[Bldg-sim] Energy model calibration - normalizing the utility bills to month start-end

Jim Dirkes jim at buildingperformanceteam.com
Thu Jun 25 13:44:14 PDT 2015


A dissertation, no less.  FYI, I think I 'll be able to absorb this
sometime next week, but not sooner.
Thank you for sending it; I don't think I attended your talk because all of
this is sounding new... and I like learning.

On Thu, Jun 25, 2015 at 11:48 AM, Maria Karpman <
maria.karpman at karpmanconsulting.net> wrote:

> Jim,
>
>
>
> FYI attached is a presentation from 2014 ASRHAE/IBPSA summarizing our
> findings on accuracy of the calibrated simulations based on projects that
> went through an incentive program in NJ. (You presented there also – loved
> your Sherlock Holmes hat and pipe J.) To clarify the language, ECM
> savings estimated via calibrated simulation are referred in the
> presentation as “projected savings”; ECM savings estimated via “whole
> building” approach are referred to as “realized savings”.  Slide 7
> summarizes high level results – while there was a relatively good alignment
> between the total projected and realized savings for the entire sample,
> individual projects were all over the map.
>
>
>
> I agree with your last point below that the greatest source of error is
> too little measured data, but given the finite budget that each project
> has, I hoped that documents such as Guideline 14 would emphasize the link
> between the minimum scope of measurements and ECMs considered for a given
> project. For example Guidelines 14 says the following concerning simulation
> defaults, which in my opinion is impractical:
>
>
>
> *5.3.3.3.7 Minimizing Default Values. *Check and thoroughly
>
> understand all default input variables in the simulation
>
> program, as many of the default values have little resemblance
>
> to the actual building being simulated. The fewer the
>
> number of default values used, the more representative the
>
> simulation will be—but only if the changes are well reasoned.
>
> This also includes inspection of the default performance
>
> curves of the various systems and plant equipment, because
>
> such curves can significantly impact the results of the simulation.
>
> Any program default values that are altered, however,
>
> should be well documented.
>
>
>
> There is also a section on adjusting infiltration / ventilation rates to
> calibrate the model (quoted below), but it should only apply if project
> does not include ECMs involving air-sealing (which are common in
> multifamily and school projects) or ventilation controls such as DCV. If
> project includes infiltration or ventilation related measures, the related
> inputs cannot be used to calibrate the model even as “a last resort”.
>
>
>
> *5.3.3.3.6 Estimating Infiltration Rates. *Infiltration
>
> rates are difficult to measure and may be treated as an
>
> unknown that is iteratively solved with the simulation program
>
> once the other major parameters are determined. This
>
> approach is only recommended as a last resort. To solve for
>
> the infiltration rate and/or the ventilation rate iteratively, conduct
>
> a series of simulation runs such that only the infiltration
>
> and/or ventilation rates are changed from one-tenth to as
>
> much as ten times the expected rates. Next, compare the simulation
>
> outputs produced to the measured building data as discussed
>
> below. In addition, supporting evidence should be
>
> used to justify the final choice of variables.
>
>
>
> I think calibrated simulation should be treated as an extension of
> retrofit isolation approach, and site measurements performed in support of
> simulation must at minimum include (a) parameters that are modified to
> model ECM savings, and (b) simulation inputs that do not change but drive
> ECM savings. For lighting fixture replacement ECM, pre-retrofit wattage
> would be an example of (a), and lighting runtime hours would be an example
> of (b). On the other hand, if there are no ECMs that reduce base load (e.g.
> plug load, lighting, etc.), but there are ECMs involving HVAC controls,
> site measurements should focus on operation of existing controls, and
> establishing existing lighting wattage is not necessary because model
> calibrated to capture the overall base load (lighting + plug loads) usage
> may be good enough in this case. We had to develop guidelines for incentive
> programs that rely on calibrated simulation to specify minimum scope of
> measurements and acceptable “estimated” inputs for each common ECM type. I
> hope that a similar guidance would be included at some point in one of
> ASHRAE standards / guidelines. As it stands now, there is a sea of
> difference between the specificity of simulation requirements in 90.1
> Appendix G compared to calibrated simulation of existing buildings. Unless
> I am missing some key reference?
>
>
>
> Maria
>
>
>
> --
>
> *Maria Karpman *LEED AP, BEMP, CEM
>
> ________________
>
> Karpman Consulting
>
> www.karpmanconsulting.net
>
> Phone 860.430.1909
>
> 41C New London Turnpike
>
> Glastonbury, CT 06033
>
>
>
> *From:* Jim Dirkes [mailto:jim at buildingperformanceteam.com]
> *Sent:* Thursday, June 25, 2015 4:49 AM
> *To:* Justin Spencer
> *Cc:* Maria Karpman; Building Simulation
>
> *Subject:* Re: [Bldg-sim] Energy model calibration - normalizing the
> utility bills to month start-end
>
>
>
> Excellent insights in this thread!
>
> Our "two cents":
>
>    - We measure key variables such as larger motor kW and outdoor
>    airflow.  The idea is to eliminate uncertainty for energy aspects that we
>    know are "big".  Because it's easy to obtain, we use the actual weather
>    data.  It's not always a big impact, but eliminates needless uncertainty.
>    - We don't trust people's memory about almost anything; it's amazing
>    how different the story can be when told by two different people, both of
>    whom should be knowledgeable.
>    - We also don't trust sensor calibration.  Not all sensors matter as
>    much, however; discharge sensors on reheat systems, for example, matter
>    more than room temp sensors.
>    - More data is better.  Smart meters are very helpful, as is trend
>    data if the client has taken time to set them up. Not many do :(
>    - We have not yet tinkered with sensitivity analysis for uncertain
>    variables such as boiler efficiency or infiltration, but we do run GenOpt
>    for the best R-squared fit to each (exact) billing period and review /
>    revise the output for reasonableness.
>    - On the one hand, it bothers me that calibrated models are "grossly
>    incorrect" in Maria's experience.  My bias is that those models may have
>    too little measured data.  On the other hand, this is a brave new world and
>    we probably all have a lot to learn.
>
>
>
> On Tue, Jun 23, 2015 at 4:51 PM, Justin Spencer <jspencer17 at gmail.com>
> wrote:
>
> I'll second that this has turned into a really good thread. My personal
> rule of thumb in doing calibration is, "don't mess with things that relate
> directly to an ECM." If you have a retrofit ECM going on, you should have
> detailed data on the pre-installation conditions associated with that ECM.
> If that isn't documented, your model is never going to provide you with
> accurate savings results. It pays to think about these things at the
> fundamental level. And also apply a reasonableness range to those values.
> What am I most unsure about? What's the possible range of this value?
> Personally, I twist my dials all in one direction and then use setpoint as
> my fine calibration factor at the end. We need to remember that we're using
> models as tools to help guide decision-making. When we make decisions in
> calibration, we should think about how it will impact the decision being
> made -- does it impose a bias (like the 35% assumed pre-retrofit boiler
> efficiency for a boiler retrofit project)? These models are just tools for
> extrapolating from known energy consumption data to unknown energy
> consumption data.
>
>
>
> An earlier post commented that hourly data might make calibration
> easier... It doesn't make it easier in the sense of less work, but it does
> make your model a lot better if you do it right. Calibrating models to real
> hourly consumption data tells you so much about what is likely wrong with
> your model and so much about how your real building performs. If you have
> hourly end use data, or close, then you are really in business. At the
> aggregate level, this kind of data can tell you that you didn't really know
> much to start with. For one project for Con Edison, we had access to hourly
> whole-premise consumption data for several thousand NYC buildings by
> building type. We were able to use this data to uncover that our models
> vastly underestimated consumption at nights and on weekends. We didn't know
> why, but we altered schedules accordingly.
>
>
>
> As for the monthly billing data question at the beginnning of this thread,
> if you're dealing with a building and climate where monthly variability in
> occupancy and weather are driving changes in energy consumption in smooth,
> continuous fashion, you can make some other approximations. We've used a
> simplified slope method, where you calculate the slope in usage between the
> two adjacent billing months, i.e. if period 1 is 1000 kWh/day, period 2 is
> 1100 kWh/day, and period 3 is 1200 kWh/day, and all are 30 days long, you
> wind up with a slope of 3.33 kWh/day. We then assign daily kWh for each
> month using this slope and then reslice the data to match our calendar
> months. This is important when you are calibrating an aggregate model,
> which is a common need for us when we're estimating program-level savings
> for an ECM. For example, we're using a building simulation model to
> extrapolate measured consumption at a large sample of sites to usage in a
> typical year.
>
>
>
> Alternatively, for individual buildings, we don't worry about it and just
> calibrate to the billing periods instead. We're not generally working in
> eQuest, but rather with the hourly output of whatever engine we're using.
>
>
>
> One thing I've been wondering about recently is how to avoid
> over-calibration. The econometrics/statty/mathy folks I occasionally
> consort with talk a lot about overfitting of regressions and the same
> problem applies here. Has anybody ever tried reserving part of their
> calibration data set to use as a test set? I've gotten some things that
> looked like really great calibrations in the past, but I'm wondering how
> folks have sought to prove whether they were getting things right or
> overfitting. When I teach junior staff about this sort of thing, I always
> include something about not getting too cute. I point them to the John Von
> Neumann quote:
>
> ·         *With four parameters I can fit an elephant
> <https://en.wikiquote.org/wiki/Elephant>, and with five I can make him
> wiggle his trunk.*
>
>
>
> On Tue, Jun 23, 2015 at 1:04 PM, Maria Karpman <
> maria.karpman at karpmanconsulting.net> wrote:
>
> Jim and all,
>
>
>
> We have incentive programs in NY (Multifamily Performance Program) and NJ
> (Pay for Performance Program for C&I buildings, P4P) that require
> developing calibrated models to estimate ECM savings. Both programs rely on
> spreadsheet-based tools to facilitate model calibration, and require that
> the proposed ECM package reduces overall energy consumption by at least
> 15%. The programs have been around for 5+ years, and have hundreds of
> participating projects. Part of the incentive is awarded based on the
> projected (i.e. modeled) savings, and the rest (as much as 50% for P4P)
> based on the actual achieved savings established using Whole Building
> approach by comparing pre/post utility bills.
>
>
>
> My biggest take away from the involvement with these programs was that a
> calibrated model that meets MBE,  CVRMSE, and uncertainty requirements of
> Guideline 14 may produce grossly incorrect ECM savings. It is not feasible
> to create a calibrated model that can be reliably used to project savings
> from any conceivable ECM in a commercial non-research setting because,
> aside from the modeling effort, it would require a lot of very detailed
> field work. On the other hand, developing a calibrated simulation to
> estimate savings from a particular set of measures considered for a given
> project is a much more manageable task. So the bulk of the recent updates
> to the technical requirements of our incentive programs were focused on
> itemizing parameters that should be tweaked to achieve calibration
> depending on the ECMs included in the project scope. For example, if
> project involves boiler replacement, efficiency of existing boilers that
> are being replaced must be measured. (We had a project which modeled
> existing boiler as 35% efficient because that produced calibrated
> simulation. Of course such model would very likely exaggerate savings from
> installing a new boiler.)
>
>
>
> Do any of you know references that outline calibration techniques
> depending on the ECMs being modeled, beyond the general advice included in
> IPM&VP?
>
>
>
> Thanks,
>
>
>
> Maria
>
>
>
> --
>
> *Maria Karpman *LEED AP, BEMP, CEM
>
> ________________
>
> Karpman Consulting
>
> www.karpmanconsulting.net
>
> Phone 860.430.1909
>
> 41C New London Turnpike
>
> Glastonbury, CT 06033
>
>
>
> *From:* Bldg-sim [mailto:bldg-sim-bounces at lists.onebuilding.org] *On
> Behalf Of *Jim Dirkes
> *Sent:* Tuesday, June 23, 2015 1:31 PM
>
>
> *To:* bldg-sim at lists.onebuilding.org
> *Subject:* Re: [Bldg-sim] Energy model calibration - normalizing the
> utility bills to month start-end
>
>
>
> I'm encouraged to see so many people addressing this topic because it
> means you are modeling existing buildings; a lot of work is needed in this
> arena.  Keep it up!
>
>
>
> We, as usual, have a spreadsheet solution.  In this case, the spreadsheet
> is happy to use billing periods of any length, such as is normal for
> day-of-reading variations, but also to combine "estimated" readings into a
> period that has an actual reading at each end.
>
> It requires that you tell EnergyPlus to report hourly meter data for each
> fuel (e.g., electricity and natural gas).  A macro totals that data into
> the billing periods for your site, displays the predicted vs actual energy
> and calculates an R-squared value for each fuel and the total. An example
> is shown below.
>
>
>
> [image: Inline image 1]
>
>
>
> On Tue, Jun 23, 2015 at 12:02 PM, Maria Karpman <
> maria.karpman at karpmanconsulting.net> wrote:
>
> Hello all,
>
>
>
> We usually do the following to calibrate model to monthly utility bills:
>
> 1)      Create or purchase weather file corresponding to pre-retrofit
> period for which we have billing data. Lately we’ve been using
> WeatherAnalytics files, which we found to be more cost effective than
> creating our own (they charge $40 for an annual file).
>
> 2)      Run simulation using this weather file instead of TMY.
>
> 3)      Standard simulation reports (we typically use eQUEST) show usage
> by calendar month (e.g. January, February, etc.) which is usually not
> aligned with dates of utility bills, as noted in the question that started
> this thread. As Brian mentioned in one of the earlier posts, this may be
> circumvented by entering the actual meter read dates into eQUEST as shown
> in the screenshot below. This will align usages shown in eQUEST’s “E*”
> reports such as ES-E with the actual utility bills.  The approach does not
> allow entering more than one read date per month (e.g. we can’t capture
> April 3 – 28 bill). For projects where this limitation is an issue we
> generate hourly reports that show consumption by end use for each meter in
> the project, and aggregate it into periods that are aligned with utility
> bills.
>
>
>
> 4)      We then copy simulation outputs (either from ES-E or hourly
> reports, depending on the method used) into a standard spreadsheet with
> utility data. The spreadsheet is set up to plot side by side monthly
> utility bills and simulated usage, and also calculates normalized mean bias
> error (NMBE) and variance CV(RMSE).
>
> 5)      If we did not to where we want to be with NMBE and CV(RMSE) we
> adjust and re-run the model, and re-paste results into the same
> spreadsheet.
>
>
>
> In my experience regression analysis using weather as independent variable
> (i.e. running model with TMY file and normalizing for difference in
> weather) or relying on HDD to allocate usage to billing periods can be very
> misleading, mainly because on many projects weather is not the main driver
> of consumption. For example energy usage of a school during a given time
> period depends much more on vacation schedule than outdoor dry bulb
> temperatures.
>
>
>
> Thanks,
>
>
>
> --
>
> *Maria Karpman *LEED AP, BEMP, CEM
>
> ________________
>
> Karpman Consulting
>
> www.karpmanconsulting.net
>
> Phone 860.430.1909
>
> 41C New London Turnpike
>
> Glastonbury, CT 06033
>
>
>
> *From:* Bldg-sim [mailto:bldg-sim-bounces at lists.onebuilding.org] *On
> Behalf Of *Jeff Haberl
> *Sent:* Tuesday, June 23, 2015 10:16 AM
> *To:* Joe Huang; bldg-sim at lists.onebuilding.org
>
>
> *Subject:* Re: [Bldg-sim] Energy model calibration - normalizing the
> utility bills to month start-end
>
>
>
> Hello Joe,
>
>
>
> Yes, you can count the degree days and regress against that to show a
> correlation. However, one will get a better "fit" to the weather data if
> you regress to the degree day that is calculated for the balance point
> temperature of the building -- hence the inverse model toolkit or the
> variable based degree day method.
>
>
>
> PRISM actually calculates the degree days to a variety of change points
> and actually provides a table for each location that you use as a look up.
> The IMT will actually perform a variable based degree day calculation that
> agrees well with PRISM. IMT will also provide you with the average daily
> temperature for the billing period.
>
>
>
> When using DOE-2 for actual billing periods, one will have to extract the
> appropriate hourly variable, sum it to daily and then regroup to align with
> the billing periods. Here's a chunk of code that will create a dummy plant,
> display PV-A, PS-A, PS-E and BEPS, and extract the relevant hourly
> variables to normalize the BEPS to the utility bills:
>
>
>
> INPUT PLANT ..
>
>
>
> PLANT-REPORT VERIFICATION = (PV-A)
>
> $ PV-A, EQUIPMENT SIZES
>
>
>
> SUMMARY = (PS-A,PS-E,BEPS)
>
>
>
> $ PS-A, PLANT ENERGY UTILIZATION SUMMARY
>
> $ PS-E, MONTHLY ENERGY END USE SUMMARY
>
> $ BEPS, BUILDING ENERGY PERFORMANCE SUMMARY
>
>
>
> HVAC=PLANT-ASSIGNMENT ..
>
>
>
> $ EQUIPMENT DESCRIPTION
>
> $ ELECTRIC DOMESTIC WATER HEATER
>
>
>
> BOIL-1 =PLANT-EQUIPMENT TYPE=ELEC-DHW-HEATER SIZE=-999 ..
>
>
>
> $ ELECTRIC HOT-WATER BOILER
>
>
>
> BOIL-2 =PLANT-EQUIPMENT TYPE=ELEC-HW-BOILER SIZE=-999 ..
>
>
>
> $ HERMETICALLY SEALED CENT CHILLER
>
>
>
> CHIL-1 =PLANT-EQUIPMENT TYPE=HERM-CENT-CHLR SIZE=-999 ..
>
>
>
> $ Graphics block for Data Processing ***
>
>
>
> RP-3 = SCHEDULE THRU DEC 31 (ALL) (1,24) (1) ..
>
>
>
> $ 8 = Total PLANT heating load (Btu/h)
>
> $ 9 = Total PLANT cooling load (Btu/h)
>
> $ 10 = Total PLANT electric load (Btu/h)
>
>
>
> BLOCK-3-1 = REPORT-BLOCK
>
> VARIABLE-TYPE = PLANT
>
> VARIABLE-LIST = (8,9,10) ..
>
> BLOCK-3-2 = REPORT-BLOCK
>
> VARIABLE-TYPE = GLOBAL
>
> VARIABLE-LIST = (1) ..
>
> HR-3 = HOURLY-REPORT
>
> REPORT-SCHEDULE = RP-3
>
> REPORT-BLOCK = (BLOCK-3-1,BLOCK-3-2) ..
>
>
>
> END ..
>
>
>
> COMPUTE PLANT ..
>
>
>
> STOP ..
>
>
>
> 8=!  8=)  :=)  8=)  ;=)  8=)  8=(  8=)  8=()  8=)  8=|  8=)  :=')  8=) 8=?
> Jeff S. Haberl, Ph.D.,P.E.inactive,FASHRAE,FIBPSA,......jhaberl at tamu.edu
> <........jhaberl at tamu.edu>
> Professor........................................................................Office
> Ph: 979-845-6507
> Department of Architecture............................................Lab
> Ph:979-845-6065
> Energy Systems Laboratory...........................................FAX:
> 979-862-2457
> Texas A&M
> University...................................................77843-3581
> College Station, Texas, USA, 77843.............................
> http://esl.tamu.edu
> 8=/  8=)  :=)  8=)  ;=)  8=)  8=()  8=)  :=)  8=)  8=!  8=)  8=? 8=) 8=0
> ------------------------------
>
> *From:* Bldg-sim [bldg-sim-bounces at lists.onebuilding.org] on behalf of
> Joe Huang [yjhuang at whiteboxtechnologies.com]
> *Sent:* Monday, June 22, 2015 9:17 PM
> *To:* bldg-sim at lists.onebuilding.org
> *Subject:* Re: [Bldg-sim] Energy model calibration - normalizing the
> utility bills to month start-end
>
> Maybe I'm missing something here, but why can't you just count up the
> degree days for the utility period?
> I hope you're not working with average or "typical year" degree days, but
> the degree days from the same time period.
>
> I also recall that the old Princeton Scorekeeping Method (PRISM) back in
> the 1980's allows the user to enter the degree days for that time period,
> so it's not a new problem.
>
> Joe
>
> Joe Huang
>
> White Box Technologies, Inc.
>
> 346 Rheem Blvd., Suite 205A
>
> Moraga CA 94556
>
> yjhuang at whiteboxtechnologies.com
>
> http://weather.whiteboxtechnologies.com for simulation-ready weather data
>
> (o) (925)388-0265
>
> (c) (510)928-2683
>
> "building energy simulations at your fingertips"
>
> On 6/22/2015 6:09 AM, Jones, Christopher wrote:
>
> When calibrating an energy model to utility bills the utility bills often
> don’t align with the month start and end.  I have reviewed a couple methods
> to calendar normalize the utility bills but find them somewhat
> unsatisfactory.
>
>
>
> For example the method I am looking at does the following:
>
> The April gas bill runs from March 25 – April 24.  The algorithm takes the
> average number of m3 per day from that bill, applies it to the days in
> April.  Then it takes the average number of days from the May bill which
> runs from April 24 – May 25 and applies that average to the remaining days
> in April.
>
>
>
> The issue is that the March-April period has much higher HDD than the
> April-May period and the “normalized” gas usage is significantly lower than
> the simulation data for April.
>
>
>
> I am wondering if there are any papers or other sources of information as
> to how others approach this problem.
>
>
>
>
>
> [image: cid:image003.png at 01D09C46.E75BA0D0]
>
> *Christopher Jones,*P.Eng.
> Senior Engineer
>
>
>
> *WSP Canada Inc.*
>
> 2300 Yonge Street, Suite 2300
>
> Toronto, ON M4P 1E4
> T +1 416-644-4226
>
> F +1 416-487-9766
>
> C +1 416-697-0065
>
>
>
> www.wspgroup.com
>
>
> ------------------------------
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>
>
> --
>
> James V Dirkes II, PE, BEMP, LEED AP
> CEO/President
> The Building Performance Team Inc.
> 1631 Acacia Dr, GR, Mi 49504
>
> Direct: 616.450.8653
> jim at buildingperformanceteam.com
>
> Website <http://buildingperformanceteamcom>l  LinkedIn
> <https://www.linkedin.com/pub/jim-dirkes/7/444/413>
>
> Studies show that four out of every three people have a hard time with
> math.
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>
>
>
>
> --
>
> James V Dirkes II, PE, BEMP, LEED AP
> CEO/President
> The Building Performance Team Inc.
> 1631 Acacia Dr, GR, Mi 49504
>
> Direct: 616.450.8653
> jim at buildingperformanceteam.com
>
> Website <http://buildingperformanceteamcom>l  LinkedIn
> <https://www.linkedin.com/pub/jim-dirkes/7/444/413>
>
> Studies show that four out of every three people have a hard time with
> math.
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-- 

James V Dirkes II, PE, BEMP, LEED AP
CEO/President
The Building Performance Team Inc.
1631 Acacia Dr, GR, Mi 49504

Direct: 616.450.8653
jim at buildingperformanceteam.com

Website <http://buildingperformanceteamcom>l  LinkedIn
<https://www.linkedin.com/pub/jim-dirkes/7/444/413>

 Studies show that four out of every three people have a hard time with
math.
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