sim at onebuilding.org<mailto:bldg-sim at onebuilding.org>
Subject: Re: [Bldg-sim] Lies, darn lies, and statistics

I think Dru Crawley coined the phrase, "All models are wrong. Some are useful."

Chris, you highlighted an assortment of variables which are omnipresent, inconsistent and uncontrollable - so what, exactly, does your client expect? Is it a realistic expectation? For example, are they going to nail you to the wall when, inevitably, you are "wrong" next year?

I have not calibrated many models, but have been made more appreciative of all the uncontrollables by the ones I calibrated :(. All the statisticians know that there is always more than one solution which will result in a high R2 value or low CVRSME, so which is correct?

Rather than a calibrated model, lately I've been encouraging clients to consider one of the FDD platforms on top of their Building Automation System. Spending time and money to evaluate whether things are working properly makes more sense to me - it's "real life" vs a prediction. (Can't forget to mention thoughtful and thorough commissioning here; that's essential.)

ps, I love your thoughtful approach. You're setting a great example! One aspect of that is to reach out to the wider modeling community to gather input and feedback.

Jim Dirkes  1631 Acacia Drive NW Grand Rapids, MI 49504<https://urldefense.com/v3/__https://www.google.com/maps/search/1631*Acacia*Drive*NW*Grand*Rapids,*MI*49504?entry=gmail&source=g__;KysrKysrKw!!KwNVnqRv!Gyy3l3XXdU0uo6SKEX1uDKDVeqKvb3BM7ELdcAhCyGrNAZnPW1FVt64gzSX8gd4XJkQtPfaNE_z8YgDv9YFrSf_crIA$> -  616 450 8653

Coffee Conversation:

The "individual" is an impossible concept, conceived by the Enlightenment philosophers. It makes no sense to the Christian. In marriages, and families, in associations and friendships and religious orders, we are not individuals, but a communion of persons.

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On Tuesday, March 14th, 2023 at 2:10 PM, chris.malcolm.yates at gmail.com<mailto:chris.malcolm.yates at gmail.com> <chris.malcolm.yates at gmail.com<mailto:chris.malcolm.yates at gmail.com>> wrote:

I think I need to qualify this: informed by G14, but definitely not compliant with it! There is some allowance for repairing or “healing” data, but when the data has a lot of holes or modes/ category variables then forget it. This is my case, but the client still wants some kind of representative simulation.

Monthly models can be garbage. School holidays cut across months at different times, combined heat and power is popular which complicates gas usage especially when metering is limited… did the heat by-product of electricity generation go to the building, or was it rejected? They can work for heating in our temperate climate, but not for cooling.

Nevertheless, we need some kind of representative simulation model. We can’t make any ECM qualifying claims but we can do something useful. This is where these methods can give you a lot of insight before you start modelling.

I hadn’t tried the IMT previously. We tend to have limited our regression analysis to monthly “degree day” methods (your 2p model, I think). I plugged some project specific daily electricity data into the MVR example (multi variate regression) and it seemed to give decent CVRMSE (~2-3%) but low R2 (~0.7). However, it appeared to provide some insight on cooling usage (monthly 2p models are meaningless for this in the UK’s temperate climate).

I also made some 2p monthly models of gas. I thought these were good until I compared successive years. I guess this is were understanding a range of statistical indices is helpful.

Here’s the final rub, because the underlying data has so many inconsistencies that can only be made sense of with some regression models, it’s easier to “calibrate” the simulation model to the regression models than the original data. But I may use 2p monthly for gas, daily MVR for electric…

So I need to ask if I have wondered completely off-piste with this!

Chris

From: David Eldridge <dancingdavide at hotmail.com<mailto:dancingdavide at hotmail.com>>
Sent: Tuesday, March 14, 2023 12:53 PM
To: Jim Dirkes <jvdirkes2 at protonmail.com<mailto:jvdirkes2 at protonmail.com>>
Cc: chris.malcolm.yates at gmail.com<mailto:chris.malcolm.yates at gmail.com>; bldg-sim at onebuilding.org<mailto:bldg-sim at onebuilding.org>
Subject: Re: [Bldg-sim] Lies, darn lies, and statistics

Indirectly there probably isn’t a daily set of metrics in the Guideline since the simulation programs aren’t usually outputting daily results, but there’s no reason there couldn’t be one statistically.

You could make one if you had only daily utility data and had to aggregate the simulation results to daily totals, there isn’t a published target metric but you could still show that you calculated one and why you think it was a good or bad result.

DSE Mobile

On Mar 14, 2023, at 6:10 AM, Jim Dirkes via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:

Dear Chris,

Kudos for appreciating a gap in your understanding. (I'm in your camp)

On the other hand, there are SO many variables in building operation that, short of a highly instrumented (and carefully calibrated) building for everything from lights to people to plug loads to HVAC - calibration is a fiction (and I'm confident that no such building exists). Daily calibration is a complete fiction, perhaps even a deception. On top of that, a "calibrated" model is just a moment in time; everything going forward is guaranteed to be different than during the calibration time period.

I think of "calibration" as more like a sensitivity analysis - determine which variables matter more and which matter less. GenOpt works nicely for that purpose https://github.com/lbl-srg/GenOpt<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Flbl-srg%2FGenOpt&data=05%7C01%7Cjhaberl%40tamu.edu%7Cf23024d02e764900791708db259d4949%7C68f381e346da47b9ba576f322b8f0da1%7C1%7C0%7C638145129729846219%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Q0J8mtgnCDr6pMQmZ3dn34rUkKoeaQBgYZnzvrlSKzs%3D&reserved=0>

Jim Dirkes  1631 Acacia Drive NW Grand Rapids, MI 49504<https://urldefense.com/v3/__https://www.google.com/maps/search/1631*Acacia*Drive*NW*Grand*Rapids,*MI*49504?entry=gmail&source=g__;KysrKysrKw!!KwNVnqRv!Gyy3l3XXdU0uo6SKEX1uDKDVeqKvb3BM7ELdcAhCyGrNAZnPW1FVt64gzSX8gd4XJkQtPfaNE_z8YgDv9YFrSf_crIA$> -  616 450 8653

Coffee Conversation:

The "individual" is an impossible concept, conceived by the Enlightenment philosophers. It makes no sense to the Christian. In marriages, and families, in associations and friendships and religious orders, we are not individuals, but a communion of persons.

------- Original Message -------
On Tuesday, March 14th, 2023 at 6:52 AM, Chris Yates via Bldg-sim <bldg-sim at lists.onebuilding.org<mailto:bldg-sim at lists.onebuilding.org>> wrote:

Hi All

I do find ASHRAE Guideline 14 a little too hardcore for my basic understanding of statistics. I can plug any of the equations into Excel, but I’ve realised my statistics understanding is very limited! (I’m outed!)

We don’t actually have to work to G14 in the UK (probably good because my copy is a bit old). I finally realised I didn’t know enough after I’d been (lazily) using R2 in Excel on some monthly data. I thought that R2 > 0.9 was generally ok… yeah, it wasn’t.

So, are there any easy to understand resources available?

I’ve been messing around with the IMT as well. It’s been fun going back to DOS 😊. This got me into daily methods, which leads to my next question. Is there any reason why there isn’t a daily calibration option specified in G14?

Many thanks!

Chris Yates

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