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

[Jim Dirkes]

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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