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 U.S. DEPARTMENT OF ENERGY

 OFFICE OF ENERGY EFFICIENCY AND RENEWABLE ENERGY

 BUILDING TECHNOLOGIES OFFICE

ENERGY CONSERVATION PROGRAM FOR CONSUMER PRODUCTS:

 ENERGY CONSERVATION STANDARDS FOR RESIDENTIAL

 FURNACES

 TUTORIAL SPREADSHEET MEETING

 CALL # 49279-01-EE-5B

 WEDNESDAY, NOVEMBER 7, 2014

 The above-referenced working group met

in Room 4A-104, 1000 Independence Avenue SW,

Washington, D.C., 20585 at 9:00 a.m., John

Cymbalsky, presiding.
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1 P R E S E N T

2 ASHLEY ARMSTRONG,

 Office of General Counsel, U.S. Department of

3 Energy

4 DAN COHEN,

 Office of General Counsel, U.S. Department of

5 Energy

6 JOHN CYMBALSKY,

 Office of Energy Efficiency and Renewable

7 Energy, Building Technologies Office, U.S.

 Department of Energy

8

ERIC STAS,

9 Office of the General Counsel, U.S.

 Department of Energy

10

 A L S O P R E S E N T

11

ADAM DARLINGTON,

12 Navigant Consulting

13 RACHEL FEINSTEIN,

 Hearth, Patio and Barbecue Association

14

VICTOR FRANCO,

15 Lawrence Berkeley National Laboratory

16 LAURA PETRILLO-GROH,

 Air Conditioning, Heating and Refrigeration

17 Institute

18 PETER HANSON,

 Council on Environmental Quality

19

MARSHALL HUNT,

20 Pacific Gas and Electric Company

21 ROGER HUNT,

 Lennox Industries

22
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1 DIANE JACOBS,

 Rheem Manufacturing

2

CHRIS LAU,

3 Navigant Consulting

4 ALEX LEKOV,

 Lawrence Berkeley National Laboratory

5

NEIL LESLIE,

6 Gas Technology Institute

7 JACKIE LONG,

 Toxicology Council

8

TOM MASSARO,

9 New Jersey Natural Gas

10 WILLIAM MILLER,

 McCarter & English;

11 General Counsel, American Public Gas

 Association

12

NICK MISLAK,

13 Air Conditioning, Heating and Refrigeration

 Institute

14

ELIZABETH NOLL,

15 National Resource Defense Council

16 HENRI ADROIT (sp),

 Goodman Manufacturing

17

ANDREA PAPAGEORGE,

18 AGL Resources

19 GREG ROSENQUIST,

 Lawrence Berkeley National Laboratory

20

DAVE SCHROEDER,

21 Consultant, Gas Technology Institute

22 DAVE SCHRYVER,

 American Public Gas Association
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1 DON SORINO,

 National Association of Home Builders

2

FRANK STANONIK,

3 Air Conditioning, Heating and Refrigeration

 Institute

4

ALISON WILLIAMS,

5 Lawrence Berkeley National Laboratory

6 DAVE WINNINGHAM,

 Allied Air Enterprises

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1 C O N T E N T S

2 Welcome and Introductions

3 John Cymbalsky 7

4 Opening Remarks 11

5 Spreadsheet Tutorial

6 Victor Franco

7 Introduction 19

8 LCC Spreadsheet 47

9 NIA Spreadsheet 150

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1 P R O C E E D I N G S

2 MR. CYMBALSKY: Okay. Welcome to the

3 public meeting to demonstrate our spreadsheet

4 analytical tools. My name is John Cymbalsky. I

5 am the program manager for appliance standards

6 here at DOE. I want to first welcome everybody

7 and thank everybody for coming.

8 Today's workshop will focus on

9 presenting our analytical spreadsheets that we use

10 for all of our rulemakings, and the purpose here

11 is to give interested stakeholders an overview and

12 maybe a little more in-depth knowledge of how to

13 use the spreadsheets, how to find certain key data

14 inputs and whatnot.

15 What the meeting is not about, and our

16 general counsel will speak more specifically on

17 this, it's not about the furnace proposal that is

18 at OIRA. So questions about, you know, why this or

19 how that are deliberative to the rulemaking and we

20 will answer at the public meeting for the NOPR in

21 due course when that comes out of OIRA.

22 Also, the meeting today is being
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1 broadcast on the Web via webcast. People on the

2 webcast can ask questions by raising their hand.

3 We will try to patch you in, in audio. If that

4 doesn't work, we will write down your question and

5 read it.

6 Now, at this time, I'd actually like to

7 go around the room, everyone introduce themselves.

8 A few ground rules, when you do have a question,

9 the microphone has a little button. Push it on.

10 Green light will come on to speak. When you're

11 done, please push it off so we don't get feedback.

12 And again, state your name and association every

13 time you'd like to speak. So we'll start over

14 here to my left with Bud.

15 MR. MILLER: Thank you, John. My name

16 is William Miller, with McCarter and English,

17 general counsel for the American Public Gas

18 Association.

19 MR. ADROIT: Henri Adroit, Goodman

20 Manufacturing.

21 MR. LESLIE: Neal Leslie, Gas Technology

22 Institute.
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1 MR. SCHROEDER: Dave Schroeder,

2 consultant for the Gas Technology Institute.

3 MS. PETRILLO-GROH: Laura Petrillo-Groh,

4 Air Conditioning, Heating and Refrigeration

5 Institute.

6 MS. PAPAGEORGE: Good morning. Andrea

7 Papageorge, AGL Resources in Atlanta.

8 MS. JACOBS: Diane Jacobs, Rheem

9 Manufacturing.

10 MS. NOLL: Elizabeth Noll, NRDC.

11 MR. M. HUNT: Marshall Hunt, Pacific Gas

12 and Electric Company.

13 MR. WINNINGHAM: Dave Winningham, Allied

14 Air Enterprises.

15 MR. MASSARO: Tom Massaro, New Jersey

16 Natural Gas.

17 MR. R. HUNT: Roger Hunt, Lennox

18 Industries.

19 MR. STAS: Eric Stas, DOE general

20 counsel's office.

21 MR. FRANCO: Victor Franco, Lawrence

22 Berkeley National Laboratory.
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1 MR. ROSENQUIST: Greg Rosenquist,

2 Lawrence Berkeley National Laboratory.

3 MR. COHEN: Dan Cohen, general counsel's

4 office, DOE.

5 MR. LAU: Chris Lau, Navigant.

6 MR. LEKOV: Alex Lekov, Lawrence

7 Berkeley National Laboratory.

8 MR. DARLINGTON: Adam Darlington,

9 Navigant.

10 MR. CYMBALSKY: And those in the

11 gallery, if you could just stand and speak loudly

12 and introduce yourself.

13 MR. HANSON: Peter Hanson, Council on

14 Environmental Quality.

15 MS. LONG: Jackie Long, Toxicology

16 Council.

17 MR. SCHRYVER: Dave Schryver, APGA.

18 MR. MISLAK: Nick Mislak, Air

19 Conditioning, Heating and Refrigeration Institute.

20 MS. WILLIAMS: Alison Williams, LBL.

21 MR. SORINO: I'm Don Sorino with the

22 National Association of Home Builders.
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1 MS. FEINSTEIN: Rachel Feinstein, with

2 the Hearth, Patio and Barbecue Association.

3 MR. STANONIK: Frank Stanonik, Air

4 Conditioning, Heating and Refrigeration Institute.

5 MR. CYMBALSKY: Okay. So I think we've

6 got everybody introduced. And at this time, I'm

7 going to turn it over to Eric for a quick

8 statement.

9 MR. STAS: Okay. This is Eric Stas, DOE

10 general counsel's office. I'd like to reiterate

11 John's welcome and thank you all for coming to the

12 meeting today, and I just wanted to say a few

13 words about the overall purpose of the meeting

14 today and the types of questions DOE will and will

15 not be answering.

16 So as John said, DOE has convened this

17 meeting as a tutorial on the department's analytic

18 tools and spreadsheets used across DOE's appliance

19 rulemakings. So we'll discuss as necessary the

20 models and inputs for conducting the Monte Carlo

21 analysis and other analyses that generate data and

22 results used in those rulemakings.
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1 The goal is to allow interested parties

2 to understand and work with those analytical

3 tools, thereby gaining insight into how DOE

4 generates the data and results for any given

5 rulemaking. So and to illustrate these analytical

6 tools, DOE has made available its preliminary

7 results for the ongoing rulemaking to consider

8 amended energy conservation standards for

9 residential, non-weatherized gas furnaces and

10 mobile home gas furnaces.

11 But as John said, please note that this

12 data is being offered for illustrative purposes

13 only as part of this tutorial, and DOE will not be

14 answering questions pertaining to any policy

15 decision, model assumptions or other deliberative

16 matters regarding the draft furnaces at this time.

17 The notice of proposed rulemaking, as John said,

18 for the amended furnaces standards has been

19 submitted to OMB for interagency review, and

20 consequently the document's not yet publicly

21 available. It's in draft form, and it's subject

22 to change.
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1 So once the document has been published,

2 however, DOE will convene another public meeting

3 to discuss and receive comments on the proposed

4 rule and at that time, DOE will entertain any

5 questions regarding the proposed standards, policy

6 assumptions, data, policy decisions and there will

7 be another opportunity for public comment and data

8 submission.

9 So focusing again today, the purpose of

10 today's meeting is to enable stakeholders to

11 understand and utilize DOE's spreadsheets and

12 analytical tools, thereby facilitating stakeholder

13 review of the proposed rule for furnaces and any

14 other DOE rule and to enhance the opportunity for

15 public -- meaningful public comment. So if we can

16 stick to those sort of ground rules, it'll

17 facilitate the meeting.

18 MR. CYMBALSKY: Okay. Thanks, Eric. I

19 do want to mention that the meeting runs to 1:00.

20 We do have a hard stop at that time. If we feel

21 like we need to reconvene to go into more detail

22 on some of the spreadsheets, obviously DOE will
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1 entertain that request. So at this time, we'll

2 open the floor for opening remarks and/or

3 questions about what Eric just stated.

4 MR. MILLER: I guess to the extent

5 questions are offensive from the standpoint of

6 what Eric just said, we'll be told at the

7 appropriate time. I mean, APGA -- first of all, I

8 would just say APGA appreciates and AGA

9 appreciates that this meeting is being held.

10 We requested it several weeks ago

11 because of our inability to understand these very

12 complex spreadsheets. And at Dan Cohen's request,

13 we submitted questions and hope you will proceed

14 to those questions shortly. We have not heard

15 back about those questions. So we're assuming

16 those questions passed muster in terms of the

17 standards you just articulated. But I guess we'll

18 be told that as we go through this exercise.

19 We would like to proceed through the

20 questions in the order in which they were

21 submitted. Obviously, when we get answers, there

22 will be follow- up questions and answers I'm sure.
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1 And you know, I made that clear in my email to Dan

2 Cohen.

3 So we appreciate DOE having this

4 meeting. We hope to come out of it with a much

5 better understanding of the spreadsheets and how

6 they operate than we have today. And also,

7 obviously if there's a follow-up meeting after a

8 rule -- proposed rule issues, that will be

9 important as well. And we would have much

10 different questions I'm sure at that meeting than

11 we have today.

12 But with that, subject to other folks

13 saying whatever they want to say, I would turn it

14 over to Neil Leslie and his colleagues at GTI to

15 march through the questions that we have already

16 submitted to DOE. Thank you.

17 MR. CYMBALSKY: Anybody else wish to

18 make opening remarks? Okay, Frank?

19 MR. STANONIK: Frank Stanonik, AHRI.

20 Just I'll say a general prefatory statement. I

21 recognize what Eric has said and what the intent

22 of the meeting is. I'd just ask you to appreciate
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1 that at the moment, our understanding of the

2 spreadsheets is kind of limited to the outputs we

3 see.

4 So I'm going to -- you know, I probably

5 will pose some questions in terms of outputs that

6 I see that, you know, raise questions. And if

7 certainly we get off-chart here or off the task,

8 yeah, as I think was said, we'll assume you'll

9 pull us back.

10 But right now, you know, I'm sitting

11 here thinking the only way I can ask you questions

12 is because I see outputs that I don't understand,

13 relative to how you use the tool. And I

14 understand we're not going to talk about the

15 furnace rulemaking directly.

16 But just kind of, you know, I guess

17 appreciate from our perspective right now, the

18 only way I can relate to that information and the

19 tools is what I'm seeing as outputs.

20 MR. CYMBALSKY: Right, and we appreciate

21 that. John Cymbalsky, DOE. I think maybe a

22 little example might help and frame up how. So a
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1 question, for example, an important aspect of the

2 furnace rulemaking, for example, is fuel

3 switching. And the spreadsheets, the analytical

4 tools have information about fuel switching in

5 them.

6 So an appropriate question in my mind

7 is, DOE, can you point us to in the spreadsheet of

8 where we can find out about how you accounted for

9 fuel switching. And Victor will do that. The

10 inappropriate question is obviously the question

11 you probably want to ask but I don't think I need

12 to ask it. But when you do, we'll tell you we

13 can't answer it. Does that help?

14 MR. STANONIK: Well --

15 MR. CYMBALSKY: I know, it doesn't -- it

16 doesn't help-help, but --

17 MR. STANONIK: My intent will not be to

18 ask those question, but you know, without getting

19 a better sense of how we -- I'm sorry. My intent

20 will be not to ask those questions because I

21 understand what's been said here. But until we

22 get a little further along and get a good sense of
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1 it, I mean, your example helps. Thanks.

2 MR. CYMBALSKY: Okay, anybody else wish

3 to make an opening statement. Okay, so for full

4 disclosure on Bud's -- oh, I'm sorry.

5 MS. JACOBS: Diane Jacobs from Rheem.

6 So I was here yesterday and you said that the

7 spreadsheet, the same format is used in all the

8 rulemakings or most of them, all of them. So I

9 think that it's good to point out in other

10 contexts, you know, what -- how it's used and it

11 seems like maybe there's parts that come in or

12 come out depending on the rulemaking. So I'm

13 really interested in how it's applied in general.

14 Thank you.

15 MR. CYMBALSKY: Right, and this is John

16 again from DOE. So that's right. So for example,

17 the life cycle cost spreadsheet, it fundamentally

18 does the same thing in all the rulemakings. Now,

19 for furnaces, there are different aspects that

20 need to be accounted for that you won't see in

21 like a refrigerator spreadsheet.

22 So I'm assuming questions today, because
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1 this one in my mind's probably a little more

2 complicated than most, will be specific to

3 furnaces. But at the same time, we're asking a

4 question about, you know, how in the analytical

5 tools can we access that and maybe play with some

6 of the parameters, et cetera. So that carries

7 over to all the other products where applicable.

8 So any other opening remarks? So to

9 address Mr. Miller's comments about questions, so

10 for full disclosure, there were questions that

11 were sent to DOE. The way we want to try to

12 answer the questions that we can answer, because

13 it is a subset -- we'll be honest with you -- as

14 Victor goes through the spreadsheet, he is aware

15 of the issues and he questions that were raised,

16 and we will try to answer them interactively with

17 the spreadsheet demonstration. And if that doesn't

18 suffice, of course you're free to ask your

19 questions as well. So yes, Neil?

20 MR. LESLIE: This is Neil Leslie, GTI.

21 Just a clarifying thing on when it's appropriate

22 to ask the questions as the spreadsheet is being
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1 described.

2 MR. CYMBALSKY: Yes, so --

3 MR. LESLIE: When do you want them?

4 Anytime or just wait until the end or how do you

5 want to do that?

6 MR. CYMBALSKY: No, no. So John, DOE,

7 so if Victor's on cell AA 135, and that sparks

8 your question about something, you ask the

9 question and we'll answer it. Anybody else?

10 Okay, all right. Let's move on. So at this point,

11 Victor is going to be our point man for bringing

12 you through the analysis in the spreadsheet. So

13 we are going to switch spots and take it from

14 there.

15 MR. FRANCO: Hi. Good morning. I'm

16 Victor Franco from Lawrence Berkeley National

17 Laboratory and I will be going through the

18 spreadsheets. I will be going through actually

19 two spreadsheets, the life cycle cost analysis

20 spreadsheet and the national impact analysis

21 spreadsheet.

22 This is kind of -- this flow chart is
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1 going to show you more or less what's the flow and

2 the connection between the two. So we have various

3 inputs. These are just some of the primary inputs.

4 So some of the analysis that goes in, for example,

5 is manufactured cost that comes from the

6 engineering analysis, markups to determine that

7 product price, energy use characterization and

8 then there's a lot of other inputs that we'll go

9 into.

10 The outputs that come out of that, the

11 results are LCC savings and the payback period.

12 Also, out of that, there's LCC outputs that are

13 some of the primary inputs to the national impact

14 analysis spreadsheet. In addition, there's the

15 shipments analysis that goes into that spreadsheet

16 as well. And from that spreadsheet, we have the

17 national energy savings and net present value.

18 So as I go through these introductory

19 slides are just to show you more or less to remind

20 you about the analysis, what we're trying to do in

21 the spreadsheet and how -- what worksheets we're

22 going to be going through and how they are set up,
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1 and hopefully that will make it a little bit

2 clearer as I go through the spreadsheets, what

3 part of the analysis I'm going through.

4 So this is a flow chart that we usually

5 present in the rulemakings just to show the flow

6 of the LCC and payback period analysis.

7 Basically, the yellow presents the final results,

8 payback period and life cycle cost. And the green

9 are the primary inputs -- for example, total

10 installed cost and the annual operating expenses.

11 And then, obviously those have a lot of inputs

12 that each of them actually becomes a worksheet, as

13 we'll see in the next slide -- actually, after

14 this one.

15 So basically, just to remind you, the

16 life cycle cost is total installed cost plus

17 operating cost and it's the operating cost of the

18 lifetime of the product discounted to the base

19 year. And in the case for furnaces, it's 2021.

20 Obviously it's different for each rulemaking.

21 We're evaluating -- yes?

22 MS. JACOBS: Diane Jacobs, from Rheem.
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1 So where do you come up with that year, 2021?

2 MR. FRANCO: So that's the year that the

3 standard is going to take into effect and that

4 just depends on the rulemaking.

5 MR. CYMBALSKY: So this is John from

6 DOE. So again, that's a furnace-specific number.

7 So depending on the rule, it's the first year of

8 compliance. So that changes.

9 MS. JACOBS: Okay.

10 MR. MILLER: Bud Miller. Are these

11 slides available today?

12 MR. CYMBALSKY: We will make slides

13 available. For the meeting, we want to keep this -

14 - we want to keep everyone's eyes focused on the

15 screen because we're going to be doing spreadsheet

16 stuff and having a stack of paper in front of you

17 I don't think is the best way to do that. We will

18 make the printed slides available though.

19 MR. MASSARO: It's Tom Massaro with New

20 Jersey Natural Gas. Victor, a question on the

21 installed cost. It looked like the installation

22 cost wasn't a variable.
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1 MR. FRANCO: This is Victor Franco. Let

2 me go back to the previous slide. So the

3 installation cost is the one that's right before

4 the consumer price. That would be the --

5 MR. MASSARO: Right, right below the

6 consumer?

7 MR. FRANCO: Yes.

8 MR. MASSARO: That's not an input?

9 MR. FRANCO: That is an input.

10 MR. MASSARO: Oh, okay. It wasn't

11 green. All right. I think anything in green was a

12 --

13 MR. FRANCO: Those are kind of like the

14 -- except for consumer price because it has a lot

15 of other inputs. But installation cost is

16 obviously a major input to the total installed

17 cost. Those were the two.

18 MR. MASSARO: Right.

19 MR. CYMBALSKY: So this is John at DOE.

20 So we will get into the spreadsheets and you will

21 see those numbers and where they are.

22 MR. FRANCO: So again, this is we're
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1 looking at the -- we're evaluating the economics

2 through the consumer perspective in the LCC

3 analysis. And to do this in the spreadsheet form,

4 we use the analysis and the variability using a

5 Monte Carlo approach. What that means essentially

6 is for certain inputs we have a distribution and

7 we'll go through that as we look at the

8 spreadsheet.

9 Now, in terms of the tools, and we'll go

10 through this with more time, it's basically to

11 view the tool, you need Excel spreadsheet. Now,

12 to actually understand a little bit more look at

13 the distributions, you'll actually need a program

14 add-in which is called Crystal Ball, and we'll go

15 into more details about that. You don't

16 necessarily need Crystal Ball to look at the

17 spreadsheet.

18 But if you want to run the spreadsheet,

19 want to look more carefully at some of the inputs,

20 you will need that add-in. And we'll go through

21 that.

22 MR. SCHROEDER: Dave Schroeder. So some
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1 variables are coming in through Crystal Ball and

2 some are not. That's correct?

3 MR. FRANCO: Yes. This is Victor

4 Franco. We'll look at that more carefully as we go

5 through the spreadsheet and you'll have an

6 understanding. Most of the variables that are

7 kind of -- that are coming in from Crystal Ball

8 are the ones that are generated by a distribution.

9 We also use Crystal Ball to kind of summarize the

10 results, as we'll go in more detail.

11 MR. SCHROEDER: So is that -- is that

12 the same for every one of these rulemakings, which

13 variables are and aren't controlled by

14 distributions. How is that decided and what --

15 which variables go in which bucket?

16 MR. FRANCO: Victor Franco. This really

17 depends on the rule. But in generally, a lot of

18 them are distributions, especially if we have data

19 that we know that this should be represented as a

20 distribution.

21 So for example, and we'll go through

22 examples of course as we go through the
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1 spreadsheet. But for example, lifetime is usually

2 a distribution. There's other variables as we go

3 through. And it's very similar to rulemakings.

4 But obviously there's some rulemakings you don't

5 have a lot of data and that might not -- you don't

6 know what the distribution might be.

7 So let's go. So this is the structure

8 of the spreadsheet. Obviously you'll have this

9 later to kind of look at this. We might go back

10 just to see the flow of the structure if needed

11 when we're presenting the spreadsheet. But I want

12 to go through this a little bit for you to

13 understand and obviously once we get into the

14 spreadsheet, we'll review it again.

15 So the main kind of calculations for the

16 LCC and payback is in a worksheet that we'll spend

17 a little bit of time called LCC and payback

18 calculations worksheet, and that's where

19 essentially the LCC and payback calculations are

20 being conducted. Everything else is an input to

21 that spreadsheet. From that, once you run the

22 spreadsheet, basically you need a -- we've done a
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1 macro to generate results at the end -- you

2 produce something else called forecast cells. And

3 we'll go what that means.

4 Basically those are generated with

5 Crystal Ball in a macro. Those forecast cells are

6 the results of conducting the Monte Carlo

7 analysis, and usually for all these rulemakings we

8 set it up to 10,000. You have options to do more

9 or less. But this is what we've come up with as a

10 good value of generating the results. From those

11 forecast cells, we generate the final results. So

12 for example, we have the summary worksheet, which

13 we'll go into, some statistics and LCC outputs

14 that go to the NIA.

15 Now, as you've seen from before, we have

16 all these key parameters which are the same. So

17 for example, we have equipment price, installation

18 cost, energy use, energy price, energy price

19 trends, lifetime discount rates and then there's

20 data worksheets that are also for each of these

21 and so you can see what goes with what. We'll go

22 through each one of them carefully and the ones
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1 that are related. For example, equipment price,

2 we'll talk about markups and equipment price

3 trends.

4 An additional one is the building

5 sample, and I will take more time to discuss that

6 one as well. So obviously I didn't want you guys

7 to read it. This is mainly once you have the

8 printout. This explains each of the worksheets

9 before and a description. And this is primary for

10 you to take after the meeting and be able to look

11 at and compare and say, oh, he was talking about

12 the statistics, oh this is what this is, non13 itemized switching, this is what this is.

14 I don't want to go through the

15 descriptions obviously. Just want to point out

16 something that's very critical. We've listed here

17 there's an asterisk. Hopefully you can see it, for

18 some of these. There we go. That's right here

19 for some of these and there's a note at the

20 bottom.

21 So the results that are displayed in

22 that worksheet are only for the household that's
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1 being calculated. So it's just one household

2 that's being calculated at that time. Once you

3 run the macro, you actually run it through 10,000

4 times and you get the final results. So it's very

5 important because sometimes it's confusing what's

6 actually the final result of the 10,000, what's

7 actually just one household result.

8 There's a lot of worksheets, so this is

9 again the second. So just to present a little bit

10 of how the worksheets are set up, so this is

11 obviously when you open up the presentation, this

12 is something that you -- I mean, the spreadsheet

13 you want to look at. You want to look at the

14 final results and this also gives you a way to

15 generate new results if you wanted to. So

16 basically this is the summary worksheet. On the

17 right, there'll be results.

18 Now, it depends on the product that

19 you're dealing with, what kind of results you'll

20 be getting. For the furnaces rulemaking, we've

21 disaggregated some of the results by region and

22 market, say like new construction, and
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1 replacements to give people that are looking at it

2 more of a sense of what's going on in these

3 submarkets. That's not very usual.

4 Usually results is just presented

5 national. So this is one difference, furnaces with

6 -- but it depends. There's other rules that you

7 find something like this. Another difference is,

8 for example, you have standby results that are

9 different from the actual standard, AFUE standard.

10 So there's a lot of results represented. Another

11 thing that we usually have in these spreadsheets

12 is some kind of instruction to kind of lead you to

13 how to use the spreadsheet in case you wanted to

14 actually run it, change some of the variables.

15 And the user options are right here on

16 top, and those are the ones that we've set up so

17 that they can change. And this has been evolving.

18 It depends on the product. One, for example,

19 that's very different is we have this switching.

20 And you can actually turn off and on the switching

21 and see what the results are. Something that's

22 usually available in all these rulemakings is, for
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1 example, the energy price trends or changing the

2 start year.

3 Something else that's useful is we could

4 sometimes -- these spreadsheets are large and it

5 might take quite a bit of processing time to

6 actually generate the final results. We have this

7 option of trial numbers. So in case we wanted to

8 run this spreadsheet today, we would change this

9 to a lower number and generate results, and you

10 can change that as a user. This is also important

11 just to kind of give you a sense of what we're

12 going to be talking about today in all these input

13 worksheets, kind of the setup.

14 So we've tried to make it as useful or

15 as easy for the user to kind of navigate through

16 the spreadsheets. But we understand that these

17 are pretty difficult to understand, especially if

18 you don't know the product. But we've tried to

19 make it as user- friendly as possible. So a lot

20 of these worksheets are set up in the following

21 manner. You'll have a data throughout. For

22 example, this is the markups. And so, for example,
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1 on markups, we have markups -- sorry about that --

2 markups that are divided by region. And you can

3 see the regional numbers and that's the data.

4 Then you have some other inputs that are

5 coming from other parts of the spreadsheet. For

6 example, in this case we are a region. For

7 example, a specific household, you probably can't

8 really see it but I'll say it verbally, 21, region

9 21, which represents Texas. So this specific

10 household is in Texas. For that household, it's

11 actually replacement and so it'll use a certain

12 input from these markups. And here on this side

13 are the calculation. Calculations sometimes vary

14 between spreadsheet and spreadsheet.

15 In this, we thought that this was easier

16 to visualize in this manner. But it depends on

17 the worksheet and we'll go through that. The

18 final piece is actually the export value and this

19 goes to another worksheet or might actually go to

20 that LCC and payback calcs worksheet.

21 MS. JACOBS: This is Diane Jacobs from

22 Rheem. So just kind of to go back, so we're in
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1 the LCC and PB worksheet, right, or spreadsheet.

2 MR. FRANCO: Right now we're in the

3 specific worksheet that's generating the markups.

4 MS. JACOBS: But that's part of --

5 MR. CYMBALSKY: Inside the life cycle

6 cost worksheet.

7 MS. JACOBS: It's a life cycle cost

8 spreadsheet.

9 MR. FRANCO: That's correct.

10 MS. JACOBS: And I think you just said

11 that the results when you run that are for a

12 single household. But now, you're saying the data

13 is for all these cities and regions.

14 MR. FRANCO: No, no. So yeah, let's go

15 over it.

16 MS. JACOBS: Okay.

17 MR. FRANCO: This is Victor Franco

18 again. Let's go -- this is very important to start

19 with. And we'll go through this every worksheet.

20 We'll review it. But you open up the spreadsheet.

21 You go to this worksheet which is called markups,

22 and you'll see that this -- the results that are
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1 being calculated here are for that specific

2 household, just one household.

3 MS. JACOBS: Okay, to respond, so it's

4 like a look-up table.

5 MR. FRANCO: Yes.

6 MS. JACOBS: So depending on what your

7 input is, it's looking in there and picking one

8 number for one house.

9 MR. FRANCO: Correct. So for example,

10 just an example here and we'll go through this

11 more carefully, is this household is in Texas. So

12 it will pick up the values that are appropriate

13 for that region in Texas. So that's -- yes.

14 MR. CYMBALSKY: So we have a question

15 from Julie Hatfield, asking why we have -- using

16 2003. She feels that's outdated. I believe she's

17 referring to the CBECS 2003 survey, and so, that's

18 kind of unfortunately the last CBECS that was

19 published in its entirety. So DOE would

20 appreciate some more recent data. So Julie, if

21 you have that, please forward it on to DOE. Thank

22 you for the question.
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1 MR. FRANCO: Thank you. Victor Franco.

2 So this is -- it kind of ends the introduction to

3 the LCC spreadsheet. Let me move on to the NIA.

4 Once we finish with that introduction, then we'll

5 go into the spreadsheet. So only a few more

6 slides and we'll actually go to the spreadsheets.

7 So this is a lot of information

8 obviously. Let's just focus on some of these

9 inputs and outputs. This is kind of the analysis

10 flow chart and then we'll go into the spreadsheet

11 flow chart. So the things we want to focus on

12 first is, as before, we're saying primary input to

13 the NIA is shipments and LCC outputs. Some of

14 those are the actual outputs that are necessary --

15 for example, consumer price, installation cost,

16 energy prices, energy consumption.

17 And then, there's others because the NIA

18 is doing this very long period of time. So

19 there's trends that are also built in. And at the

20 end, from all this we get the two primary results,

21 the net present value and the energy -- national

22 energy savings.
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1 MS. JACOBS: Diane Jacobs, from Rheem.

2 So you said that trends over a long period of

3 time. Is it always 30 years you're trying to

4 forecast?

5 MR. CYMBALSKY: So this is John

6 Cymbalsky, DOE. The 30 years represents the

7 analysis period for the number of shipments that

8 are going to be analyzed. So the analysis period

9 takes 30 years of shipments and then extends it

10 until the last years' worth of shipments have been

11 retired from the stock. So depending on the

12 product, transformers for example has a 30-year

13 life. So 30 plus 30, it would be a 60-year

14 analysis, so to speak.

15 MS. JACOBS: Okay. So I think furnaces

16 was 26. So it's be 56.

17 MR. CYMBALSKY: Fifty-six, correct.

18 MS. JACOBS: Okay, thank you.

19 MR. FRANCO: So let me move on to --

20 obviously you'll have this so hopefully this is

21 useful for you to -- once you go back to

22 understand this spreadsheet. Let's move on a
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1 little bit, just where we're talking about the 30-

2 year pay period, analysis period. So we're

3 looking at, for furnaces, just specific for

4 furnaces, the shipments between 2021 and 2050.

5 After that, then we're just looking to

6 make sure everything is retired and we're

7 analyzing those retirements. Basically the energy

8 savings -- the national energy savings are the

9 primary energy savings and the NPV is the

10 difference between the present value of the

11 savings of the annual operating cost and the

12 present value of the increased annual total

13 installed cost between the base case, which is

14 without the standards, and these standard cases.

15 Now, how the spreadsheet works is

16 there's a macro that we've built in to generate

17 the final results. Again, this is using a

18 spreadsheet. So you can open it up and take a

19 look at it. It has a macro, so you don't need any

20 additional add-ins or anything. You can actually

21 just use it right away.

22 And there's one thing to understand
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1 that's critical is that the individual worksheets,

2 when you look at the intermediate results, are not

3 the final results but they represent either the

4 results for a certain region, in this case we've

5 divided it between north and the rest of the

6 country, and a market-type representation,

7 commercial. When we get to the NIA, we'll look at

8 it more carefully of how that's represented. But

9 also I'll show that in the next slide to just go

10 over it a little bit.

11 MR. CYMBALSKY: Victor? Victor?

12 MR. FRANCO: Sure.

13 MR. MILLER: So is the macro -- the

14 result you get using the macro, is that a Crystal

15 Ball result? So it has the -- it is this 10,000

16 iterations?

17 MR. FRANCO: No, sorry. Let me make

18 that very clear. No. So the LCC outputs

19 obviously is the result of the 10,000. Once you

20 get to the NIA, you already have that result. And

21 in the NIA, you use the macro to kind of go

22 through the different iterations. You calculate
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1 the results for the north. You copy and paste --

2 the macro copies and pastes that and it generates

3 the results for the rest of the country.

4 And obviously actually it goes north,

5 residential, north commercial, rest of the county

6 residential, rest of the country commercial and it

7 generates the final results. And we can actually

8 see that once we get to the spreadsheet. It'll

9 probably be easier to visualize.

10 MR. CYMBALSKY: So this is John from

11 DOE. So Victor, is it safe to say that the Crystal

12 Ball add-on is only needed for the LCC?

13 MR. FRANCO: That's correct, yes. The

14 LCC, yeah, is the only one that uses the Crystal

15 Ball add- in. So this again gives you a sense of

16 how the spreadsheet works. And the individual

17 worksheets. So let's start from the outputs.

18 So essentially when you open up the

19 spreadsheet, what you want to see is the results

20 and this is available in the NIA summary. Those

21 results are being generated from a couple

22 worksheets that are kind of the accounting
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1 spreadsheets and they have different calculations

2 that are going on. I'll go through those.

3 Basically for this product, we're using

4 -- we have two product classes. So they're

5 calculated differently. We have non-weatherized

6 gas furnaces and mobile home gas furnaces. Now

7 we're going to be mostly talking about non8 weatherized gas furnaces. But in case you have

9 questions, let me know.

10 And those specific worksheets actually

11 calculate three things kind of at the same time.

12 They're accounting for three things. And I just

13 wanted to empathize that. Both they take in the

14 economic and energy use inputs and they process

15 those. And they also take historical shipments

16 and those to actually calculate the shipments.

17 And for non-weathered gas furnaces, they actually

18 calculate the impacts of product switching.

19 So obviously we'll spend a lot of time

20 on that, on those worksheets because everything is

21 going on there all at once. And there's some

22 other inputs. And so, we'll spend a little bit --
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1 probably less time on the inputs, data worksheets

2 as they're called. But those are important as

3 well. They have the data, some of the data that's

4 being used. Once again, worksheet description.

5 This is for you to have and to take a look at to

6 kind of compare what each worksheet does.

7 So again, this is the summary worksheet.

8 It's very similar to the LCC, how it's set up.

9 You have a results area. You have instructions

10 here at the bottom. And there are two things

11 going on here. First, there's some analysis

12 parameters just to see what those are. And these

13 are -- you can't really change these in the

14 spreadsheet but at least it will show you what

15 they are.

16 So for example, for this rulemaking,

17 it's the standard takes in effect 2021. The

18 analysis period is 30 years, so the end of the

19 analysis period is 2050. There are two discount

20 rates that are being used, 3 percent and 7 percent

21 and the present value is 2014. Now, there's

22 another part where you can actually change some of
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1 the assumptions and this is in this which is

2 labeled scenarios.

3 So for example, similar to LCC, you can

4 change the energy price economic variables, which

5 we call economic growth. And this is -- we use

6 some data from AEO and you can change either

7 reference in two scenarios, the trend and the fuel

8 switch. Those are the primary. So fuel switching

9 again is very specific to furnaces. It's not in

10 all products.

11 I just wanted to point out that in here

12 then you have this button which actually generates

13 a final result. So in case you wanted to change

14 some of this and you wanted to generate the

15 results you click here and it will change the

16 results at the end, a couple minutes usually it'll

17 take to run through it. So they will spend a lot

18 of time on this worksheet, which is the non19 weatherized gas furnace worksheet and it has a lot

20 of different spaces. It's calculating shipments.

21 It's calculating a lot of these inputs and

22 processing and also for non-weatherized gas
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1 furnaces, doing the fuel switching.

2 So obviously, this doesn't have the

3 whole thing. But just to give you a little bit of

4 background. So basically this part over here is

5 getting data from other worksheets and has -- so

6 the data input. And this side, on the right side,

7 we're actually calculating, doing all those

8 calculations, doing the base case calculations,

9 the standard case calculation and the product

10 switching. And we'll spend a lot of time on that.

11 The thing that I wanted to just remind

12 you again is that when you're looking at this

13 spreadsheet, which might be confusing when you

14 open it up, is that the results that you're

15 looking at there are for a specific region and

16 it's very, very small here. And we should look at

17 it more closely when we open up the spreadsheet.

18 This one is actually for the northern region and a

19 specific sector. For example, in this case,

20 residential. If you wanted to see the results,

21 then you would have to change the inputs to see

22 just the south, maybe you want to just look at
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1 commercial or something like that.

2 So that's important. When you generate

3 the final results, obviously it goes through all

4 these and it generates the final results.

5 MS. JACOBS: This is Diane Jacobs from

6 Rheem. So in the first spreadsheet, you're doing

7 the 10,000 households and all that. And so,

8 you're just segregating the data that you've input

9 into this second spreadsheet.

10 MR. FRANCO: Correct, yes.

11 MS. JACOBS: Okay, so it's input. Okay.

12 MR. FRANCO: Correct, correct. Yes, and

13 we'll go through that a little bit more carefully,

14 how that's set up.

15 MR. CYMBALSKY: This is John from DOE.

16 Just to clarify the difference in the spreadsheets

17 and maybe frame it up a little differently. The

18 life cycle cost spreadsheet is what would this

19 standard do to an individual customer, right? So

20 that's the sample of one. Then we know that

21 that's not representative. So we throw it through

22 the Crystal Ball. It does the 10,000 Monte Carlo
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1 simulation. So now we have all of that.

2 So then in the second spreadsheet, what

3 we're trying to do is say, okay, what does it mean

4 to the whole nation. And so, I think if you think

5 of it that way, maybe it'll help logically

6 delineate where certain things are happening, so

7 if that's helpful.

8 MS. JACOBS: Yeah, I think sometimes,

9 like what's the input, what's the output, you

10 know, can be confusing about -- yeah, just those

11 two things because you can see things. And I know

12 I've been in the NIA and trying to change the

13 input. But it really came out of this other

14 spreadsheet. So to someone who's used to working

15 with it, that must be completely illogical. But

16 it's not always clear. But from the labels,

17 you're kind of guessing what the things are. So

18 okay, thanks.

19 MR. FRANCO: Victor Franco. So again,

20 as we go through these spreadsheets, please, yeah,

21 stop me if you have additional questions in that

22 area. We'll try to make it as clear as possible,
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1 the inputs, how the inputs kind of work so you can

2 kind of get a sense if you wanted to kind of

3 visualize how that maybe would change or maybe

4 what happens. But we'll go through it individual.

5 MS. JACOBS: So just -- this is Diane

6 Jacobs from Rheem. So just in my experience, you

7 know, we're going through the technical support

8 data document and maybe we disagree with some of

9 the assumptions. But I don't want to complain

10 about every single thing. So I was trying to use

11 the spreadsheets to see if it would make any

12 difference. Was it worth bringing up, you know?

13 So it seemed like it would be easy to do but it

14 didn't end up being that easy.

15 But so I guess that's the way I would

16 use it. I don't necessarily only want to see how

17 you did it or the results of yours but to be able

18 to change some of the assumptions and understand

19 whether it's worth making an argument or not. You

20 know, there are a lot of assumptions. Some of

21 them, you know, I may or may not have ever thought

22 about. And it's not really clear which battle go
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1 to after, you know? So okay.

2 MR. FRANCO: Great, thank you. Victor

3 Franco. So yeah, we'll try to make that clear as

4 we go through.

5 MS. JACOBS: Thank you.

6 MR. FRANCO: Yes?

7 MR. MASSARO: Victor, this is Tom from

8 New Jersey Natural.

9 MR. FRANCO: Sure.

10 MR. MASSARO: Kind of along those same

11 lines, it could still be helpful to see -- go back

12 to like an incremental cost of the different

13 appliances versus what we see with our contractor

14 base versus what's in the model. If changing that

15 does have like a demonstrable difference, on the

16 output, is that something that we would get into

17 today that we'd be able to get in there and say,

18 well, if you change the installed cost of this

19 specific appliance to x, this is what the output

20 ends up looking like?

21 Is that -- is that something we would be

22 able to get into today?
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1 MR. FRANCO: Victor Franco. So we'll

2 obviously open up that worksheet once we get into

3 that worksheet, we can take a look and we'll see

4 if, yeah, if that's appropriate.

5 MR. CYMBALSKY: Yeah, well the purpose

6 here is to show you how to do that, right, and

7 we'll point you to that spot. We are not going to

8 sit here and run simulations for everybody, just

9 to be clear. Okay. We're teaching you how to

10 fish, not frying it for you. It is a Friday fish

11 day for some of us I guess, so.

12 MR. FRANCO: So this is the very last

13 slide and this is just -- this is to show you what

14 the topics I'm going to be going through. So LCC

15 spreadsheet, there's a lot more to talk about.

16 And we'll be first talking about the summary

17 worksheet, LCC calculation engine, the building

18 sample, the total installed cost, so customer

19 prices -- consumer prices, installation cost

20 model. Then we'll go into operating costs and

21 maintenance repair costs, energy use, energy

22 prices and trends. Then we'll go into other
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1 components such base case efficiency distribution

2 discount rates, lifetime and product switching.

3 So product switching again is very specific.

4 Almost everything else is very general and all

5 products will have that.

6 In terms of the NIA spreadsheet,

7 basically we'll be calculating -- we'll be looking

8 at -- focused on summary worksheet, which we kind

9 of already talked about and then the calculation

10 engine, which will have three main components:

11 inputs, the shipments and the product switching.

12 So I'll begin to talk about the LCC

13 spreadsheet. And as we go through, please stop me

14 if you have any questions. So we'll go through

15 here and let me close -- oh, I'll keep it open in

16 case we want to see something. One more thing,

17 obviously the spreadsheet view, in case you can't

18 see something I'm talking about, please let me

19 know so I can enlarge it.

20 When you open up the spreadsheet, one of

21 the first things obviously is you want to look at

22 the summary, what are the results. And this is
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1 the sheet that we've been talking about, the

2 summary worksheet. So again, once again, we're

3 looking at the user inputs which are these.

4 There's a button right here which if you press it

5 and you have Crystal Ball, everything's set up

6 properly. You can actually run the spreadsheet

7 and generate new results. And then there's some

8 instructions how to do that.

9 And then, here on the side are the

10 results that we usually have when we're generating

11 the final results. They're usually like install

12 price, lifetime operating cost, first-year

13 operating cost, LCC, LCC savings and net impact,

14 no impact. These fractions over here --

15 MS. JACOBS: Victor?

16 MR. FRANCO: And paybacks.

17 MS. JACOBS: So it says average life

18 cycle. Do you do like the 10,000 samples and

19 average or is it some kind of weighted average or

20 --

21 MR. FRANCO: Yes, this is what we're

22 going to be talking about, the specifics. Victor
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1 Franco.

2 MS. JACOBS: Okay.

3 MR. FRANCO: So we'll be talking about

4 that, where these numbers are being pulled out and

5 all that and that's very important.

6 MR. ADROIT: Victor, just a question.

7 Henri Adroit with Goodman. So you can only run

8 the spreadsheet, this particular one if you have

9 the Crystal Ball, right? Otherwise it gives you

10 an error?

11 MR. FRANCO: That's correct, yes.

12 MR. ADROIT: Okay.

13 UNIDENTIFIED FEMALE: Is it expensive?

14 MR. CYMBALSKY: How much was your flight

15 here? We'll do a quick payback analysis on your

16 flight --

17 UNIDENTIFIED FEMALE: It cost $400

18 actually to stay today.

19 MR. CYMBALSKY: You are getting a great

20 deal. You don't know how much he bills that.

21 MR. FRANCO: So again, these are the

22 results. Just very briefly, these are the
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1 national results and we try to label it so it's

2 easy to navigate. Then these are the results just

3 for the northern region. Down here, we have the

4 results for the south. Then we have some stand-by

5 results for this product. Now, on the side -- and

6 this is -- this is very, again, very specific to

7 furnaces. We have provided disaggregated results.

8 So for example, we have the results just

9 for replacements, for residential market and then

10 below that, the north replacement residential,

11 south replacement residential and then so on. New

12 construction, and then even commercial. So --

13 MR. CYMBALSKY: So this is the results

14 summary, right? So this is not where you would

15 start changing those costs, just to be clear.

16 We'll show you where to do that. This is where

17 you would actually run the scenario and see the

18 results.

19 MR. FRANCO: Right.

20 MR. CYMBALSKY: Diane?

21 MS. JACOBS: So Diane Jacobs from Rheem.

22 Again, is it the average or is it a weighted
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1 average based on population? It's just flat

2 average?

3 MR. FRANCO: We'll go -- we'll go into

4 that detail right --

5 MS. JACOBS: Could you give me a hint?

6 MR. FRANCO: It's the average of the

7 10,000 -- the 10,000 runs. So for example, you

8 calculate one household and then that gets saved

9 in the process and then you can calculate the next

10 household, the next household and we'll go into

11 what the weighting --

12 MS. JACOBS: So is the sample somehow

13 related to population?

14 MR. FRANCO: Of course, yes, and that's

15 very important.

16 MS. JACOBS: Okay.

17 MR. FRANCO: And we'll get that when

18 we're talking about the building sample.

19 MS. JACOBS: Okay.

20 MS. ARMSTRONG: Okay, so can I answer?

21 My translation is it is a simple average but the

22 weighting comes in because your distribution that
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1 you pick up in that 10,000 is dispersed depending

2 on the weighting of the actual residences that

3 you're trying to pick up. So the results are a

4 weighted average even though it's a simple average

5 of the 10,000 runs. Does that make sense?

6 MR. FRANCO: Thank you for --

7 MR. STANONIK: Frank Stanonik.

8 Actually, let me try and flip -- let me try and

9 flip that around to make sure I just understood

10 that. So for the 10,000 runs on the national

11 average, you'd have a certain distribution of I'll

12 say simulations, data sets, right? For the 10,000

13 runs of either the north or south, it would be a

14 different group of -- yes, because now -- because

15 now, this 10,000 runs is 10,000 runs in a north

16 installation. So it does -- okay.

17 MR. FRANCO: Thank you for that. So

18 basically, let's go -- let's go to -- let me see.

19 One thing to point out, obviously something that's

20 a little bit different from other products is we

21 have commercial and we have a residential market.

22 And for this product we have a little bit of a
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1 commercial market and that's why we have those

2 results.

3 MR. CYMBALSKY: But they are residential

4 -- they meet the statutory definition of a

5 residential furnace, so just to be clear.

6 MR. FRANCO: Another part of the

7 spreadsheet that's kind of like the general

8 results are the statistics and the LCC outputs.

9 So I'll go through those two worksheets. But

10 first of all, if I click on one of these, and

11 hopefully you can kind of see that for example I'm

12 clicking on K7. You'll notice that that's linked

13 to a worksheet that's called forecast cells and

14 that's a specific cell.

15 Let's go to the forecast cells. So the

16 forecast cells are kind of the output that comes

17 out of the spreadsheet. And for this case, let's

18 go to one that is interesting --

19 MS. JACOBS: (Off mic.)

20 MR. FRANCO: Of the spreadsheet, of the

21 runs. So once you decided that you want to do a

22 run, you click run. The spreadsheet runs and then
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1 goes through and generates these statistics. The

2 statistics, which is really hard here because it's

3 not really formatted nicely, this is kind of like

4 the raw output -- is the number of trials that

5 this represents, the results they are being, and

6 the mean, median, min/maximum, 5 percent out or 25

7 percent out, so on.

8 And these actually -- all these are

9 nicely formatted in the statistics worksheet,

10 which we'll go to.

11 MS. JACOBS: So what does it mean if

12 everything is a one?

13 MR. CYMBALSKY: Can you use --

14 MS. JACOBS: Sorry, Diane Jacobs.

15 Everything is a one. Is that just because you

16 haven't run it?

17 MR. FRANCO: No. It depends on what

18 statistic we're trying to look at. But let's

19 focus --

20 MS. JACOBS: Oh, it's a flag.

21 MR. FRANCO: yeah, sometimes it's just a

22 flag, a one and a zero and sometimes it's just one
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1 household. So it depends on what you're looking

2 at.

3 MS. JACOBS: Oh, okay.

4 MR. FRANCO: So for example, you're

5 looking at specific discount rate and then there's

6 an actual value that you're getting. And so, for

7 example, here you're getting 4.7 is the average of

8 all the discount rates that you're doing but you

9 also have different statistics. So some households

10 have really small discount rates, some have -- and

11 so like you can kind of visualize a distribution

12 of that and we'll go into it.

13 So everything that's in that summary

14 worksheet, everything that's going to be in the

15 statistics and everything that goes into the LCC

16 outputs comes from here. So it's very important.

17 So that's from the 10,000 run. So let's go back

18 to the statistics so that'll be a nicer format.

19 So for example, if you wanted to look at

20 the LCC savings, what would be the maximum, what

21 percentiles, how the distribution, you would go

22 here. You could also look at the annual fuel use
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1 and the maximum, minimum and percentiles. And you

2 can kind of see the distribution. This helps you

3 out to visualize a little bit of what's going on

4 in the spreadsheet. And we've provided what we

5 think is the primary inputs and information that

6 would be useful.

7 We even go to kind of some economic

8 variables, installation location and some energy

9 variables that are important like operating hours,

10 calculated fuel use and some inputs. And so, you

11 can kind of play around and see what everything.

12 But this is again coming from that 10,000 run, all

13 the households that are looked at.

14 MR. MASSARO: Victor, quick question on

15 that. You were mentioning that this is where you

16 can -- is this -- oh, Tom Massaro, New Jersey

17 Natural Gas. Is this where you would go in and

18 change that?

19 MR. FRANCO: No.

20 MR. MASSARO: No, I didn't think so

21 because I still saw the equal sign. All right.

22 MR. FRANCO: Everything we're looking at
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1 is the final result, still coming up and then

2 we'll go into where you can actually change it.

3 MR. SCHROEDER: Is there a way you can

4 get - - oh, sorry. Dave Schroeder. Is there a

5 way you can get the complete output? So if you

6 wanted to see like the complete distribution of a

7 particular cost or a particular energy

8 consumption, is there a way you can get that

9 complete distribution for the entire 10,000?

10 MR. FRANCO: Yes, yes, there is. It's

11 not built into the macro, as you -- I'll probably

12 describe it in just a couple steps.

13 MR. SCHROEDER: Okay, that's fine.

14 MR. FRANCO: But that's a good question.

15 I wasn't sure if we wanted to go into that. But

16 I'll actually go into that detail to show you more

17 or less how you would do that using Crystal Ball.

18 So if you don't have Crystal Ball, obviously you

19 wouldn't be able to do it. So just briefly --

20 MR. M. HUNT: Excuse me, this is

21 Marshall Hunt with PG&E. The Crystal Ball I found

22 on the Web was $1,000. Such a deal.
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1 MR. CYMBALSKY: Less than a life cycle

2 cost of a furnace in the north.

3 UNIDENTIFIED MALE: You can buy a magic

4 eight ball for $8.99.

5 MS. JACOBS: It'll only cost $50,000 to

6 learn how to use it.

7 MR. FRANCO: So just one additional

8 worksheet that I wanted to show you and it's in

9 this realm of final results that is important and

10 we need to talk about it because this is a major

11 input to the NIA is the LCC outputs. This will

12 talk about more detail. This goes into the NIA

13 spreadsheet. And again, this is the results of

14 the 10,000 running the spreadsheet 10,000 times.

15 So now, we'll be more focused on looking

16 at the worksheets and the individual household

17 calculations when we're going to households.

18 MR. CYMBALSKY: So I think now we'll

19 turn to the inputs and I think let's just start

20 with the cost of furnaces and go from there. Does

21 that sound -- it sounds like I'm picking up a vibe

22 that that's where people want to start.
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1 MR. FRANCO: There's just one worksheet

2 just before that and then we'll go right into it.

3 MR. CYMBALSKY: Okay, great.

4 MR. MILLER: Victor, just quickly, just

5 go back one. The percentiles --

6 MR. FRANCO: Yes.

7 MR. MILLER: Okay, so what are you

8 telling - - what is that telling us? So the 5

9 percentile of all population would have a negative

10 life cycle? I'm trying to figure out. It can't

11 be cumulative, right? Explain to me what I'm

12 seeing there.

13 MR. FRANCO: Yes, yes, and that's very

14 important. And you kind of think about it. I

15 don't know if you have kids sometimes, they take

16 the standardized tests and they're saying, oh,

17 your kid is in the 95th percentile, or if he might

18 not be a good student, he's in the 5th percentile

19 or something like that.

20 So in that kind of -- if you're thinking

21 about that, if your child was in the 5th

22 percentile, he would be exactly at this 750.7
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1 negative dollars. So obviously there's -- below 5

2 percent there's people that are actually worse

3 than that and that gets into this minimum. This

4 would be the minimum. Then you have some people

5 that just don't have anything and then you go into

6 the other range and 95th percentile then goes into

7 the other direction. And so, that at exactly,

8 95th, 1,900, but then there's people that actually

9 save more and then you get to this 25,000.

10 MS. JACOBS: And then Diane Jacobs from

11 Rheem, and because it's life cycle cost savings, a

12 negative means it's costing 5 percent --

13 percentile more, though they're paying more.

14 MR. FRANCO: Correct.

15 MR. CYMBALSKY: So 5 percent is just the

16 -- that's the percentage of the

17 MS. JACOBS: No, that small segment of

18 the population --

19 MR. CYMBALSKY: Yeah.

20 MS. JACOBS: Has to pay more.

21 MR. CYMBALSKY: Right, 11,000 or more.

22 So the first 5 percent losers let's call them,
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1 they save -- they lose as much as 11,000 I thought

2 I saw, yeah, 11,732. So that's the guy who lost

3 the most.

4 MS. ARMSTRONG: That's the min.

5 MS. JACOBS: Oh, wow.

6 MR. CYMBALSKY: So he never used -- he

7 bought a furnace and never used it.

8 MS. JACOBS: Oh, okay.

9 MR. CYMBALSKY: That's actually --

10 that's what that means.

11 MS. JACOBS: There are those people.

12 Okay, thank you.

13 MR. FRANCO: And obviously what we're --

14 what we're looking at the summary, we're looking

15 at the average, which is this value here and this

16 matches the summary. But if you want to look at

17 it in more detail, this is why we provide this so

18 you can kind of like get a sense of the

19 distribution.

20 So let me briefly go to the main

21 calculation spreadsheet. The main calculation

22 spreadsheet is where all the calculations are
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1 going to end up. All the inputs from anything

2 that's being done in the spreadsheet comes here.

3 And so, for example, we'll talk about retail

4 prices. So for example, the cell R6, we'll go

5 into the retail price worksheet and we'll look at

6 how those are inputted in there. But this is the

7 output from there that goes in here. Installation

8 cost, that's where this is being calculated.

9 UNIDENTIFIED MALE: The calculations.

10 MR. FRANCO: The calculations, yes. At

11 the far right of all this are the actual

12 calculations of the annualized calculations that

13 are going on. So first, you would calculate the

14 total installed cost. So that would be your first

15 cost and then every year you're calculating

16 basically your operating costs. But that might be

17 some maintenance, other costs that are associated.

18 So you can actually see here year by year what's

19 going into that household. And it varies.

20 Obviously the energy price varies. And that's why

21 this is varying and then you have maintenance

22 costs that are being generated there.
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1 Everything else are inputs to that. The

2 final thing to look at here are the results. So

3 when you look at and you're comparing for example,

4 well, what's going on here at LCC savings or even

5 LCC. So for this individual household, you're

6 just looking at that LCC savings for that

7 household. And depending on the economics of the

8 household, that's the result that you're getting,

9 just one household.

10 The final thing to -- what I wanted to

11 talk about this spreadsheet, this worksheet is

12 there's two things to note related to Crystal

13 Ball. When you see a green cell like this,

14 that'll mean that it's a distribution. And we'll

15 open it up and see what that means. When you see

16 a blue, that means that it's a forecast cell. So

17 let's cite that into the idea of forecast cell

18 worksheet. So what happens is if we set this up

19 as to be a forecast cell, then every time that a

20 household is being calculated, this value is being

21 recorded. And then as it's processed, the final

22 result is an average. Going back to the question
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1 -- what was your --

2 MR. SCHROEDER: Dave Schroeder.

3 MR. FRANCO: Dave Schroeder had, we

4 would -- once we run the spreadsheet, so this is

5 again -- this is more of a detail obviously. If

6 you have Crystal Ball and you run the spreadsheet,

7 what you're able to do is extract all that data.

8 So what you want to do is you run the spreadsheet.

9 Then you go to the Crystal Ball. If you have

10 Crystal Ball, you actually have this tab called

11 Crystal Ball. And what you can do is once you run

12 it, you actually -- there' a button that --

13 because we haven't run the spreadsheet.

14 It's not live. But once you run it,

15 it's active, which is called and hopefully you can

16 see it, which is called extra extract data. Once

17 you click on that, actually gives you what you

18 want to extract. So you might want to just

19 extract one variable or, sorry, one forecast cell.

20 And that'll be it and that'll be the whole 10,000

21 and actually you can do statistics also. You can

22 actually just the raw data or the statistics or
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1 you can do all the variables. Now, there's so many

2 variables that depending on sometimes it doesn't

3 fit in one worksheet. So sometimes you want to

4 minimize and it also takes more time to extract

5 obviously. So that's that and hopefully that

6 clarifies that and that's more of a detail.

7 Let me just go in here. For example,

8 here we have a green. If you have Crystal Ball,

9 you can see what the distribution of this is in

10 Crystal Ball. So here, you wouldn't be able to

11 tell what's going on there. It's just a green

12 cell. There's no formula. There's nothing. So

13 you would actually -- I click on here which says

14 define assumption. And you click on it and it'll

15 actually give you what that assumption is. All

16 right, it might take a while because it's looking

17 at it. Hopefully it'll open up.

18 MR. CYMBALSKY: So while this does it's

19 thing, Richard Murphy had a question. Are there a

20 different set of assumptions, variables for

21 replacement and new and the answer is yes. I

22 think Victor has been detailing that breakout in
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1 the spreadsheets and we'll see more of that as we

2 go through. Another question from David Nestor.

3 What size house is being used as the input and

4 what is the average age of the house. I think

5 this comes from the RECS database.

6 So each one of those houses has

7 different characteristics. Age is one of them.

8 Size is another one. And that goes to sampling

9 and then goes into the Crystal Ball and gets a

10 distribution, et cetera.

11 MR. FRANCO: And Victor Franco again.

12 We'll go into that once we get into building

13 sample. So this is how the distribution actually

14 looks for this particular variable. And this is

15 the probability that that will be the

16 distribution. Obviously this will get more of a

17 probability that this will be selected versus

18 these other two.

19 So this is kind of the weighting, the

20 probability of that. And actually this is another

21 detail. In Crystal Ball you can actually see

22 statistics and percentiles and other things that
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1 are in terms of distribution. Once we get to

2 distribution, it's a little bit more interesting.

3 We'll actually open that up and we'll see how that

4 functions. So hopefully that clears up the flow.

5 So this is what we can call the

6 calculation engine and we might need to go back to

7 this. So now let's go to the actual input

8 worksheets. And let's start as we were saying

9 we're going to be starting with the equipment

10 prices, the one that calculates the consumer

11 prices. Okay. So basically the consumer prices

12 are the manufactured costs times markups which

13 will also include sales taxes. Now obviously

14 there's a lot of other inputs that go into it.

15 For this, it depends also for example on

16 the furnace fan type and if it's for example in

17 California that it's being shipped, then we

18 consider that it's a fraction of time it'll be a

19 low NOX unit. So there's very specific variables.

20 And so, there's where you get into the details of

21 what product you're dealing with. But in general,

22 the methodology is you have a manufacturer cost
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1 and you multiply that times markup and sales

2 taxes. Also, you have a shipment cost as well

3 associated with that.

4 So let's briefly go over the worksheet.

5 Again, almost all these worksheets will have the

6 same setup. We have input parameters. We have

7 data down here. So this is the engineering data

8 that gives us the manufactured production cost and

9 then we actually go into the calculations.

10 Another thing that I just want to -- I

11 forgot to mention is the first part of the

12 presentation, I want to focus just on the general

13 results that are without the product switching.

14 As we discuss the product switching, then we will

15 have to go back to some of these worksheets, which

16 there are certain components that are related to

17 product switching and you might have questions on.

18 So we'll go into those. But just to have the

19 flow.

20 MR. MILLER: Victor, could you just

21 slide back to the -- my left, yeah. Learning

22 rate, is that what that says?
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1 MR. FRANCO: Yes.

2 MR. MILLER: What is the learning rate

3 factor?

4 MR. FRANCO: So this is the equipment

5 price but we'll talk about that and it's

6 essentially over time equipment prices change, and

7 so this is supposed to take that into account.

8 MS. JACOBS: Diane Jacobs from Rheem.

9 So you have references to X13 and ECM. Is that

10 for the standby? It was going to the side?

11 MR. FRANCO: These would be, yeah.

12 MS. JACOBS: Okay.

13 MR. FRANCO: These would be down here.

14 This is for the actual furnace motor that's being

15 used for the furnaces.

16 MS. JACOBS: Yeah, but that's part of a

17 furnace fan, so --

18 MR. FRANCO: Yes, that's correct. But

19 we have to -- since there is a furnace fan rule,

20 that's going to be before 2021, then we take that

21 into account. So for example, if we were just

22 analyzing today, we would have certain fans for
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1 furnaces which have PSC motors and some fraction

2 that have ECM or X13. By 2021, the assumption is

3 that everything will be X13 and ECM because of the

4 furnace fan standard. So that's why we were

5 considering.

6 MS. JACOBS: But for furnace -- the

7 furnace rule, you're talking about AFUE, aren't

8 you?

9 MR. FRANCO: That's correct, yes. But

10 those --

11 MS. JACOBS: So that's only gas

12 consumption.

13 MR. FRANCO: That's correct.

14 MS. JACOBS: So why are you --

15 MR. CYMBALSKY: Well, what do you do

16 when you buy a furnace, brand new?

17 MS. JACOBS: What do I do? I don't buy

18 them. I get them. (Laughter.)

19 MR. CYMBALSKY: You know, and in the

20 government, we don't get free money because we

21 print it.

22 MS. JACOBS: So --
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1 MR. CYMBALSKY: Sign me up. I'm in.

2 It's the cost of a furnace, right?

3 MS. JACOBS: Oh, the cost is related to

4 the fan. Okay.

5 MR. FRANCO: Yes, yes.

6 MS. JACOBS: The first-time cost.

7 MR. CYMBALSKY: Right.

8 MS. JACOBS: Okay.

9 MR. CYMBALSKY: So you can buy up on the

10 fan.

11 MS. JACOBS: Okay, okay.

12 MR. CYMBALSKY: Yeah.

13 MS. JACOBS: Thank you.

14 MR. FRANCO: So, correct. Yes, of

15 course.

16 MR. ADROIT: Henri Adroit, Goodman.

17 More of a suggestion than a question. Would it be

18 possible I guess when you release this again in

19 the NOPR stage to highlight input cells that

20 stakeholders have an option to play around with,

21 assuming that they have Crystal Ball, but just

22 similar to how you did the green and blue shading,
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1 just to be able to highlight the input cells as

2 well that we would have control over?

3 MR. FRANCO: That's really good --

4 Victor Franco. That's a good suggestion.

5 Definitely we'll take that into account. Thank

6 you.

7 MR. CYMBALSKY: Yeah, normally we lock

8 the cells that you shouldn't change. That's how

9 we do it.

10 MR. FRANCO: So that's sometimes a

11 little bit difficult. You want to know everything

12 that goes into that assumption and then to be able

13 to change the variables and usually the

14 documentation will have that kind of detail to

15 kind of walk you through what kind of the

16 assumptions and then how to generate those

17 assumptions. But that's a really good suggestion.

18 Thank you for that.

19 MR. LESLIE: Neil Leslie -- excuse me.

20 Neil Leslie, GTI. You said that you lock cells

21 that you don't want people to change.

22 MR. CYMBALSKY: Right.
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1 MR. LESLIE: Could you elaborate just a

2 little bit on --

3 MR. CYMBALSKY: So for example, if

4 there's a calculation that takes the number of

5 days of the year into it, we won't let you make it

6 anything but 365. So, an example. You can change

7 that number. You will break the model. Just so

8 that you know my background, for 20 years I wrote

9 the EIA NEMS residential model and it's the same

10 thing there. You protect certain input variables

11 from outside users who purchase and use the model

12 from breaking it. The only way we can do that is

13 by locking cells.

14 So can they be unlocked? They can, but

15 it's not of any value to do so. You'll just have

16 users break the model and then have more questions

17 that shouldn't arise.

18 MR. MILLER: John, Bud Miller. It's on.

19 Can we get a list of the locked cells? In other

20 words, what it is that's being locked? You gave

21 an example and that's nice. But can we get a list

22 of those?
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1 MR. FRANCO: Victor Franco. So in this

2 spreadsheet, actually there isn't any specific

3 that's being locked. But what happens is that you

4 have these if statements and there is some logic

5 in that that if you kind of break it -- like John

6 is suggesting, like if you don't have a maximum of

7 365, then it kind of doesn't make -- the result

8 doesn't make sense. And that's a good suggestion

9 to actually put that as a list of what those

10 variables are and what those -- yeah. Thank you.

11 MR. CYMBALSKY: My experience, you know,

12 from my previous employment is that I'd say 99

13 percent of the users who wanted to use a model

14 broke it, not intentionally but unless you protect

15 from going out of the bounds of what the model can

16 and can't do, you have to protect it somehow. And

17 I'm sure Excel and other spreadsheets and programs

18 have that as well. Please come up to the mic. I

19 think you'll have to switch the button on.

20 MR. SORINO: Don Sorino, National

21 Association of Home Builders. The cells that are

22 locked, you know, I had heard the question can we
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1 know which ones they are. Could we also know what

2 their parameters are?

3 MR. FRANCO: Yeah, so just to emphasize,

4 in this current spreadsheet, there is actually no

5 cells that are locked and you can't like change.

6 But to emphasize, if you do some -- make some

7 changes and maybe you don't understand what things

8 are doing, you will break the model. It will

9 produce results that might not make sense, but --

10 MR. SORINO: Understood.

11 MR. FRANCO: Yeah.

12 MR. SORINO: But if there are locked

13 cells in any of the sheets, understood we

14 shouldn't unlock them. You had them locked for a

15 reason.

16 MR. FRANCO: Correct.

17 MR. SORINO: But to know what those

18 cells are and to know what they're locked at would

19 be helpful information.

20 MR. FRANCO: Great. Thank you so much.

21 I appreciate that.

22 MR. SORINO: Thank you.
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1 MR. CYMBALSKY: It's in the

2 documentation.

3 MS. ARMSTRONG: Yeah, so I mean,

4 typically - - this is Ashley from DOE. So while

5 Victor's saying that there's no specific locked

6 cells in the LCC spreadsheet, the furnace one that

7 he's using for an example, there are sometimes

8 locked cells in other spreadsheets. And I know

9 for example in one of them, questions have some up

10 recently.

11 There is documentation in there about

12 what cells are locked and why. And specifically,

13 it says like this cell is locked. It's an

14 implementation equation and it shows the equation

15 written out on a worksheet in the spreadsheet

16 itself. So I would encourage you to look at it

17 first and see if you can - - because you can see

18 the equation being implemented but it's just

19 locked. It can't be changed.

20 But you can actually see the equation.

21 And then on a separate tab, it has the equation

22 written out as well as in the TSD. So you can
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1 follow it all. It's just that if you change that

2 equation, it's then -- it's not an implementation

3 of our test procedure. It's not an implementation

4 of an accurate model. It's not the DOE model and

5 it will not give you any type of meaningful

6 result. So it's not helpful. Okay?

7 MR. FRANCO: Thank you for that. So

8 just to highlight some of the -- and if as a user,

9 this is part of the complexity -- some of these

10 have a lot of if statements and a lot of uses of

11 Excel that are maybe not usual. They're a little

12 bit more complicated, so using, for example, match

13 function or index to generate these. So if you

14 are an advanced user of Excel, you will more or

15 less be able to navigate these. But we understand

16 that these are hard to do.

17 So just to highlight the questions about

18 where you would so some of the changes, so for

19 example, let's say for example you wanted to

20 change the manufacturer production cost. So for

21 example, this column right here is picking up the

22 appropriate manufacturer production costs that are
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1 related for that household. For furnaces, it

2 actually picks up other components as we talked

3 about. It picks up what the furnace fan that

4 household will have and, for example, if it's in a

5 region, California, it will have a low NOX add.

6 So that's part of that specific cell. So if you

7 wanted to change that, then you could be able to

8 change it there to change specific.

9 MR. CYMBALSKY: But to be clear, if you

10 don't care about all that other stuff, let's just

11 say, Neil, for example doesn't believe it's 381,

12 it's 500, we can just change it right here and it

13 will flow through.

14 MR. FRANCO: Yeah.

15 MR. CYMBALSKY: So this is an

16 appropriate spot to overwrite the number. Now it's

17 going to ignore all that other stuff. So you want

18 to simplify this? You could just say, I think it's

19 500, I think that one's 600, et cetera, et cetera.

20 MR. FRANCO: Exactly. And so, this is

21 the basic -- the first place that you could do

22 that because that's the manufacturer cost. Then
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1 you actually multiply it times markups. So

2 there's an additional place that you might be able

3 to do that. Now, going backwards, if you didn't

4 want to go that far but you wanted to change kind

5 of like the primary manufacturing cost, the inputs

6 are here.

7 Say, for example, these are fixed

8 values. This is data coming from the engineering

9 analysis. You say, well, what if increase this

10 incremental 10 percent. So here you can actually

11 go, oh, I'll just increase this by 10 percent, 10

12 percent or 15 percent. And then you can actually

13 change it there. These are fixed values. You

14 generate your results based on that and you have

15 to have an understanding of what you're trying to

16 do. Obviously this is manufacturing cost at 10

17 percent there, means something else in the final

18 consumer price. It's not going to be 10 percent.

19 Now, in the final result that comes from

20 this is the retail price and that's related to the

21 markups, which will go to a different worksheet.

22 The retail price is another place that you could
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1 actually have some type of a percentage. So say

2 you wanted to have a fixed 10 percent increase for

3 one of these levels. You thought this is 10

4 percent more. Then here you can actually just 10

5 percent, put in that cell.

6 MR. CYMBALSKY: And so, to get to Frank

7 Stanonik's question about the learning, so you can

8 see it comes in here. If you go back to the

9 learning where it said 0.94, if you just changed

10 it to one, you would zero out the learning. I

11 know that's another thing that people want to

12 know. So the 0.94 I think represents there's a 6

13 percent decline in the price of furnaces.

14 MR. FRANCO: Correct, and we'll go into

15 that in a little bit more detail because we're

16 going to two worksheets really quickly that are

17 related to this. And so, once you kind of

18 visualize that these are kind of the calculations,

19 the next part is kind of some of the inputs. So

20 the two inputs that we looked at are markups and

21 equipment price trends.

22 So there's a markups worksheet that I
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1 kind of started with when I was doing the slides.

2 It's a very similar setup. You have some input

3 parameters, the markups calculation and then

4 intermediate calculations we kind of already

5 discussed.

6 In this sheet, for example, for this

7 specific building, it's in region ID 5, and so

8 what it's doing, it's looking the individual

9 results for that region. Once that picks up

10 what's appropriate for that household, it'll go

11 down here and calculate what are the markups that

12 are going to be applied for the equipment prices.

13 In this case, we have a manufacturer's

14 markup which is fixed, 134, and then we have a

15 baseline and incremental markup which includes

16 sales tax if it's appropriate. And that's what's

17 outputted into the equipment prices. And you can

18 actually go back to the equipment prices -- let's

19 just -- oops, sorry about that -- and match that

20 these two values are the right values that we saw

21 as outputs before including this manufacturer

22 markup.
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1 Now, let's talk about the industry price

2 trend or learning rate. And then, that's this

3 parameter. Now, because we have the standard

4 that's fixed at 2021 which is when we are assuming

5 that the equipment is being purchased, this value

6 is 0.937. It's a fixed value. Obviously if that

7 year changes, then it would be a different value.

8 So that's what's being picked up. Now, the user,

9 if we go back to the summary, has the option built

10 in to actually change that -- the trend. So right

11 now it's looking at a decreasing default. If you

12 put no learning, actually click on that and go

13 back to equipment price trend, it'll change what

14 the fraction will be or what's being looked up.

15 So in this case, it's looking up a different

16 value.

17 So the equipment price will have a

18 different forecast in that case and you can

19 actually hard code this value if you thought that

20 that value was different. You can actually hard

21 code it to 0.5 or I want one and you can do that

22 as well.
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1 MR. M. HUNT: And this is Marshall from

2 PG&E. So you do not need Crystal Ball for some

3 analysis this looks like if you --

4 MR. FRANCO: Well, then again, the

5 results that you will see if you don't have

6 Crystal Ball are just for individual households.

7 MR. M. HUNT: Oh, okay.

8 MR. FRANCO: Yeah, and it becomes really

9 hard because then --

10 MR. M. HUNT: Got it.

11 MR. FRANCO: We don't have an average

12 household. There's all these variables and so it

13 would be a little bit hard to you. Yeah,

14 definitely once you change this, for example, you

15 want to run it and see what happens to 10,000.

16 MR. STANONIK: So Frank Stanonik, AHRI.

17 So the information you had up above, you were

18 showing cost in MSP in 2013 dollars and then you

19 showed I think the price, right --

20 MR. FRANCO: This right here would be in

21 2013 dollars, yes.

22 MR. STANONIK: Right, and then you
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1 showed what I think was the retail price or

2 something.

3 MR. FRANCO: This is correct, the retail

4 price 2013 dollars, yes.

5 MR. STANONIK: So that's also -- that

6 amount doesn't reflect the learning factor.

7 MR. FRANCO: It does, yes.

8 MR. STANONIK: Wait, it does or doesn't?

9 MR. CYMBALSKY: It does, the 2013.

10 MR. STANONIK: It does, but you put it

11 back in the 2013 dollars.

12 MR. CYMBALSKY: Yes.

13 MR. FRANCO: Correct.

14 MR. CYMBALSKY: It's real dollars, not

15 nominal, yes.

16 MR. STANONIK: Okay, thank you.

17 MR. FRANCO: So that takes a look at the

18 equipment prices. So hopefully that's more or

19 less clear. As we go through these very similar

20 the worksheets.

21 MR. CYMBALSKY: So I think now let's

22 jump to the installation costs. It's probably
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1 where there's more questions. Neil, question?

2 MR. LESLIE: I do have a quick question

3 and it's going to get into some of the other

4 aspects of this related to the product switching.

5 And that is, where would I find the costs of the

6 heat pump and the electric furnace and the

7 electric water heater and the gas water heater

8 that are all necessarily wrapped up in that

9 impact.

10 MR. FRANCO: Yes, and obviously I

11 mentioned that we were going to go to that when we

12 go to the fuel switching. But let me just show

13 you where those are at and then we'll go back to

14 it into more detail. So some of those inputs,

15 because they're related to equipment prices, the

16 markups we're assuming they're the same, are in

17 this sheet called equipment prices. And this part

18 of the equipment prices is what we've been talking

19 about, the non-weatherized gas furnaces. Now, if

20 we go to the right, we will find the equipment

21 switching options.

22 So let me just really briefly talk about
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1 what the options are and what the different

2 components here are. So we're considering a

3 central air conditioner and this would be the

4 retail price of that, a heat pump, this would be

5 the retail price and this is for the individual

6 household. So again, if the household is located

7 in a certain region, all these other factors, this

8 is the cost. Gas water heater, electric water

9 heater and electric furnace. When we go back,

10 we'll go into more detail as to some of the

11 assumptions.

12 So it's easier to understand because we

13 have to actually see the switching price. But as

14 you have questions, please --

15 MR. LESLIE: Absolutely. This is Neil

16 Leslie again. Are there distributions of

17 efficiencies for each of those options as well?

18 MR. FRANCO: Yes, and it depends on

19 certain inputs. So let me just very briefly -- so

20 for example, we consider what the heat pump size

21 is of the equipment. So depending on what the

22 heat pump size, it will have a certain cost. So
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1 let me go down here. For example, if we're looking

2 at a heat pump and we assign a two ton, it will

3 have this as the manufacturer production cost,

4 $900. But if it's a five ton, it will have this

5 cost.

6 MR. LESLIE: Understood. I guess my

7 question is --

8 MR. FRANCO: Sure.

9 MR. LESLIE: Is it only for a 14 SEER

10 heat pump or are there other heat pump

11 efficiencies within this that the consumer might

12 choose to switch to? Am I making sense, I hope?

13 MR. FRANCO: Yes, yes, that's a very

14 good question and I'll answer it.

15 MR. LESLIE: And so, if there is

16 information related to a more efficient heat pump,

17 this example, where in the spreadsheet would that

18 distribution be found is my question.

19 MR. FRANCO: Correct, correct. Yes.

20 Again, this is an equipment and we would go into

21 more detail, but the specific answer to that

22 question is that in this analysis we assume it's
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1 only 14 SEER. We assume that the household that's

2 being switched to, it's the minimum efficiency

3 because that's where the economics might make more

4 sense for that house.

5 MR. LESLIE: Neil Leslie again. That's

6 why I couldn't find it.

7 MR. FRANCO: Yes, yes.

8 MR. LESLIE: Thank you.

9 MR. FRANCO: Thank you. So let -- sure.

10 MR. MILLER: Bud Miller. Do you have a

11 learning curve for all that equipment, the

12 electric equipment, et cetera?

13 MR. FRANCO: Yes. Yes, so let me go and

14 show you that.

15 MR. MILLER: Okay.

16 MR. FRANCO: Yes, so for furnaces, we

17 use the furnace learning curve. For central AC

18 and heat pump, we have this central heat pump

19 learning curve which is similar values and for

20 water heaters we have its own learning curve.

21 MR. MILLER: What is it --

22 MR. FRANCO: Oh, water heater.
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1 MR. MILLER: Meaning no learning curve?

2 MR. FRANCO: That's correct, yes. And

3 some of the assumptions, years are used for those

4 inputs are here. For water heaters, it's just

5 one. There's no -- it's not changing, not a

6 variable.

7 Okay, so we're entering installation

8 costs. It's one of the more complicated

9 worksheets, probably the more complicated

10 worksheets in the spreadsheet. So bear with me as

11 we go through. There are a lot of details that

12 are very specific to furnaces. Almost all other

13 products, it's very -- there's less inputs, let me

14 put it that way and it's less complicated.

15 So to understand this, the whole what's

16 going on, you do actually need to understand the

17 furnaces and furnace market and how they're

18 installed and things like that. So as we go

19 through, obviously you'll have some questions and

20 we'll try to answer them as best we can. Now, a

21 lot of this goes back to the building sample in

22 the household that we're assigning and that will
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1 actually be the next section.

2 But let's assume that you have a

3 household that you've selected and it's in a

4 certain region and that you assume that it's a

5 replacement. So you have all these assumptions

6 there citing to that specific household. Again,

7 the setup is we have all these input variables.

8 So this is hopefully -- I'll try to make it not

9 too overwhelming but obviously this is probably a

10 lot, very overwhelming.

11 So this is the input parameters that

12 come mainly from the building sample and some

13 distributions and some information that comes from

14 other cells. But primarily for the building same.

15 For example, one of the primary things that we're

16 trying to calculate is the pipe length. To

17 calculate the pipe length, it's very complex. You

18 have to understand that it depends on the building

19 type. So what's going on is that let's say you

20 start with 80 percent or lower AFUE furnace that's

21 being installed in an old building. So what we

22 want to do is we want to try to see what's going
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1 on, what would be -- if they're trying to do

2 horizontal or if they could do some type of

3 vertical, what would be the vent length. So a lot

4 of these input variables are trying to determine

5 that.

6 One of them is, for example, the number

7 of floors in the building or household. And so,

8 that's an input that comes from RECS. RECS

9 actually tells us that this household has one

10 floor. Another input is whether that household

11 has, for example, a basement. Then obviously then

12 the furnace might be installed in the basement and

13 then you'll have one additional floor. Other

14 input variables that come from RECS are for

15 example if it has a high roof or ceiling, things

16 of that sort.

17 Let me focus on an additional one is the

18 installation location. That does not come

19 directly from RECS but there is a process that we

20 set up to try to simulate where the installation

21 location will be based on RECS variables.

22 Basically, for example, let's say that a household
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1 has a basement. Most of the time, the furnace

2 will be in the basement. So we assume that the

3 furnace is in the basement. When we do that and

4 we run this spreadsheet, we come up with similar

5 results to survey data from different sources. So

6 we assume that that's -- that that works out.

7 But RECS again does not tell us that the

8 furnace -- that's not a question in RECS. Then

9 after that, then we assume if it has an attic, a

10 certain percent of time it will be in an attic or

11 a garage, crawl space. So we have all these

12 installation locations.

13 Let me go back to the statistics. Some

14 of the -- those type of variables that are

15 important and to look at, kind of what we're

16 getting into final results, could be available

17 here. For example, this is the installation

18 location. And here, you can see the different

19 installation locations. Each one of these has

20 very specific installation costs that would be

21 associated with them and let me put this a little

22 bit larger so we can all see. This is what our
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1 assumptions is. It's basically a condition.

2 Actually RECS will tell us if it's

3 conditioned or unfinished or unconditioned, a

4 crawl space, garage, indoor, attic, if it's

5 conditioned or unconditioned. And these are

6 actually the fractions that result from running

7 this model 10,000 times. This is the national.

8 And let me just point out that the same -- you

9 could look at the north region or this is -- okay,

10 I'm not quite sure -- what I'd like to go up --

11 and the southern region.

12 So you can actually look at what those

13 are. Now, again, the question about if you wanted

14 to look at all the 10,000, you actually output

15 that and you can actually look each individual

16 household what that input for that installation

17 cost.

18 MR. MILLER: All right, this -- well,

19 this may be one of the questions I'm getting a

20 little gray area. So in the particular analysis

21 that we're using as an example here, there are

22 some percentage of installations that are in a
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1 commercial building. And in what you're just

2 showing us, I would say those were all

3 installation sites in a residential building.

4 MR. FRANCO: Correct.

5 MR. MILLER: Is there a different set of

6 -- let me call them installation options for

7 commercial buildings and I'm trying to envision

8 the CBECS, right. It's a commercial building. So

9 and is that based on what the building looks like

10 or is it still based on where in the building it

11 is for commercial because, boy, that gets really

12 squirrely.

13 MR. FRANCO: Yes, yes. And obviously

14 when you're trying to do this analysis, you are --

15 you try to visualize, right, what's going on and

16 what the input variables. And for example CBECS

17 does not actually provide where the building is,

18 like what location as well as with RECS. So what

19 we assumed is it's going to be indoors and

20 depending on the number of floors, we make some

21 number of assumptions about what would be the vent

22 length, so --
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1 MR. CYMBALSKY: SO this is John from

2 DOE. Yeah, we're getting close to over the line.

3 But let me just step back a little bit. So let's

4 just logically think this out. So some

5 residential furnaces are used in commercial

6 buildings. Why would that be? Because they're

7 probably very small buildings that actually look

8 like a house.

9 For example, a small restaurant or a

10 barbershop or something of the like that would not

11 put a residential furnace in a very complicated,

12 big office building such as this. So based on

13 that logic, I think we did an analysis and I think

14 we need to stop it at that point.

15 MR. FRANCO: That's correct, yeah.

16 MR. MILLER: Okay, thank you.

17 MR. FRANCO: So those are kind of the

18 inputs. Then obviously after you have those

19 inputs, you're making some assumptions and then

20 that's where some of the distributions are coming

21 in. So for example, one is the average ceiling

22 height. Now, we have some NHAB data that shows
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1 there's a distribution and you can actually see

2 the distribution here and fraction of the time

3 it's going to be eight feet.

4 A fraction of the time it's going to be

5 nine feet and a fraction of the time it'll be 10

6 feet. And that's based on that data almost 70

7 percent are going to be picked up as nine feet.

8 For this distribution you can actually see a

9 statistic and the mean would be 9.2 feet, the

10 average. But you're actually picking up the

11 distribution. So some households will be 10 feet.

12 Some households will be nine or eight feet.

13 MR. SCHROEDER: Can I ask a question on

14 those distributions?

15 MR. FRANCO: Sure.

16 MR. SCHROEDER: So that distribution and

17 then there are other distributions on this sheet

18 and others, do those depend on each other and do

19 they depend on some of these other inputs or are

20 they each one as a completely independent

21 variable?

22 MR. FRANCO: Victor Franco. So almost
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1 all variables in this spreadsheet, I almost want

2 to say that all of them are independent.

3 Obviously there are some dependencies that are

4 associated with them. If there's a strong

5 correlation, you can actually have a correlation

6 variable related to that. But that would require

7 more data in what would be that correlation

8 factor.

9 MR. CYMBALSKY: I do think it's safe to

10 say that in the 10,000 simulations that are run,

11 that'll be picked up. Those co-dependencies will

12 be, you know, mapped to one another and it's one

13 of the 10,000.

14 MR. FRANCO: So the distributions

15 themselves are kind of independent. But there's

16 if statements to say that this and this has to

17 match to be able to pick up a certain variable.

18 So there's some logic that tries to pick up the

19 dependencies that are obvious and then there are

20 some that might not be as obvious and you would

21 actually have to have the data to say that this

22 depends on this a certain fraction of the time
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1 with that data. What we have data on, and we have

2 fractions and we can do some if statements, we do

3 do.

4 MR. CYMBALSKY: Laura?

5 MS. PETRILLO-GROH: Laura Petrillo-Groh,

6 AHRI. I might be getting ahead or

7 misinterpreting. But does this assume that the

8 mobile home gas furnaces are all -- all have the

9 same installation?

10 MR. CYMBALSKY: Yeah, that's a different

11 meeting. Sorry. That's a good example of a

12 deliberative question.

13 MR. FRANCO: So those are the different

14 assumptions for different product classes. And

15 yeah, we're focused more right now on the non16 weatherized. But if you see the spreadsheets, the

17 inputs and the variables for both of them are

18 significantly different and that's based on just

19 the application. So we do look at the application

20 and we make those assumptions, yeah.

21 So obviously there's -- we then need to

22 look at whether the household is commonly vented.
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1 So if you're familiar with furnace installations,

2 some of the furnaces are commonly vented with

3 other products. Here we only assume that they're

4 commonly vented with water heaters. There might

5 be other equipment but here we're just assuming

6 gas water heaters with a gas furnace.

7 Other assumptions, for example, are

8 there's some apartment buildings and townhouses or

9 condos that might actually have -- they're sharing

10 a wall. The wall might actually be the closest

11 one that the furnace has. So it can't actually be

12 vented through that wall because it will be going

13 through the other apartment. So that wouldn't be

14 a good idea and it actually has to go through a

15 different wall or multiple walls.

16 So we make some assumptions about what a

17 fraction of the time there will be apartment that

18 wit will be really hard and you have to go through

19 multiple walls or very, very long vent length. So

20 that's taken into account.

21 MR. CYMBALSKY: So the takeaway there,

22 you know, just to generalize about the
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1 installation, the analysis in the spreadsheet, and

2 you can see the numbers, takes into account the

3 range, the entire breadth of different venting and

4 installation costs associated with that. So I

5 think that was an earlier question, so.

6 MR. FRANCO: So obviously it's pretty

7 complex. There's a lot of details very specific

8 to furnaces. And I'm just browsing through it,

9 right, but if you have any specific questions,

10 we'll see if we can answer them.

11 MS. JACOBS: This is Diane Jacobs from

12 Rheem. So you don't know which side is the front

13 of the house. So you're assuming -- you must be

14 assuming the furnace is somewhere and then the

15 closest -- are you putting it like in the middle

16 and then you're finding the closest wall or --

17 MR. CYMBALSKY: That's deliberative too,

18 so yeah.

19 MS. JACOBS: Oh, okay.

20 MR. CYMBALSKY: That'll be part of the

21 NOPR conversation.

22 MS. JACOBS: Okay.
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1 MR. FRANCO: That's correct.

2 MR. STANONIK: Frank Stanonik, AHRI.

3 But okay, I'm looking. The example right here,

4 you have two columns.

5 MR. FRANCO: Yes.

6 MR. STANONIK: And these are inputs,

7 right?

8 MR. FRANCO: That's correct.

9 MR. STANONIK: So what -- oh okay, never

10 mind. Missed the heading.

11 MR. FRANCO: I'm sorry, and I should

12 have gone over this at the beginning. All of

13 these worksheets have -- because we have two

14 products. No, there are other spreadsheets that

15 you might have like 16 product classes, different

16 products and then you have 16 columns. In this

17 case, we're only analyzing non-weatherized gas

18 furnaces and mobile home gas furnaces.

19 In the case of installation cost, you

20 can see that the inputs for non-weatherized are a

21 lot more. There's a lot more going on. Mobile

22 home, there's a lot less. It's more simplified
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1 because it's a different installation type.

2 MR. CYMBALSKY: So is there a table you

3 can point to that shows the values for --

4 MR. FRANCO: Yes, we're going to go to

5 that. So let me just mention that then there's

6 other inputs that are coming. We assume some AFUE

7 efficiency that's associated with the building,

8 the year that the house was built, the age of the

9 equipment that's currently being installed, what

10 type of wall is being.

11 So obviously if you have a wall that's

12 wood or something, it's very easy to drill a hole.

13 But if you have a brick wall or you have some

14 other, then it's more expensive. So we have that

15 from RECS. And also we mentioned about if they

16 have commonly vented or not. So we want to know

17 if it has the water heater and what kind of air

18 conditioner equipment for some of the other

19 assumptions that we're trying to make.

20 MR. SCHROEDER: So in that section I

21 actually just have a question. I think I can stay

22 away from being deliberative. This is Dave
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1 Schroeder, sorry. So in this particular example,

2 we have an AFUE existing of 90 percent, right?

3 MR. FRANCO: Yes.

4 MR. SCHROEDER: And that's I think just

5 randomly assigned, right? That's coming out of

6 RECS because that's not in there.

7 MR. CYMBALSKY: So this home already had

8 a condensing furnace.

9 MR. SCHROEDER: This home already had a

10 condensing furnace.

11 MR. FRANCO: Correct.

12 MR. SCHROEDER: In this case, if you run

13 this model for the 90 percent case, would there be

14 any life cycle impact, you know, in basically the

15 case where they reinstall what they have?

16 MR. FRANCO: So the differential,

17 because then you would be comparing more between

18 if you're going from 90 to 90, there's no

19 differential obviously. But then if you're

20 comparing 92 to 90, it depends on the assumptions.

21 MR. CYMBALSKY: It depends on what

22 standard level you picked.
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1 MR. FRANCO: Then there might be a

2 differential.

3 MR. SCHROEDER: Okay. But if you just

4 picked the 90 standard, then there would be zero.

5 MR. CYMBALSKY: It would zero, right.

6 MR. FRANCO: Yeah.

7 MR. SCHROEDER: Okay. So it would be in

8 the no impact work out.

9 MR. CYMBALSKY: Correct. It would be in

10 the non-impacted. They're already there, yeah.

11 MR. FRANCO: That's correct, yeah.

12 MS. PETRILLO-GROH: Laura Petrillo-Groh,

13 AHRI. What does five mean for installation

14 location?

15 MR. FRANCO: Yes, and --

16 MR. CYMBALSKY: So that's back to the

17 original -- I think --

18 MR. FRANCO: Let's go back to the

19 statistics. Five actually means indoor. There is

20 -- we try to have as much as possible what those

21 mean.

22 MR. CYMBALSKY: So you go to building --
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1 MR. FRANCO: If you go to building

2 sample --

3 MR. CYMBALSKY: Right.

4 MR. FRANCO: There is -- and we haven't

5 gotten there yet but let's just go there now.

6 There is a definition here of the numbers.

7 MR. CYMBALSKY: Yeah, so this is where

8 you pick up all those parameters you see on the

9 other spreadsheet. So if you wanted to pick up a

10 different set from the sample, this is where you'd

11 come in here and pick the one you want to do.

12 MR. FRANCO: And here is the list. So

13 for example, five is indoor. Four would be garage

14 crawlspace, basement, conditioned or

15 unconditioned. This would give you the list.

16 MS. PETRILLO-GROH: And because this is

17 looking at the case for one house and then this

18 goes through the Crystal Ball to assume some

19 distribution of installation in any of those other

20 locations?

21 MR. FRANCO: That's correct. So a

22 specific household, it's indoor location.
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1 MS. PETRILLO-GROH: Thank you.

2 MR. FRANCO: Yeah, and in another

3 location obviously it would be something else.

4 MR. CYMBALSKY: Right, so if you're

5 playing around with the spreadsheet, this is a

6 good question actually. So you come here and you

7 say, actually I want to do this house and then

8 write down what parameters from this table and

9 then you go to the other one and change five to

10 four and you can see the numbers change.

11 MR. FRANCO: How that would vary.

12 MR. CYMBALSKY: Yeah, so you'll see you

13 can go house by house to see if it makes sense.

14 If you changed it from the house type and the

15 location, et cetera, do the numbers that you think

16 would change in a positive direction change in a

17 positive direction. So that's like a good error

18 test that you might want to make, so.

19 MS. PETRILLO-GROH: Thank you.

20 MR. FRANCO: Thank you for the question.

21 So let me first just go to the final results

22 before going into more details. So at the end of
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1 it, for this case, we've divided them out into

2 certain categories of installation costs. So

3 we've divided them into the basic installation

4 costs and what those are, they're very similar for

5 all furnaces. It's just putting in a furnace,

6 taking the old one out. It's the replacement one.

7 That's just a basic installation. Then

8 there's some venting costs associated with them

9 and this would be the next column. Then there is

10 if sometimes when you're installing, you're going

11 from 80 percent to a 90 percent and you have

12 commonly vented water heater, you get into

13 something else called orphan water heater and then

14 this would be the associated cost.

15 In this case, there is no orphan water

16 heater. It's going from 90 to 90 so there is no

17 cost. And then you go into the condensate drainage

18 and that's applicable only to condensing

19 equipment. And then you get the sum of all these

20 and this would be your installation cost only for

21 non-weatherized gas furnaces. When you get into

22 equipment switching, there's additional costs that
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1 are being calculated separately. And again, we're

2 doing two product classes at the same time. Below

3 here you can see the mobile home and they have

4 different assumptions.

5 MR. CYMBALSKY: So I think now might be

6 a good time to pause for a little break. So let's

7 say five after. Ten minutes enough for everyone?

8 I know -- okay, so quarter after, reconvene.

9 (WHEREUPON, the foregoing recessed.)

10 MR. CYMBALSKY: -- today. So just to do

11 an agenda check, so we're going to hit up the

12 product, the fuel switching/product switching next

13 and then head into the NIA. I seriously doubt

14 we'll get to the GRIM model today. But my sense

15 is that these first two spreadsheets have more

16 interest anyway. We could -- like I said, if we

17 need to have another meeting like this on

18 methodology, we're happy to do that at a later

19 date.

20 Before we turn it back over to Victor on

21 the switching, we had a couple of questions that

22 were asked on the webinar. One of them from Carl,
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1 that's a deliberative question. So we're going to

2 table that for the NOPR meeting. Another question

3 came to

4 UNIDENTIFIED MALE: Can you tell us what

5 the question is so we know --

6 MR. CYMBALSKY: It's about the venting

7 and it's very specific about what we considered;

8 for example, sidewalks, streets, average snow

9 heights, et cetera, et cetera. So you know, we'll

10 get into that at the NOPR stage. And then David

11 Peretti asked more specifically about in the water

12 heater costs, can you explain what the water

13 heater is and did you have all these units -- what

14 type of unit it was, what did it have a power

15 vent, et cetera, et cetera, et cetera.

16 So this is a good segue I think into the

17 next portion of the presentation. And what we can

18 say about switching, it's not going to be much

19 because all of what's going on is mostly going to

20 be explained at the NOPR stage. But I will say

21 that the switching routine is based on economics,

22 okay? And so, if we think about that in a very
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1 broad sense, the economics of a particular

2 consumer to switch, the reason they considered it

3 is because the option in front of them is costly

4 and they're looking for a less costly alternative.

5 So if you just use that, if we frame it

6 that way and leave it at that, we can't get into

7 any more detail than that. I think what I just

8 said answers David's question in terms of what

9 type of water heater or what type of heat pump

10 they might consider.

11 Clearly it's an economic decision.

12 They're switching based on an economic decision.

13 What is their alternative? That doesn't -- it

14 probably means that they're least cost people,

15 right? So just think of it that way. I'm not

16 going to answer it anymore beyond that. But I

17 think it's pretty clear that the switching's for a

18 reason and we'll leave it at that.

19 So back to Victor. We'll hopefully --

20 like I said, fuel switching, NIA and then we have

21 to -- we have to end at 1:00. So here we go.

22 MR. FRANCO: Great. Thank you, John.
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1 Victor Franco again. I just wanted to spend five

2 minutes just to go over a little bit all the other

3 worksheets, just very briefly and then we'll go

4 right into fuel switching just to finalize. And I

5 wanted to bring two points into the installation

6 cost. Obviously the installation cost, the

7 calculations and some of the additional

8 assumptions if you're trying to understand this

9 spreadsheet are below, and there's many, many

10 other aspects of it that are going here in this

11 area. And you can see there's distributions and

12 there's assumptions and based on locations and

13 just wanted to emphasize that.

14 The next is there are some forecast

15 cells. So you can disaggregate what kind of costs

16 are going to the individual household. So if you

17 actually wanted to look at one household or the

18 10,000 households and look household by household,

19 you can actually do that as well and see what the

20 basic installation, what the venting cost, was it

21 applied, wasn't it applied. You can do that as

22 well.
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1 The actual calculation, there are two

2 parts. One is the detailed part. Actually we're

3 calculating the individual components that go into

4 it. What's the cost of the thermostat? What's a

5 little bit of the gas piping, the connection, the

6 actual installation? And for venting, different

7 types of components that are going into it. Then

8 those are summed up and then they go into this

9 kind of subtotal field, the installation cost

10 component. And then those are totaled up in what

11 we were looking at before the break, which would

12 be this part.

13 When we come back to fuel switching,

14 there's actually -- below there's a fuel switching

15 component. And similar to equipment cost, those

16 are -- they're different assumptions and they're

17 different costs that are associated with those.

18 The last point because now we're wrapping up the

19 total installed costs is that these costs are then

20 inputted into, again, the payback -- LCC payback

21 calcs.

22 So if we go into one of these fields,
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1 these are picking up the retail installation cost

2 that we got from those two worksheets. And that

3 gives us the total installed price. So that kind

4 of wraps up that part. The final, final, final is

5 that if you wanted to look again from the 10,000,

6 what is the average, for example, you could go to

7 the summary and then this is the average of

8 everything. Of all 10,000, this is the total

9 installed cost. This is the average from 90 and

10 that's your differential.

11 And obviously everything that we discus

12 about individual households goes into these --

13 coming up with these final results. So hopefully

14 in five, maximum 10 minutes, really briefly go.

15 So there' a lot of other inputs that are going

16 into this. Next would be the adjusted final

17 installation cost. There's actually an

18 installation cost data sheet that's being pulled

19 out, some data. This is just data. And then

20 there's a sheet that pulls out labor costs.

21 And for example, in this sheet you

22 actually have different labor costs for different
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1 regions of the country that's being pulled out

2 into here. And so, for individual households, if

3 you see what the cost is, you're going to get $43

4 here for example. But if you go to another region,

5 it'll be completely different because it's a

6 different region of the country.

7 Maintenance and repair is also taken

8 into account. Now we're talking about the

9 operating costs and so this now goes into costs

10 that happen after the installation of the product.

11 There's some different considerations, assumptions

12 that are going into the calculation.

13 But again, it's a similar structure. We

14 have some import parameters. We have the final

15 results which show the exports. And we have non16 weatherized and the switching again. And we have

17 some calculations or assumptions, intermediate

18 calculations that are going on. So obviously we

19 don't have time to focus on everything. But these

20 are very similar. A lot of the products have this

21 type of setup.

22 Let's go before going to the building
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1 sample, or get -- okay, let me just use building

2 sample really simply. So this kind of ties up the

3 idea of what are the weights that are being used.

4 And this is very important to understanding what

5 data's being pulled and how often it might be

6 pulled. Here, we provide the actual weighting

7 that we're using in the spreadsheet. We actually

8 have two sets of weights. We have a replacement

9 sample and a new construction sample.

10 This is residential. And in the case

11 for this product, we also have building sample for

12 from CBECS. And a same, we have replacements and

13 new construction. If you go through -- let's go -

14 - for example, there's not a lot of commercial

15 buildings that use residential. So you see a lot

16 of zeroes. So essentially, if it's a zero, it's

17 not being used. But you'll find that there is

18 one, for example, here that has a weight, 139.

19 So let's discuss a little bit what that

20 weight means. So RECS provides a weight that's

21 based on the actual weight of the stock of that

22 building, the building stock. Now, what we're
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1 trying to do is we're trying to look at the

2 shipments and the shipments in 2021. So we do our

3 own weight, weighting that's based on shipments

4 and that produces a different result which

5 actually matches the number of shipments that are

6 in the NIA spreadsheet.

7 In this case, we have 3.5 million

8 shipments in 2021. We went to the NIA spreadsheet

9 and this is a result. And then, there' a logic

10 between how many are replacements and how many are

11 new construction that is also provided by the NIA

12 spreadsheet. In this case, it's about 25 percent

13 new construction, 75 percent replacements and

14 about 97 percent are residential and 3 percent are

15 commercial. That all comes from the NIA

16 spreadsheet.

17 The last part is -- so now that you

18 understand that these are the weights, RECS

19 provides for that building different inputs. So

20 for example, let's say this is a building that's

21 being sampled, building number ID 2 from RECS.

22 These are all the inputs that we use in our
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1 analysis. These are what's being captured. And

2 you can see over here this is actually called

3 export parameters and this is what's being

4 captured. And this goes to different worksheets.

5 The last thing here to then look at is

6 what's the actual household ID that's being used

7 for this and you need to look at two things. So

8 one is whether this is from the RECS or CBECS.

9 This is actually being pulled from the CBECS

10 sample. This is two. Let's change this to one

11 just to look at a RECS. The household that's

12 actually being sampled is 5,975. So if you want to

13 actually look at that household, you would have to

14 go 5,975, go all the way down and you would see

15 what those inputs are. And those are the inputs

16 that are being picked up for that household.

17 Hopefully that gives you a little bit of

18 an understanding of what's going on and how you

19 might be able to change to see a specific

20 household. Obviously you could change what's

21 being --

22 MR. STANONIK: Victor, okay -- so these
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1 first two columns for the RECS data -- okay, so as

2 I understand RECS, it's a survey of certain

3 thousand number of houses, actual survey of

4 houses, right?

5 MR. FRANCO: Correct.

6 MR. STANONIK: Twelve (thousand),

7 13,000, something like that?

8 MR. FRANCO: That's right.

9 MR. CYMBALSKY: For the 2009 survey.

10 MR. STANONIK: Yeah, yeah. Okay, so

11 that's the first column. It's that particular

12 house, right? So then the weighting is what, the

13 estimate of the number of houses nationwide that

14 are like that ID?

15 MR. CYMBALSKY: Correct.

16 MR. FRANCO: This column I, yes, it's

17 called end weight. That's actual variable that's

18 provided from RECS.

19 MR. CYMBALSKY: From RECS and it's the

20 weighting that they use to make the sample to a

21 population.

22 MR. FRANCO: Correct.
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1 MR. STANONIK: All right, so if we want

2 to - - okay, so I guess -- well, it'd be nice if

3 that was just a percent in terms of trying to

4 understand the effect of the weight, I guess. But

5 okay.

6 MR. CYMBALSKY: Well, I think for the

7 math to work out you need to have a number.

8 MR. STANONIK: I got it, yeah.

9 MR. CYMBALSKY: But sure, you could

10 actually just make another column, sum it up and

11 do your division if you'd like.

12 MS. JACOBS: Okay, and then the fourth

13 column you said was DOE assigned it their own

14 weight?

15 MR. FRANCO: That's correct. For each

16 of --

17 MS. JACOBS: And what's the basis -- how

18 do you come up with 357 instead of 85, 56 or --

19 MR. FRANCO: So basically the baseline

20 shipment we know by state how many shipments have

21 been historically shipped to certain regions of

22 the country.
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1 MS. JACOBS: How is that related to a

2 house? Oh, you're just going regional?

3 MR. FRANCO: Yes, regional.

4 MS. JACOBS: Okay.

5 MR. FRANCO: Because we have shipments

6 by state and that slightly changes the

7 distribution.

8 MR. STANONIK: If I can, so would it be

9 fair -- as an example, okay, so the number two,

10 the weighting is what, it looks like 85 or

11 something. Let's say it's 8,600. I'm just looking

12 at the second column. Let's say it's 8,600.

13 MR. FRANCO: Yes.

14 MR. STANONIK: If we knew that in the

15 U.S. let's say the mix of heating was 50 percent

16 gas, 50 percent electric and being very simple

17 here, then the weighting for the gas furnace would

18 be half of that number, right, if there were no

19 whole other issues. That's kind of what you're

20 doing, right? Kind of?

21 MR. FRANCO: Not completely clear your

22 question. But let's say for example there's 110
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1 million households in the U.S. and you're saying

2 that 50 percent are gas. So let's say that's 55

3 million households. And then this represents

4 8,600 houses. So if you divide 8,600 divided by 55

5 million, that gives you the percentage that

6 represents.

7 MR. STANONIK: Yeah, right. That's kind

8 of what you're doing on a little more

9 sophisticated basis.

10 MR. CYMBALSKY: Use the mic.

11 MR. STANONIK: Pulling in shipments

12 because you're looking at what happens -- oh, I'm

13 sorry. Frank Stanonik. But so you're pulling in

14 shipments because you're really interested in

15 what's happening as we go forward in terms of

16 energy savings year to year.

17 MR. FRANCO: Exactly.

18 MR. STANONIK: Got it, okay.

19 MR. FRANCO: And here then how you would

20 do it is from 3.5 million shipments, you divide

21 this 357 and that gives you the fraction of how

22 often this would be sampled probability-wise. So
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1 that hopefully helps you out how this is done.

2 Now, let me just really point out because I know

3 that this might be something that you want to play

4 around with, how you might want to play around

5 with this.

6 So for example, there's one. So for

7 example, let's say you want to change the

8 residential commercial sampling or you just want

9 to look at residential. This would be the cell

10 that you would change that in and then you would

11 change that part. Let's say that you want to

12 actually change the distribution so that this gets

13 into actually using Crystal Ball. And then, you

14 would maybe change this numbers here and then

15 these actually numbers are being populated down

16 here. Let me see if I can -- yeah.

17 So these numbers are coming from these

18 cells as the sample. Then you could actually have

19 to change this. Let me see if this doesn't take

20 too long. Okay, great. That didn't take too long.

21 This is the actual looking at the household IDs,

22 12,000 household IDs and what's their relative
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1 probability of being shown up. So if you change

2 it here, then you would change your sample. So

3 for example, let's say you only wanted to look at

4 households in New Jersey or you only wanted to

5 look at households in California. So then you

6 would take out all households that are not in

7 California or not in New Jersey or something like

8 that. Then you would have to change here the

9 distribution. Then you would be only looking at

10 the impacts for that specific part.

11 MR. MILLER: Just a process question on

12 that. How would you then do that thing you just

13 said? How would I -- just as an example, how would

14 I just get New Jersey?

15 MR. FRANCO: So that requires two

16 things.

17 it's the first thing is the easiest way

18 to do it is you would maybe have an equation here

19 or just have some way of which ones you are

20 picking up or just two columns. Basically you

21 just need two columns. One is the ID column that

22 matches this number and then some kind of weight.
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1 And say, for example, you wanted to use non2 weatherized, this, the RECS weight for example.

3 And so, let's say for example we want to

4 equal this to this. Whoops, let's say, another --

5 so for example, let's say so every time this pops

6 up, that's a gas furnace. So you have the logic

7 that it's a gas furnace. Instead of using this

8 weight, you use this weight, for example. Or that

9 only households in certain part of the region pop

10 up, something like that. So you have your logic.

11 Then after that, the way to do it in

12 Crystal Ball is you want to select these two

13 columns as your data fields and you kind of blank

14 out what was there before. Do you want me to go

15 through that or --

16 MR. CYMBALSKY: That's a lot more time

17 than I think we need to do on that right now.

18 MR. FRANCO: Okay, great. So yeah, but

19 that's basically how you would do it. So I think

20 that was all to kind of get a sense again. All

21 these are the inputs that are going. We tried to

22 make it so that you know where things are going
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1 to. And hopefully you get a sense that there's a

2 lot of data that's being pulled from RECS.

3 Now, let's very briefly go -- and this

4 is kind of a question that was asked before a

5 little bit about how the selection of the

6 efficiency of the current household is being

7 conducted. And so, it happens here. This is

8 called the base case AFUE. And so, this would be

9 the AFUE of the household that's installed in

10 2021. There is -- it depends on the region. We

11 have some efficiency shipments by efficiency,

12 historical shipments by efficiency for each

13 region. So for example, in northern states, they

14 have a larger fraction of condensing. Southern

15 states, they have a larger fraction of non16 condensing.

17 Based on that region, for example, now

18 it's being sample region 21, for example, let's

19 say Texas, it's looking up that 97 percent of the

20 time it's going to be non-condensing. And so, for

21 this household, it actually is picking up 80

22 percent AFUE as the existing furnace in that.
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1 Now, if you're in a different region -- let's say,

2 for example, you're here in a mobile home, has a

3 different distribution. It's actually picking up

4 92 percent because it's actually in Kansas and

5 Nebraska. So it's more likely that it'll be a

6 condensing.

7 MR. SCHROEDER: This is Dave Schroeder.

8 Did you say that those distributions have been

9 projected to 2021 or this is --

10 MR. FRANCO: That's correct.

11 MR. SCHROEDER: Okay, so this isn't

12 current. This is 2021 projected.

13 MR. FRANCO: And now we'll go into the

14 historical part. Just one very brief point. If

15 you notice, there's a lot of tables, lookup tables

16 and that depends on the market. So this one that

17 I was showing is just residential replacement.

18 Obviously if the household is a commercial, it has

19 a different distribution or if it's new

20 construction, the distribution is below. Mobile

21 home has a different distribution.

22 So that deals with the 2021. Now, if
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1 you want to look at the existing, then this is

2 this sheet. And this sheet again looks by region

3 and looks at historical data and we stop at 2009

4 because that's when RECS's last year and for

5 commercial 2003. So then you look at the age of

6 the furnace. For example, this one is 2005

7 because it's 2009 minus four years. It's already

8 four years old and then you will look at the 2005

9 distribution based on the ID of the equipment.

10 And so, this one is actually pricing ID seven. So

11 that's how that functions.

12 For air conditioning and heat pump

13 equipment, the historical efficiency comes from

14 these average shipment-weighted distributions. So

15 depending on the age of the equipment, you

16 actually have an average.

17 MR. STANONIK: Victor, this is Frank

18 Stanonik, AHRI.

19 MR. FRANCO: Sure.

20 MR. STANONIK: And John, I may be

21 getting a little again off here. All that

22 information on the right, you said that it's
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1 distribution.

2 MR. FRANCO: Yes.

3 MR. STANONIK: That's shipments, though,

4 right, not distribution products in the field.

5 MR. FRANCO: That's shipments and

6 percent of shipments.

7 MR. STANONIK: Okay. I just want to

8 make sure we're clear.

9 MR. FRANCO: Yes.

10 MR. STANONIK: Okay.

11 MR. FRANCO: Yes, yes.

12 MR. M. HUNT: Victor?

13 MR. FRANCO: Yes.

14 Mr. M. HUNT: Can I back up just to

15 where we were? The 2021 forecast of installations

16 -- so in New Jersey, the 31 percent that are still

17 going 80, that's because they would have failed in

18 economic tests to move to high efficiency -- no?

19 MR. CYMBALSKY: This is the baseline.

20 So this is not the standards.

21 MR. M. HUNT: So --

22 MR. CYMBALSKY: This is absent --
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1 MR. M. HUNT: Absent anything happening,

2 this is what would have happened in 2021.

3 MR. CYMBALSKY: Right.

4 MR. M. HUNT: Okay.

5 MR. CYMBALSKY: With no regulation.

6 MR. M. HUNT: With no regulation, that's

7 what would have happened.

8 MR. STANONIK: Additional regulation.

9 MR. M. HUNT: Right.

10 MR. CYMBALSKY: We haven't had a

11 regulation in this space since I've been alive I

12 don't think, Frank.

13 MR. STANONIK: I'm sorry.

14 MR. M. HUNT: So moving forward, you

15 mentioned that the -- we're not there yet.

16 MR. FRANCO: Great, so a couple more

17 topics here. So the next big one is energy use

18 and obviously we don't have time to go too much

19 into energy use. Energy use is very, very similar

20 to everything that we've been talking about, has

21 input parameters, has export parameters. It's

22 actually divided by month. We try to estimate what
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1 would be the monthly and we then compare that to

2 energy prices. We actually have average and

3 marginal energy prices would be the next topic

4 that are by month and we tried to match both the

5 fuel use and electricity use to those prices.

6 We then have the actual calculations as

7 part of the -- so the more complicated

8 calculations as part of this energy use calcs just

9 for the non-weatherized gas furnace and mobile

10 home. That's part of this worksheet. And this

11 worksheet goes into a lot of detail. It's very

12 similar to the test procedure and assuming how

13 often if it's a two-stage it would be in the high

14 and the low, makes all these assumptions and

15 calculates the end result is -- let me go back a

16 little bit -- is the actual heating energy use and

17 electricity use which would be these two columns.

18 And then that gets inputted into the

19 previous sheet, which is the energy use. Stop me

20 if I'm going too quickly, but we kind of need to

21 go a little bit quickly. Now, the energy use for

22 the equipment that we discussed are the switching
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1 -- is available here in this sheet. This is again

2 the calculation of the heat pump, CAC equipment

3 and the water heating equipment, either electric

4 or gas equipment and this would go into the sheet.

5 Very similar, they have some import

6 parameters. You have the calculations and the end

7 result are by month the energy use.

8 MS. JACOBS: Can you make that a little

9 bigger?

10 MR. FRANCO: Sure. Yeah, and I'm very

11 sorry because we're kind of rushing through. But

12 please stop me if -- yeah, if there's something

13 that you want to look at more carefully. And

14 here's the export. And obviously there's a lot of

15 input data that goes into that that we're trying

16 to calculate. So here is the monthly. You can

17 see here for electric furnace and we'll see this

18 in a little bit more detail once we get to the

19 switching and then the annual value for this

20 particular electric -- in this household.

21 Now, there's some data that's being

22 pulled out that's necessary for others. For
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1 example, we need to determine what's the size of

2 the furnace and the air conditioner. This would

3 be the worksheet where that gets determined. We

4 don't have time to go into that detail but that's

5 where this is determined. I'll just leave it at

6 that. Energy prices, this is where those are

7 being determined. These are the energy prices in

8 2012. This is the reference here, 2012. And we've

9 calculated both average energy prices and marginal

10 energy prices.

11 Just wanted to briefly go very quickly

12 how that's being done. We have by region we have

13 monthly energy prices. These would be the average

14 monthly energy prices and then both for natural

15 gas and electricity we have marginal price factors

16 either for non-winter and winter. We calculate

17 these price factors. So what happen when you have

18 a region that you're looking at, say for example

19 we're in region 21, we go look up, for example,

20 the prices in Texas. These would be the monthly

21 prices in Texas.

22 This would be the average. These are
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1 being picked up over here for natural gas fuel.

2 And then, we multiply those times the marginal

3 price factors either if it's a winter or summer or

4 non-winter month. And this would give you the

5 marginal price. In this case, for this particular

6 household, the price for example in January month

7 one would be $9.05. The marginal is $7.67. So

8 it's lower and that's the price that's being

9 applied for that house.

10 MR. STANONIK: That's the price being

11 applied -- Frank Stanonik, AHRI. Which price is

12 being applied, the nine whatever or the seven?

13 MR. FRANCO: Yeah, that gets -- just I

14 don't know how much I can go into detail. But the

15 average gets applied to kind of your base energy

16 use and the marginal is to your savings. I think

17 I can --

18 MR. MILLER: This is Bud Miller. The

19 development of the marginal price factor, can you

20 discuss that?

21 MR. CYMBALSKY: No, we can't. That's

22 deliberative.
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1 MR. FRANCO: Okay. The last part of

2 that is obviously those are costs for 2012 and

3 then we want to project those to the future. We

4 use AEO projections and because 2012 is the start

5 year, obviously that's one. That's the start

6 year. And then, as we project further, we have

7 this factor. So the number that we looked at

8 before times this 1.075 would be the price in 2021

9 and then so on as you go further in time. These

10 are projections are by the census division. That's

11 how AEO defines this is the census division.

12 So the last two parameters before going

13 to fuel switching, well, let's just finish with

14 energy usage. These adjustment factors that we do

15 for climate conditions and there is some weather

16 data also associated with it. And so, those are

17 adjustments that are used in the energy use

18 calculation.

19 The two other important parameters that

20 are being used are the discount rate. Those are

21 being calculated in the sheet. Again, we have

22 some import and we have some export data and
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1 export data obviously is the discount factor, and

2 the lifetime. And the lifetime is a distribution

3 and these are the parameters that go into

4 lifetime. We have different lifetime for

5 different equipment.

6 So we have one lifetime for furnaces,

7 one lifetime for water heaters and one lifetime

8 for central AC and heat pump and these are the

9 resulting values that are coming from just doing

10 one run. If you wanted to see some of the

11 statistics, again you could look at defining the

12 assumptions.

13 And for example, here the statistic is

14 the mean of this distribution is 21.5. You can

15 look at other statistical information. You can

16 actually look at the probability and this is a

17 Weibull distribution and so you can actually see

18 how that looks and more or less 21.5 is right

19 here. So that's and you can go look at the other

20 ones as well.

21 Similar to -- and these parameters, if

22 you wanted to change that, then you could either
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1 change the assumption or you could take out the

2 assumption and just have a fixed value. So that's

3 how you would handle changing something like this.

4 MR. STANONIK: Victor, Frank Stanonik,

5 AHRI. And I think this is a general question.

6 When you're running these analyses, does DOE

7 normally use a different discount rate for a new

8 installation compared to a replacement

9 installation on the theory that a new

10 installation, your cost is really I'll say hidden

11 in your mortgage as opposed to a replacement is

12 likely, you know, direct consumer payment, if you

13 will, or I'll say charged at a different rate? I

14 mean --

15 MR. CYMBALSKY: That'll be a NOPR

16 question.

17 MR. STANONIK: Okay.

18 MR. FRANCO: So the next big topic is

19 fuel switching. So we actually have a sheet

20 that's called non-weatherized switching and this

21 is completely different from any other product

22 that we have. So this is something that you would
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1 only see here in the furnaces. Everything else

2 is, as we talked about, is very similar.

3 So this sheet is kind of like the

4 calculation sheet of switching when it occurs. So

5 again, this is a very, very introductory. There's

6 a lot of details that are associated. I'll try to

7 go as much as I can. But we also want to get into

8 the NIA. But please stop me if there's something

9 that I can answer in here.

10 So there's some input parameters as

11 before and there's some logic that goes into why

12 we do certain things. Some of them we can't

13 really talk about but I'll try to best present

14 what's here without going too much into the

15 analysis. One thing just to understand the

16 structure is that you have different situations.

17 Let me call it like that.

18 So you might have a household that's in

19 RECS that has a non -- starts with a non20 weatherized gas furnace. All these start with a

21 non-weatherized gas furnace obviously. And then

22 they have either a gas water heater or an electric
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1 water heater. So that'd be the second level. And

2 so, this is, for example, the first is they just

3 have a non-weatherized gas furnace and gas water

4 heater.

5 For this one, they don't actually have a

6 cooling. They might have a room air conditioner

7 or no cooling, something else. Then the next one

8 that we find is non-weatherized gas furnace and

9 electric water heater. Then there's households

10 that have non- weatherized gas furnace, gas water

11 heater, central air conditioning. Those are

12 usually the most common. Then we have non13 weatherized gas furnace, electric water heater,

14 CAC and then the same but with a heat pump. And

15 obviously this is just data from RECS, like that's

16 how the households are currently being built.

17 From that, obviously we're then looking

18 at what would be the switching options. In this

19 case, the households that we're looking at is a

20 household configuration. So let me go back here.

21 This is kind of the data that's being pulled from

22 RECS. So for this household, it hasn't obviously
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1 all households have non-weatherized gas furnace,

2 gas water heater and a CAC unit and a

3 configuration and it's seven.

4 An additional thing that this household

5 has is that we assume that this is commonly vented

6 and that comes from some of the assumptions in the

7 installation costs. But because it has these two

8 plus some other considerations, this is commonly

9 vented. So that's another important parameter.

10 But this is basically a configuration

11 number seven. So if we go back, this is what we

12 were talking about, configuration number seven.

13 So if you're in configuration number seven, you

14 have some different switching options. So this is

15 the more -- the one that has the most options. So

16 you could again go with a non-weatherized gas

17 furnace, which -- so we're only considering the

18 case where you're going from an 80 percent non19 condensing to a condensing when we're doing the

20 switching.

21 So one is that you remain with a non22 weatherized gas furnace. But because of the
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1 standard, you have to go to condensing. And you

2 remain with your existing equipment, gas water

3 heater and central air conditioning. But it could

4 be the case that you're commonly vented and it

5 might not work out. You might want to install an

6 electric water heater. So this would be that

7 option. So this is option eight. The next is you

8 actually switch your electric -- you're going from

9 a non-weatherized gas furnace to an electric

10 furnace. You switch your heating but you remain

11 with the same cooling equipment.

12 Then the next thing is that you could do

13 is -- and obviously you could do gas water heater

14 or you'd switch to an electric water heater. The

15 next thing you can do is you switch to a heat pump

16 so that essentially switches your cooling

17 equipment and you have to have an air handler as

18 well. So those would be the options.

19 MR. M. HUNT: Victor? Victor, this is

20 Marshall Hunt at PG&E.

21 MR. FRANCO: Sure.

22 MR. M. HUNT: The column O, I just don't
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1 understand what the title is, hot water, water

2 heating to number two. What is that?

3 MR. FRANCO: Yes, yes. Sorry and that

4 probably is more like we understand. But

5 basically what this is telling us or the logic is

6 that whether it's switching to a different water

7 heater. So for example, here it's not switching

8 but here you're switching to electric. And so,

9 this is more for the logic. Yeah. Sorry about

10 that. That labeling there is not that clear.

11 So once we know that and we have all

12 those switching options, then we can go to the

13 calculation kind of piece of the puzzle here which

14 is this part, column R to -- all the way to column

15 AN, this part of the worksheet. Everything else

16 that we might talk about is the inputs to this.

17 So obviously because it's that configuration,

18 those are the options that are being picked up.

19 So we start with a non-condensing, non20 weatherized gas furnace, seven, and then if we are

21 going to look at 90 percent standard, these would

22 be the options that it could switch to and this
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1 would be the 90 percent and then at 92 these would

2 be the options again but now you're looking at 92

3 percent non-weatherized, 95 and 98.

4 So depending on the economics, the

5 ability to switch might change if you're at 98 or

6 90 because the equipment price might be different

7 and so your economics will change. Everything

8 else here are the inputs. So the two primary

9 inputs that we're looking at here are the

10 operating cost, so it relates to the annual fuel

11 use and the electricity use, and the installation

12 cost -- the total install cost which obviously the

13 retail price, installation cost.

14 So the first row in each of these

15 switching sub-tables are what we've been talking

16 about. This is the one we were just looking at,

17 the non-weatherized gas furnaces. What's the

18 energy use? This is the energy use. What's the

19 electricity use? This is the electricity use.

20 What is the retail price? What is the

21 installation cost? What's the total install cost?

22 Then we have what's the maintenance.
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1 This is more of an input to the final result and

2 all these other parameters. When we're actually

3 looking at the switching, whether it's going to be

4 doing a switching or not, then we're looking at

5 kind of a payback criteria. So based on the

6 differential cost of going to a condensing 90

7 percent versus some of these other options, then

8 you have a certain payback. So let's look at that

9 a little bit more carefully how that works.

10 So the first thing we want to take a

11 look at when we're looking at this is we're only

12 considering cases where the cost of the equipment

13 you're switching to, the total install cost is

14 less. In the case of this specific household, the

15 only situation that occurs is in this option right

16 here, which I believe is an electric furnace, gas

17 water heater, CAC. And that's why the payback is

18 only being calculated for that.

19 Everything else is above that. So

20 that's the first thing. Then this switching is

21 basically just the switching payback is just

22 basically the comparison between the 90 percent
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1 and the switching option. And then the switching

2 occurs based on just a switching criteria payback.

3 Let's go back to the input. This is we're using a

4 3.5 year payback criteria. So based on that

5 criteria, we're saying that it's going to be

6 switching if it takes 3.5 years to pay back going

7 to the gas furnace versus switching to another

8 equipment.

9 If it takes less than that, for example

10 in this case 0.2, then it's not going to be

11 switching. Let's just see why this is occurring in

12 this case. Here we only have a differential of

13 $200 between this and this, between the 90 percent

14 and the electric. But the differential in

15 operating costs is almost $1,000. So obviously in

16 the first year, like 0.2 years actually here it's

17 not. It's going to be recuperated.

18 So that's -- it's not going to very

19 happen - - very likely that that's going to

20 happen. And then the model then assumes that

21 that's -- that doesn't occur in this case. So the

22 final output is actually going through all those
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1 in situations and seeing if there's going to be a

2 switching. So for example, in this case we start

3 with a seven option and we end up with a seven.

4 There's no switching, seven, seven.

5 If there was switching, then it might be

6 like seven to nine or seven to a different number.

7 And this might actually vary. It might be that

8 for this level it might be switching. For the

9 next level it might not be. It just depends on

10 the economics for that individual switching.

11 MR. SCHROEDER: Can I just ask a

12 question --

13 MR. FRANCO: So hopefully that finalizes

14 and let's go to questions. Yes?

15 MR. SCHROEDER: So I'll try to stay away

16 from anything deliberative here.

17 MR. CYMBALSKY: Name?

18 MR. SCHROEDER: Sorry, Dave Schroeder.

19 If there are multiple options that, say, cross

20 that 3.5- year threshold --

21 MR. FRANCO: Yes.

22 MR. SCHROEDER: Where is the logic
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1 contained that would decide which of those options

2 would get chosen?

3 MR. FRANCO: So it would be the one that

4 has the best economics of those options.

5 MR. SCHROEDER: But where in the model?

6 Is that in column D here near the bottom?

7 MR. FRANCO: This would be I believe in

8 -- and that's a good question. This would be --

9 this would be right here. As you can see, this

10 right here is looking at that row and that's

11 exactly -- the logic is embedded in here. So it

12 would be the one that has the more benefit. So

13 for example, let's go into a case and see. Let's

14 say either it's going to switch to a heat pump or

15 an electric furnace. Because the economics maybe

16 of the heat pump are very favorable because of the

17 operating cost, we assume that they'll choose the

18 heat pump.

19 But it could be that maybe the

20 economics, because of the install cost of the heat

21 pump is very expensive for another household, they

22 might actually choose the electric furnace. So it
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1 just depends on the individual case and the

2 economics and it's specific --

3 MR. SCHROEDER: And this allows for

4 negative numbers as well, right? You can have --

5 you could have negative cases here where you have

6 a first cost advantage and you have a first year

7 operations cost advantage.

8 MR. FRANCO: Yes. That doesn't happen.

9 For example, like with an electric furnace. But

10 it does happen for a heat pump. There could be

11 situations where you have a heat pump in a certain

12 region that the economics play out that the gas

13 price -- the electric and the gas prices are very

14 competitive to the electric price and you have

15 actually less operating costs compared to the 90

16 percent. So for example, in this column, you

17 might have that the heat pump option is less than

18 590 and that would lead to a negative result in

19 that case, correct. Yes?

20 Mr. MILLER: Just to make sure I

21 understand the answer of that thing, then in that

22 negative payback situation, there would be a
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1 switch?

2 MR. FRANCO: In that negative payback,

3 there would be a switch yes.

4 MR. MILLER: Thank you.

5 MS. PETRILLO-GROH: Hi. Laura Petrillo6 Groh, AHRI. How does this tab interact with the

7 building sample tab?

8 MR. FRANCO: Good question. So we kind

9 of very quickly went through that. But there are

10 some things that I kind of browsed over that are

11 important inputs obviously. So the first one for

12 the building sample is that it's picking up for

13 that household. It's picking up what type of

14 equipment it's associated with. It's picking up

15 that it's a gas water heater that's in the

16 household and it has a central air conditioner.

17 So that comes directly from RECS for that specific

18 household.

19 There's also some life remaining -- not

20 -- the existing equipment age that is also being

21 picked up from RECS. Those would be the primary

22 inputs that are directly. But everything else
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1 that's associated, for example, the installation

2 costs, energy use are originally some of the data

3 that to produce those are coming from RECS.

4 MS. PETRILLO-GROH: If I understood

5 correctly, the building sample only accounted for

6 households with gas. Does the information on fuel

7 switching get fed back to that?

8 MR. FRANCO: Repeat?

9 MS. PETRILLO-GROH: If the building --

10 the building sample tab only accounted for

11 households with gas, will the information from

12 this fuel switching tab get fed back and change

13 maybe the proportions of which homes now have gas

14 in them?

15 MR. FRANCO: I don't know if I can

16 answer that or not or --

17 MR. CYMBALSKY: Yeah, I don't -- so the

18 RECS sample -- can you just go back to the --

19 MR. FRANCO: I can answer but I don't

20 know if -- is it --

21 MR. CYMBALSKY: Well, you can't answer

22 the second part but just the first part. So go
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1 back to the building sample. So go down all the

2 way to the bottom of that table.

3 MR. FRANCO: A little bit further?

4 MR. CYMBALSKY: Just go to the end of

5 that table.

6 MR. FRANCO: Oh, oh the very end?

7 MR. CYMBALSKY: End of column H.

8 MR. FRANCO: Oh, okay. Yeah, I have

9 this thing here. It's kind of bugging me.

10 MR. CYMBALSKY: Let's do it end down

11 arrow key.

12 MR. FRANCO: Yeah.

13 MR. CYMBALSKY: It won't let you go down

14 to the bottom?

15 MR. FRANCO: Oh, I got it.

16 MR. CYMBALSKY: Well, there should be

17 9,000, or 12,000 or whatever. So this is

18 everything in the RECS sample. So there's RECS

19 had 12,083 sample. So we have the whole

20 population of the RECS database. So I don't know

21 what the gas -- I don't know.

22 MR. FRANCO: Well, this is -- what we're
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1 picking up as we're analyzing, as I think you

2 alluded to, are just the gas equipment.

3 Essentially the switching is if there's going to

4 be switching to electric. So that's already

5 picked up.

6 MR. CYMBALSKY: Right. So they already

7 have gas and when we apply the standard, the

8 methodology is will they switch. If they do, then

9 that gets fed in and the calculations run from

10 there.

11 MR. FRANCO: And I'll go actually a

12 little bit to that, how that works in the LCC and

13 then how that works in the NIA.

14 MR. CYMBALSKY: And logically, once you

15 switch to the condensing furnace -- well, once you

16 have a condensing furnace, you're locked in,

17 right? That's what you showed before.

18 MR. FRANCO: Yeah. Yeah, if you start

19 with the condensing furnace, obviously you're not

20 switching. It's only in the case when we assume

21 in 2021 that you start with a non-condensing and

22 then there's a potential of switching to something
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1 else.

2 MS. PETRILLO-GROH: Thank you.

3 MR. SCHROEDER: Can I ask a quick

4 question about the payback period, not about the

5 assumption behind it but we talked before about

6 the potential for breaking the model if you play

7 with things and change things. Is there any way

8 that changing that 3.5 year number breaks the

9 model?

10 MR. CYMBALSKY: So I think you can play

11 with the model and I think he showed you the

12 equation.

13 MR. FRANCO: So you can actually go

14 here. It's a fixed value. You can have this as

15 another number, a distribution, something else.

16 We actually have provided one for replacements,

17 one for new units. You can actually play with

18 that.

19 MR. CYMBALSKY: You can change it.

20 MR. FRANCO: The last thing then just to

21 kind of bring everything together in terms of fuel

22 switching is so this is the export parameter.
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1 Let's say that this household does switch and you

2 are actually going to something else. What would

3 happen is you go to the LCC payback calcs.

4 When we were discussing this, this is

5 again going back to this sheet that calculates the

6 LCC and so something that's very specific to this

7 rulemaking and furnaces is that we're actually

8 calculating the impacts in terms of the LCC. And

9 so, what happens is that if the household does

10 switch, then instead of picking up the 90 percent

11 AFUE as is the case of this household, it will

12 actually pick up what the energy use and economics

13 are of the switched equipment and then that's the

14 final results of what's being done. In this case,

15 it's just being 90 percent.

16 So there's no switching. But if there

17 was, then it would be looking at the values down

18 here and picking those up to have the appropriate

19 economic characteristics. And when you're looking

20 at the summary results, these summary results

21 include those potential switching.

22 So the end result, we added this summary
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1 switching tab. The summary switching tab has some

2 statistics to be able to see what's more or less

3 going on, what's switching to what, to kind of

4 understand what's going on. Obviously it's a

5 little bit -- a lot of information. But similar

6 to what we were talking about, you can kind of

7 see, well, the household started with certain

8 fractions and then you have a difference. And you

9 can kind of see the overall switching per

10 fractions. And so, that'll give you a little bit

11 of an understanding of what's going on.

12 MR. SCHROEDER: Sorry. This is Dave

13 Schroeder. Every house gets run for every

14 different efficiency level, right. And then

15 switching may or may not happen depending on which

16 efficiency level you're at.

17 MR. FRANCO: Correct.

18 MR. SCHROEDER: But then there seems to

19 be only one percent switching. So how does that -

20 - how does that work? Is that -- is that at one

21 set efficiency level?

22 MR. FRANCO: That's a very good
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1 question. So obviously this is a lot of data just

2 -- and we just -- we were trying to have something

3 here for stakeholders to see. This is just for 90

4 percent AFUE. You can actually have the same

5 table for 92, 95, 98. But then, the number of

6 forecast cells that you have to create to do that

7 balloons to a lot and we just decided to pick one.

8 But you could actually -- when you do -- when we

9 do the run, extract, you can actually look at all

10 the different combinations. And that's an easier

11 way to do it because once you're doing the

12 forecast, you keep increasing the number of

13 forecast cells very quickly to do that.

14 But hopefully this kind of illustrates

15 what's going on. There's actually a table down

16 here that kind of shows you what's going on by

17 efficiency. And you can see that it varies. So

18 for example, for 98, which would be TSL 5, you can

19 kind of see that there's more switching because

20 you have higher equipment costs versus the other

21 two. So you can kind of get a sense that yeah, it

22 does vary. But all other information provided is
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1 for 90 percent AFUE.

2 So let's now connect. Let's see how

3 much time we have. We have a little bit less than

4 an hour. So hopefully we can do NIA.

5 MR. CYMBALSKY: Yeah. So before we go,

6 I have a few questions that have come in.

7 MR. FRANCO: Sure.

8 MR. CYMBALSKY: So let's answer them

9 now. So we had a bunch of questions. I'm not

10 going to read all of them. But they're all pretty

11 much the same question. They asked about the data

12 sources for the energy price forecasts, do they

13 change, et cetera, et cetera.

14 So the energy price forecasts are from

15 the EIA most recent NEMS EIA annual energy

16 outlook. We basically start with our data set and

17 based on how those census division prices change,

18 we change the marginal prices and the average

19 prices, et cetera.

20 And then, we had a question about could

21 you remind us what year the product pricing is

22 from. So the analysis starts in 2021 and that's -
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1 - it's the forecasted price for the year 2021.

2 Dose the shipment data include imports or just

3 domestic? Yes, it has everything.

4 And then another question about why the

5 rebuttable presumption showed different results on

6 the summary sheet. It's a different -- a little

7 bit different analysis. I can't go more deeper

8 than that, but that's why they're different.

9 MR. FRANCO: Yeah, there's obviously

10 some sheets we didn't go to. But this is the

11 rebuttable payback sheet. Obviously that's --

12 MR. CYMBALSKY: Yeah, that's just a

13 different calculation, so.

14 MR. STANONIK: John, quick -- Frank

15 Stanonik, AHRI. A quick question. So when you're

16 looking at price forecasts, again, for any

17 analysis you're doing for rulemaking, do you do

18 any kind of a sensitivity analysis to say, well,

19 what if -- what if our forecast really changes by

20 x or y or --

21 MR. CYMBALSKY: Yes.

22 MR. STANONIK: You do? Okay.
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1 MR. CYMBALSKY: Okay. NIA, we have 45

2 minutes and so take it away.

3 MR. FRANCO: Great. Thank you. So now

4 let's go from -- transition from LCC to NIA. So

5 this is the sheet in the LCC that's kind of the --

6 one of the more important parameters for the NIA.

7 This is the LCC outputs. So we actually see a

8 copy of this in the LCC spreadsheet, and this will

9 be the some of the major economic parameters for

10 the LCC, for the NIA spreadsheet.

11 Let's just briefly go and see how this

12 is divided up. We divided it -- so sometimes in

13 some of these spreadsheets, it's just the national

14 results and those are what's being used as the

15 average results for national. Here we actually

16 divided them up into residential by region, south

17 or north, and replacement and new construction.

18 And so, the shipments that are new construction,

19 residential south have those inputs and the ones

20 that are different have different inputs and we're

21 also looking at commercial in the same new

22 construction, replacement in the same regions,
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1 south, north. Sometimes we label south as rest of

2 country and we have south but it really means rest

3 of country.

4 The inputs as we'll see are fuel use,

5 electricity use, so the energy use, the retail

6 price and installation cost, maintenance repair,

7 which is an annual value and the distribution. So

8 this is a base case distribution. So what this is

9 saying is that 78 percent of shipments in the

10 south are non-condensing for replacements and then

11 you go and you'll have a different value.

12 So that's looking at it as if there was

13 no switching. So those are the inputs as we're

14 doing switching. Now, if we want to look at the

15 switching input, those will -- there's actually

16 some -- before going to switching, there's a

17 couple other inputs that we actually -- that are

18 from the LCC. So the LCC calculates energy

19 prices. The energy prices is in 2012. It's the

20 average for the same regions. It's the south and

21 then whether it's new construction or replacement.

22 And these would be the values. So again, these
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1 are the average for those households.

2 Those are the inputs. Then we have

3 standby. I won't talk too much about that. And

4 then we have these what we're calling the

5 switching lookup tables. So this is what is the

6 impact of switching. This is kind of what's going

7 on to just the households that switch.

8 So let me go a little in detail about

9 what's happening here. So this is kind of the

10 differential of the what's going on between you

11 didn't switch and switched. So first level,

12 efficiency level zero means that it's just a non13 condensing. So there's no switching there. So

14 that's why we have zero impact throughout and

15 there's no -- there's a hundred percent. There's

16 no switching to any of this other equipment, so

17 zero, zero, zero, zero.

18 Now, if you go to the next one, let's

19 say we're going to 90 percent standard. So I

20 forgot to mention this is only looking at

21 households that are at 80 percent. So out of the

22 households that are starting at 80 percent in
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1 2021, 80 percent in replacements in the south

2 residential will not switch, 80 percent of those,

3 80.7. Those, the actual differential energy use

4 is this. They actually consume a little bit more

5 on average, a little bit more electricity. The

6 retail prices are a little bit -- installation and

7 repair retail prices are a little bit different

8 and the repair and maintenance a little bit

9 different. So these are the differentials that

10 occur.

11 Now, 5.8 percent actually switched to an

12 electric furnace and this is the impact of that.

13 So they consume 16 million Btus less. The

14 electricity use goes up obviously, or 4,477 and

15 then this is a retail and installation cost

16 differential and as well as a maintenance repair

17 differential. Now, let's go to the next one.

18 Heat pump, in this case it's 13.5, the same

19 situation for heat pump is more efficient. So you

20 see less electricity use. And then, we also

21 account for the difference between when you're

22 going from a gas water heater to an electric water
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1 heater. Let me just before going to that, so just

2 to emphasize this, 5.8 plus 13.5 plus 80.7 adds up

3 to 100 percent. So that's just a heating switching

4 fraction.

5 Now, when we're going to the water

6 heating, this is like a separate piece of it, 2.5

7 percent of households switch their water heaters

8 because of the standard in this case and these

9 would be the impacts. So again, they're obviously

10 consuming less fuel use and so gas or LPG and then

11 they're going to use a lot more electricity. So

12 and we repeat that for north, south, new

13 construction, replacements and those are fed into

14 the NIA.

15 So now, let's open up the NIA

16 spreadsheet and I'll -- let's see. I'll close

17 this. So I won't save it and I'll close it.

18 MR. M. HUNT: This is Marshall Hunt. So

19 that's total different spreadsheet?

20 MR. FRANCO: Yes, yes. Total different

21 spreadsheet. So if you go to the DOE website, the

22 LCC is one spreadsheet that you can download and
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1 then this is the NIA spreadsheet. So let me open

2 the NIA spreadsheet. And again, to emphasize, the

3 LCC spreadsheet is where you need Crystal Ball and

4 you're looking at these distributions. The NIA

5 spreadsheet, you don't. There's just this macro

6 that you have to run to be able to get the final

7 results. But you could just use Excel. There's

8 no separate add-on program.

9 Let's go starting with the structure.

10 So very similar to what we saw before with LCC.

11 We have the results here. The results are labeled

12 based on the different scenarios. These are

13 called trial standard labels here, the labeling

14 here and then there's different scenarios. And

15 the results are kind of the overall results but

16 then you can look at the regional results. So for

17 example, north and south results and if you add

18 those two up, you'll get the national results.

19 There's two sets of results. There's

20 the national energy savings and the net present

21 value. These are looking at outputs for when

22 you're running the macro and these are specific
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1 worksheets that are just pasted values.

2 MR. SCHROEDER: How do you get the data

3 form the LCC spreadsheet into this spreadsheet,

4 because it needs the output from that, right?

5 MR. FRANCO: Yeah, and I'll go through

6 that and that's a very important part, yes. Yes.

7 And if you want, so that's a very important point

8 and that's why I wanted to focus on the LCC

9 output. So for example, let's say something that

10 you really have to do if you want to change

11 something on the LCC and then see the impact in

12 the NIA, you would need to go through that process

13 of transferring those results into the NIA and

14 we'll go through that.

15 So again, we went through this a little

16 bit. There's some analysis parameters and those

17 are kind of fixed. Those are kind of set up based

18 on how DOE does its analysis and those are not

19 things that you should try to change. The things

20 that you can change pretty easily are the

21 scenarios. So you can change the economic growth

22 assumptions from AEO which changes the shipments
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1 and energy price trends and you can also change

2 the equipment price trends, which there was a

3 question about that. You can go to different

4 scenarios and you can change whether it has fuel

5 switching or not.

6 Those are the basic parameters that are

7 very easy to change. Now, something that as you

8 go through you can change other things. So for

9 example, the LCC output and we'll try to go

10 through that. And here are some of the

11 instructions to run. But basically, if you wanted

12 to generate new results, you would click here. It

13 goes through the process of going through each

14 region and it generates the final results. It

15 takes about -- and for example, about two to three

16 minutes. So it's much easier than the LCC. The

17 LCC takes a lot longer. So let's go and see. I

18 want to check how much time. Okay, we're perfect.

19 So since this is a few hours ago, a

20 reminder, there is a lot of different worksheets.

21 But the two primary worksheets are the non22 weatherized gas furnace one, this one labeled non
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1 -- NWGF and the mobile home. So gas furnace.

2 So these are the two main sheets that

3 everything is being calculated in, the shipments,

4 the inputs by year and the actual fuel switching

5 impacts. And those are the ones that we're going

6 to be spending more time on. But there are

7 individual sheets that there's some data that's

8 being picked up. So one of them is the LCC

9 output. Yes?

10 MR. ADROIT: Henri Adroit with Goodman.

11 I'm just having -- I'm receiving an error message,

12 a runtime error 1004. Can you explain that? It

13 says method range of objet global failed and it

14 gives me an option of either saying end or debug.

15 MR. FRANCO: We could look at that maybe

16 afterward. I'd need to look at it.

17 MR. ADROIT: Okay, thanks.

18 MR. FRANCO: So what we'll -- to answer

19 your question, the specific question about the LCC

20 output, now is a good time. So right now, it's

21 set up as being something that's manual, at least

22 inputted. But basically if you remember the LCC,
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1 this is the same setup. It's set up like the

2 south residential. These are the inputs. And

3 basically what you would need to do is you would

4 need to copy and paste those here in this gray

5 area.

6 So something important to note, you

7 would need to copy it here. And if you look,

8 these are fixed values. So you would just copy

9 the fixed values. The values up here are the

10 values that are being looked up for the specific

11 region and residential. So if you notice here, it

12 says north residential and it's picking up

13 residential results.

14 MR. LESLIE: This is in the I don't know

15 what to do about -- this is Neil Leslie, GTI. My

16 version of this same spreadsheet has number ref

17 all over the place in the rows 11 through 19

18 rather than the values you are showing in there.

19 Is there a way to get that taken care of?

20 MR. FRANCO: Of course, yes. I mean, it

21 might be that the published version, there was

22 accidentally one worksheet that was deleted which
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1 is the one that calculates. So this one we'll

2 publish right away. Yeah, so you can have that

3 version. Sorry about that, yeah. We thought we'd

4 corrected that but we'll correct that right away.

5 So that's important because you don't

6 want to paste the values directly in here unless

7 you wanted to do like a national average. Then

8 you would paste it in. You would paste those

9 vales below this gray area and you have to do it

10 appropriate, commercial, south, north,

11 replacement, new construction. And the other

12 parameters up here that you want to take care of

13 is these energy prices. Energy prices would

14 belong here that are coming from the LCC. So sadly

15 you don't have the instructions to do that. They

16 would be published together with some kind of

17 rulemaking. But yeah, you don't have them in the

18 spreadsheet. That might be something to add.

19 Then if you're interested in standby,

20 the standby results would be here. Now, the

21 switching table results are in a different

22 worksheet which is this one. And it's the same
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1 situation. You have these are the lookup values

2 that are specific and then below are the actual

3 values you want to copy and paste. You want to

4 make sure one thing is that you don't have

5 anything that looks strange. So anything that's

6 like a line or it produces an error. You don't

7 want to have it in here because when it does the

8 macro and it has like N/A here, it doesn't know

9 what to do with it. So you want to just take

10 those out when you're copying and pasting into

11 here because those are -- you're going from the

12 LCC and you're putting it into here. So hopefully

13 that explains how you would manage going from the

14 LCC to the NIA, the inputs.

15 Let me just briefly go to the other

16 inputs before going to the calculations. Almost

17 all the other worksheets are kind of trends that

18 are being occurred. So in the LCC, we're just

19 looking at everything that's occurring in 2021.

20 But obviously the shipments are for 2021, 2022,

21 '23. So the trends are basically from the inputs

22 that we're getting from the LCC. That would be
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1 our base year, and then how that varies for the

2 shipments in 2022, how that varies for 2023.

3 In this case, we're looking at the

4 equipment price trend. Now, obviously if you

5 change it to some other scenario, you'll get

6 different indices. Now, let's look at for example

7 the next one would be energy use trend. So the

8 2021 is your base year.

9 Then you have a different trend. Let's

10 say for example the fraction of condensing market

11 share. 2021 would be the base year which would be

12 these values and then you have some different

13 fraction in the subsequent years.

14 MR. LESLIE: Just as a general on this

15 particular spreadsheet, since we have not yet been

16 able to -- Neil Leslie, sorry. There are similar

17 issues with the number referencing around. I'm

18 hoping and assuming that when you put this up soon

19 that those e will have been taken care of along

20 with the way that we can actually use it, right?

21 MR. FRANCO: Of course, yes, yes.

22 MR. LESLIE: So we can really do that.
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1 Thank you.

2 MR. FRANCO: Yes, yes, yes. Of course,

3 very sorry about that.

4 MR. LESLIE: We did not want to bother

5 you with that right now. Our main focus was on

6 the LCC spreadsheet. But wanted to deal with that

7 at this discussion. Thank you.

8 MR. FRANCO: Thank you.

9 MR. M. HUNT: This is Marshall Hunt,

10 PG&E. IS there a particular person who we should

11 contact with these kinds of spreadsheet issues?

12 It might help to have -- okay, thank you, John --

13 a clearinghouse person. Thank you, John.

14 MR. FRANCO: Thank you. So let me just

15 go through a couple of these and then we'll just

16 focus on the NIA. So again, fuel prices or energy

17 prices. Again, there's a trend. So 2021, there's

18 a certain price and then there's different values

19 and that trend is being applied. Let me see.

20 There is a forecast of housing starts.

21 That's -- well, this is more in the realm of

22 shipments. So now we're getting to shipments. So
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1 in terms of shipments, there's some historical

2 data and there's other inputs. So as we go into

3 shipments, then these are the inputs. Lifetime in

4 terms of the retirement, those would be a

5 different set of assumptions here.

6 The end results of everything that's

7 going on are in some of these worksheets. So

8 we're not going to talk about these. But if you

9 clicked on here, these would be kind of the end

10 results. So if you're looking at the summary of

11 shipments of everything, maybe you would go into

12 here.

13 You would -- if you're looking at

14 something else, you might go into some of these

15 intermediate worksheets. So for example, some of

16 these actually produce the results that are being

17 shown in the summary. These are just like

18 outputted from the macro and these will change as

19 you change the spreadsheet assumptions. And these

20 -- some of these are populating in the NIA

21 summary.

22 So as you can see here, this one is
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1 using intermediate for NIA plus. So if you wanted

2 to understand where those values are coming from,

3 those are coming from there.

4 So that kind of summarizes how this

5 different worksheets. Now let's get into the

6 whole spreadsheet. So this is a little bit -- a

7 lot of information but hopefully we'll structure

8 it so it's easy to understand where things are

9 coming from. So the first part is we have inputs,

10 different inputs that are coming from different

11 parts. The first part of the inputs are more

12 related to the economic and energy use.

13 So for example, this is just looking up

14 at 2021 for the northern region. So again, this

15 is divided by region, north and for the sector

16 residential. What would be the inputs in 2021?

17 So efficiency level one, 80 percent efficiency.

18 This is the annual fuel use, the electricity use,

19 the retail price, installation cost, repair cost

20 and then this is the base case efficiency. So

21 that's just for 2021. And after that, you're

22 projecting, after that period.
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1 As part of the shipments analysis, you

2 have some imported values. And what this gives us

3 is mainly the fraction of demolitions, so houses

4 that are not being replaced. So that kind of

5 decreases the number of replacements and number of

6 households that potentially could be new owners.

7 So this is the primary purpose of the calculations

8 going on in here.

9 So let's go into the main calculations

10 sheet. So starting column T all the way to, well,

11 there's a couple of -- let's see, column T to I'd

12 say DD, this column right here. All the

13 calculations that are here are related to the base

14 case. So the current -- if there's no minimum

15 standard. So the shipment, all the inputs, so

16 that's like your baseline, you're calculating the

17 base case.

18 The next set of calculations are related

19 to the standard cases. And when you're looking at

20 the spreadsheet, you have to understand that it's

21 only looking at one of the standards that you're

22 looking. So you'd have to change -- if you wanted
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1 to look at a specific standard case, you would

2 have to change. For example, instead of 90

3 percent, you want to see three results for 92 for

4 the north, replacement. You'd have to change the

5 variables to see that. In this case, it's just

6 looking at 90 percent.

7 And these are all the -- and we'll go

8 through a little bit what these columns mean. So

9 that's how that's divided up. Now, at the very

10 end, there's a standby which is a different area

11 of this. And then there's the equipment switching.

12 And so, the equipment switching, kind of the new

13 shipments that are going to electric equipment or

14 electric wiring equipment are being calculated

15 here and the impacts and ten they're being brought

16 back into the main calculation. So that's kind of

17 the three areas.

18 So in terms of if you wanted to change

19 some of the assumptions, it might not be -- you

20 would probably want to change the assumption, for

21 example, on the LCC outputs, do different outputs

22 you want to change some of the trends in the other
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1 sheets. Here, it's more the actual calculation.

2 So it's I'll go through it but it might be a

3 little bit more difficult to change the

4 assumptions here. But let's walk through it a

5 little bit.

6 So again, this is just for -- I believe

7 this is north residential. We're only looking at

8 north residential. So we go through and

9 calculate. First we look at the historical

10 shipments. And for that area of the country,

11 these are the actual historical shipments that are

12 being picked up in that worksheet, historical

13 shipment for the north residential. Then at a

14 certain point after 2013, that's the last year we

15 have historical shipments, we start projecting our

16 shipments. These are the projections. So by 2021,

17 we're projecting that 1.659 million shipments

18 would go to the north residential.

19 How is that constructed? So it's very

20 similar for all rulemakings. Usually there are at

21 least two components, but sometimes three to the

22 shipments model. The main components are the new
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1 construction market and the replacements. But

2 then you have either new owners or early

3 replacements. You have some other things that are

4 going on in the market. So we actually for this

5 analysis we have three. And basically we have new

6 construction which is labeled new units. We have

7 new owners or our understanding what new owners,

8 the difference, and the replacements. And those

9 three inputs if you add these three up, will give

10 you 1.659.

11 So each one of those has different set

12 of assumptions. Let's briefly go into where some

13 of this data comes from. So basically new units

14 basically it's how many housing -- how much new

15 housing is occurring in a country and how often

16 that's a gas furnace, for example. And so, it's

17 basically that situation. Let's say for example

18 in the north it's 60 percent of new construction

19 is gas furnaces, times the number of new

20 construction. So that gives you this number, 352.

21 So that's basically what's occurring there.

22 Next part is replacements. Basically
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1 replacements, we have remember the lifetime. From

2 the lifetime you can get kind of a retirement

3 factor because our lifetime is based on

4 distribution. So then, that gives us a sense of

5 how many units are being replaced. Now, in here

6 we also take into account households that are

7 being demolished. So there's some households that

8 are being replaced but there's also some

9 households that are being demolished and no longer

10 replaced. They're something that's occurring in

11 the market.

12 So this gives -- this takes that into

13 account as well. And then based on the

14 information that's available, we make what

15 fraction of new owners are in the market and that

16 -- the total of these three is what gives us again

17 the 1.659. Now, if you kind of go merge

18 replacements and new owners, this is the fraction

19 in the north. This would be the new units for

20 that specific year, 2021. This is again the north

21 replacements. This is actually accounting for the

22 stock that's being installed and how the stock
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1 increases. It's just for visual purposes. The

2 main result is the shipments here, 1.659.

3 So now based on that let's just go into

4 the energy use and the other inputs, the equipment

5 cost, the installation cost and how that's being

6 used. So basically, the energy use components are

7 divided up into replacements, new units and those

8 inputs again are coming up from here. So let's go

9 back here. It's probably easier to go here and

10 see.

11 So what we're saying is that these

12 inputs, 33 percent of the time or 33 percent of

13 shipments in 2021 are 54 million Btus, 6 percent

14 are at 48 because they're already 98 condensing,

15 16, 47, 45. So if you sum product, this and this,

16 that gives you the weighted -- shipment-weighted

17 energy use for 2021 and that's your base case. So

18 once we talk about standard case, obviously you're

19 not going to have this first row and then

20 everything either ships or rolls up or something

21 happens to it and you could have different

22 distribution of your shipments.
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1 After 2021, then we get into -- well,

2 maybe the energy use changes or something happens.

3 So then there's a trend that's being multiplied.

4 So 2021 is just based on LCC but then after that

5 there might be some other inputs and then the

6 energy use changes after that period. And you can

7 kind of see that there's some changes here and let

8 me just point out two of those changes.

9 So one of them, the primary one is that

10 there is a base case efficiency transfer. So

11 there's a trend of how many households are

12 condensing furnace. So originally, maybe this is

13 60 percent of the furnaces are at 90 percent or

14 above. But then the next year it'll keep growing.

15 So historically there's been the trend going

16 towards condensing. So we follow that trend.

17 Then there are also efficiency trends. So for

18 example, the building shell or how households are

19 built is they're built better, more robustly and

20 those are also an input and that also decreases

21 the energy use over time. So as you can see, the

22 energy use even at the base case decreases over
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1 time. So that's that part.

2 Now, let's go to the last few

3 components, the equipment cost and installation

4 just as this is both. The same thing happens

5 here. We add up the installation and repair. We

6 multiply it times the fraction that's occurring.

7 There's only a minor detail here is you're

8 multiplying this times the base case, this times

9 the base case and adding up together. But for the

10 retail price, there's a trend. So for that, for

11 only the retail price, there's in the case - - in

12 the reference case here there's a decreasing trend

13 in the price. So that's the only difference. And

14 assuming that installation cost is fixed.

15 So now let's go to maintenance and

16 repair. The same thing occurs and as we discussed,

17 you're multiplying. So it's shipment-weighted.

18 The only difference here, as you can see, there's

19 a difference in the price, is based -- this is

20 only based on because there's going to be more

21 condensing fraction. So we're almost done. So

22 now, what we want to do is kind of aggregate all
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1 that and get the operating cost and get the total

2 installed cost to calculate either the national

3 energy savings or the net present value.

4 So calculate the national energy

5 savings, obviously we only need the energy use.

6 And so, we kind of tally up the energy use which

7 is essentially the per unit energy use that we

8 were discussing before, this, either the fuel or

9 the electricity times the shipment. So basically

10 if it's new, for example, here we're multiplying

11 the 343 number times the million units and then

12 the fuel, the same or to the replacement new

13 owners. And that will give us these final

14 numbers which are the -- so that's why this is in

15 quads. Oh no, I went too far. Sorry.

16 Let's see. There we go. So this is

17 still base case. So these are the quads that are

18 for the energy use. Then the equipment costs are

19 the same. So this is again retail and

20 installation, total installed cost. Those per

21 unit times the number of shipments. So this deals

22 with the NES. Now, when we get to the NPV, we
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1 also want to calculate the operating cost. So the

2 operating cost, obviously you have to multiply the

3 energy use, these totals, times the fuel price or

4 electricity price. And you pick up the

5 appropriate values. You multiply them and you get

6 these results. And that basically those are the

7 final results to if you add these all up.

8 So this is 2021. You go all the way

9 down. If you're only looking at equipment costs,

10 it only goes to 2050 because you're only

11 purchasing up to 2050. But then if you're looking

12 at operating or energy use, you have to wait until

13 the end of the last unit and so then that goes

14 beyond that. And that kind of summarizes the base

15 case. But what you want to do really is compare

16 your base case to your standards case.

17 So before there's switching, before

18 taking into account switching, here the standards

19 case, the shipments are the same. So this is

20 without asking into account switching. So if you

21 compare the shipments here to the shipment before,

22 they're exactly the same. And the inputs are
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1 dependent on what you're looking up in terms of

2 the standard. So your starting point will be

3 different. So instead of taking into account this

4 line here, if you're looking at the standard level

5 two here, 90 percent, then you're only multiplying

6 these and these and then depending on what

7 product, it might be that it's the 33 is rolled up

8 or there's kind of a shift that's occurring.

9 And so that very product specific even

10 energy level if there might be some other

11 assumptions that are going on there. And then,

12 that's how those per unit costs are being

13 generated. The same thing occurs then when you're

14 trying to account for the national energy use

15 which would be these and then the equipment cost.

16 Then so because we're looking -- we want

17 to do the savings. So that's the interesting --

18 that's where the interesting aspect of looking at

19 this is looking at comparing the base case to the

20 standards case. And this is where it's basically a

21 differential between the standards case results

22 and the base case results. This is where this is
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1 being calculated and you get energy savings or

2 operating costs. You add those all up to 30

3 years. There's some discounting based on whether

4 you're using a 3 percent discount or a 7 percent

5 discount rate. That is also accounted for.

6 The macro -- one of the other things

7 that the macro does is other than just doing this

8 north, south and replacement/non-replacement,

9 commercial/residential, it also takes into account

10 whether it's 7 percent or 3 percent discount rate.

11 So it does all that in that macro.

12 MR. M. HUNT: Victor, this is Marshall

13 Hunt. We selected the standards case way back on

14 column FM I think. Is that true?

15 MR. FRANCO: Yes. YES, yes. So this is

16 T is base case, starting the base case inputs.

17 MR. M. HUNT: Okay, T.

18 MR. FRANCO: And then the actual kind of

19 results are being kind of the aggregated results

20 of all shipments are this part.

21 MR. M. HUNT: FM-4 and it's appeared

22 other places. That's a scenario we're looking at,
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1 right? That's the efficiency level?

2 MR. FRANCO: This is FM-4. Oh, don't

3 actually know what that means. I don't know.

4 That's just a one.

5 MR. M. HUNT: So but you have to run

6 different runs to select the different test

7 standards levels, correct?

8 MR. FRANCO: Yes, correct. Correct.

9 MR. M. HUNT: And that's selected where?

10 Is that way over?

11 MR. FRANCO: It's actually selected in a

12 different sheet.

13 MR. M. HUNT: Oh, okay.

14 MR. FRANCO: Yeah, and we can go there.

15 Just one second.

16 MR. M. HUNT: Way over there.

17 MR. FRANCO: Yeah. So we'll get to that

18 to change. Yeah, no that's important because if

19 you wanted to look at different assumptions

20 obviously. So that's kind of that's how that's

21 set up. And this is what the output of the

22 results that you're getting at the very end. So
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1 if you wanted to look at for that specific and

2 what's going on, how this adds, disaggregating

3 electricity and gas and looking at the different

4 details, this would be the place.

5 Now, this also accounts for fuel

6 switching. So now what we'll talk about the fuel

7 switching component and now it interacts in here.

8 so if there - - if you select that you're doing

9 fuel switching, which right now it's defaulted to

10 fuel switching, then it will pick up those results

11 and then put them in there in the standards case.

12 So let's take a look at the fuel switching

13 component. Okay, so we're not going to be talking

14 about standby. But if you're interested in

15 standby, this is where the results would be

16 calculated.

17 Let's go to equipment switching. So

18 equipment switching is somewhat similar.

19 Essentially what you want to do is you want it to

20 calculate how many homes are getting impacted and

21 what would be kind of the differential impact. So

22 for the homes impacted in terms of non-
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1 weatherized, they would be here. So I'm just

2 going to kind of go through the structure first

3 and then it depends if it's a replacement or a

4 new. Then if it's electric furnaces, this would

5 be kind of the place that would play into. Heat

6 pump and then electric water heaters.

7 And as you can see, everything is

8 replacement, new, that's being calculated and the

9 aggregated results kind of are towards the end

10 where you have quads and thousand million watt11 hours and you have the equipment cost and

12 maintenance repair cost. And that's kind of --

13 this is the setup.

14 Okay, so we have five minutes. So we'll

15 go really quickly and hopefully I have more than a

16 couple minutes to any questions. So let's see how

17 to summarize this. Basically, there's a lot

18 obviously to talk about here, a lot of details.

19 So remember we were looking at what the fraction

20 would be that switched.

21 So this is the fraction that would be at

22 non-weatherized gas furnaces in 2021, 85 percent,
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1 73 percent of the replacements and new. So

2 remember we have the fractions are different

3 between new construction and replacements. And

4 so, those would be the ones that don't switch,

5 right? Those would go to non-weatherized gas

6 furnaces. Then if you go to electric furnaces,

7 then these are the fractions, 3.5, 7.5. Then if

8 you go to heat pump, these would be the fractions.

9 And again we're looking at replacement,

10 residential, residential north results. Okay, so

11 those would be the fraction of shipments that

12 would be impacted. Before that, the actual number

13 of shipments that are impacted depends on the

14 fraction of non- condensing. So the total number

15 of shipments and replacements is this number. The

16 total number of new is this number.

17 So if you recall, if you add these two

18 up, that's the total number of shipments. Then 33

19 percent of those are non-condensing. So those are

20 the ones that are impacted. So that's 520,000

21 replacements. And -- oh stop, let me correct that

22 error, and then 35 percent of this number would be
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1 the new construction. And of those, of the 35

2 percent times this, 85 percent or 73 percent of

3 those would go to non-weatherized gas furnaces.

4 So hopefully the accounting more or less you

5 understand more or less. So for example, let's go

6 here.

7 So if you multiply those fractions, 73

8 or 85 depending on replacement or new

9 construction, you get -- you end up with this

10 number for replacements or this number for new

11 construction. So those are the ones that are

12 switching to non-weatherized gas furnaces and the

13 ones that aren't impacted. And then, you have the

14 same for electric and the same for heat pump. So

15 11 percent times all those fractions and you get

16 these are the actual impacted replacements and

17 impacted new construction.

18 The rest is similar to before.

19 It's just looking at the switching, the switching

20 impacts, how much as. So for example, because

21 you're decreasing your gas use by switching,

22 there's a negative impact. And because you're
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1 increasing your electricity usage, increased

2 electricity use. That then is inputted into the

3 standards case cost and is a part of the energy

4 savings or equipment and operating costs. And I

5 think that's about it. There's more details but

6 I'm kind of --

7 MR. CYMBALSKY: Why don't you show

8 Marshall where you have to specify what standard

9 level?

10 MR. FRANCO: Oh, yes, yes. That's it.

11 MR. CYMBALSKY: Because I think that's

12 important for everybody.

13 MR. FRANCO: That is very important,

14 yeah. So there's this sheet at the end that's

15 called summary of results. And here there is

16 what's called TSL. So there's going to be two

17 things that you need to consider when you're doing

18 this. So usually what DOE does is you have the

19 trial standard levels and you have different

20 efficiencies that are associated.

21 So you go to summary results. If you

22 want to change this manually, you can. There's no
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1 problem with doing that. So let's say we want to

2 look at TSL 1 and we change it there. The results

3 will change. Here's some kind of like preliminary

4 results that are just the addition of the numbers

5 and you can actually see --

6 MR. M. HUNT: That's what's left?

7 MR. FRANCO: Yeah.

8 MR. M. HUNT: And you run them one at a

9 time?

10 MR. FRANCO: If you wanted to go one at

11 a time. Yeah, yeah, you could just go and then

12 two and see how the results here are going to be

13 changing depending on the region? Right now we're

14 in the north. So it depends on there it's going

15 to be changing the impact.

16 UNIDENTIFIED MALE: (Off mic.)

17 MR. M. HUNT: Magic physics.

18 UNIDENTIFIED MALE: A hundred and ten

19 percent efficiency.

20 MR. FRANCO: Now, if you wanted to

21 change -- you wanted to look residential

22 commercial, you would change it here in this other
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1 one which is called labels. And I just also

2 wanted to point in the labels, you can see the

3 mapping of the trial standard levels to the

4 efficiency levels. So for example, what does TSL

5 1 mean? So then here you would see that TSL 1

6 means this, efficiency level one, efficiency level

7 one or two and three and two because this is in

8 the northern region.

9 And it might change between the north

10 and the south because you could have regional

11 standards as well. So this is where you would go

12 and maybe change -- maybe you want to have a

13 different combination. This is where you could

14 change that.

15 MR. CYMBALSKY: Okay, so this is where

16 you can cook up your own standard. Okay, so it's

17 12:58 and we have a hard stop unfortunately. And

18 so I just want to end here and thank everybody for

19 coming. We do have a parting gift for all of you

20 who've stayed. On your way out, there'll be a

21 shopping basket full of printouts from the

22 meeting. It's mostly screenshots of what Victor
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1 went through.

2 But I know some people would prefer to

3 have a hard copy. We didn't want to hand them out

4 at the meeting because I really think it's better

5 that you focus on what he's talking about and

6 where he's talking about it as opposed to flipping

7 through sheets. But please pick one up on the way

8 out. Again, if there is a follow-on that, you

9 know, we need to come back and do some more of

10 this, we're open to that. So feel free to

11 request.

12 As for the questions, we mentioned that

13 the questions that were sent in prior to the

14 meeting. I went through the list and I'm pretty

15 sure we answered all of those that we could as we

16 went through the meeting. So if you feel that

17 there were questions that weren't answered, you

18 know, just get back to me and we'll see how we'll

19 handle that. But I think I just went through the

20 list of them and I think the ones that we were

21 able to answer, Victor went through in his work

22 and kind of pointed them out. So --
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1 MR. MILLER: This is Bud miller and

2 speaking I think now for APGA and AGA, we

3 appreciate the tutorial. This is not really the

4 meeting we had asked for or planned for or

5 anticipated and unfortunately many of the

6 questions that we submitted have not been

7 answered. But it's my understanding, and correct

8 me if I'm wrong, that when the proposed rule does

9 issue, we'll get the answers to those questions

10 that we feel have not been answered today and

11 there are many of those frankly.

12 MR. CYMBALSKY: That's right. And so, I

13 think, you know, there are 20-some odd questions

14 and I think only maybe half a dozen or a little

15 more than that we consider that we answered. So

16 yes, at the NOPR meeting would be the more

17 opportune time to ask the questions that were

18 specific to assumptions and the numbers that are

19 in the spreadsheet.

20 MR. MILLER: And would you anticipate

21 that would be held within what, x days after the

22 proposed rule issues? When would you anticipate
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1 that to be held?

2 MR. CYMBALSKY: Right. So for the NOPR,

3 there will be a 90-day comment period as agreed to

4 in a settlement. We will choose a day within the

5 90-day comment period, maybe in the middle. I

6 don't know if you have a preference. Do you want

7 it in the beginning or the end? That's --

8 MR. MILLER: I would think sooner rather

9 than later. I mean, there may be a need for more

10 than one meeting. But these basic questions that

11 we needed answered were to get as much

12 understanding of the spreadsheets as possible as

13 early as possible. So I think we would like the

14 initial meeting to be early in that comment period

15 and there may be a need for subsequent meetings

16 and we hope you're open to that as well.

17 MR. CYMBALSKY: Okay. Well, we'll think

18 about that. I mean, the caution there is you'll

19 need time to actually digest the NOPR. So I think

20 that's most stakeholders need, as I've heard from

21 everyone, lots of time to digest it.

22 So we hope that by putting out these
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1 analytical tools ahead of time we're giving you a

2 running start on what the data look like and also

3 we hope that this meeting was helpful in that you

4 can go back now and play with the spreadsheets and

5 have a better understating of how the analysis

6 works.

7 So that was the intent of this meeting.

8 I know people wanted more information about the

9 proposal. But that's simply just not possible for

10 DOE to do at this time. And again, at the NOPR

11 stage, we will be obviously entertaining those

12 types of questions. So thanks everyone for

13 coming, and I hope you did gain some knowledge of

14 the spreadsheets because that certainly was our

15 intent. Travel safe. Thanks.

16 (Whereupon, the foregoing adjourned.)

17

18

19

20

21

22
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1 CERTIFICATE OF NOTARY PUBLIC

2 I, CHAZ BENNETT, the officer before whom the

3 foregoing deposition was taken, do hereby certify

4 that the witness whose testimony appears in the

5 foregoing deposition was duly sworn by me; that

6 the testimony of said witness was recorded by me

7 and thereafter reduced to typewriting under my

8 direction; that said deposition is a true record

9 of the testimony given by said witness; that I am

10 neither counsel for, related to, nor employed by

11 any of the parties to the action in which this

12 deposition was taken; and, further, that I am not

13 a relative or employee of any counsel or attorney

14 employed by the parties hereto, nor financially or

15 otherwise interested in the outcome of this

16 action.

17

18

19 ____________________________

 CHAZ BENNETT

20 Notary Public in and for the

 Commonwealth of Virginia

21

22
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1 CERTIFICATE OF TRANSCRIPTION

2

3

4 I, BENJAMIN GRAHAM, hereby certify that I am not

5 the Court Reporter who reported the following

6 proceeding and that I have typed the transcript of

7 this proceeding using the Court Reporter's notes

8 and recordings. The foregoing/attached transcript

9 is a true, correct, and complete transcription of

10 said proceeding.

11

12

13

14

November 12, 2014 Benjamin Graham

15 Date Transcriptionist

16

17

18

19

20

21

22
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$

$1,000 59:22

146:15

$200 146:13

$400 51:17

$43 116:3

$50,000 60:5

$7.67 135:7

$8.99 60:4

$9.05 135:7

$900 89:4

0

0.2 146:10,16

0.5 84:21

0.937 84:6

0.94 82:9,12

1

1 194:2 195:5

1,900 62:8

1.075 136:8

1.659 178:17

179:10 180:17

181:2

1:00 12:19 112:21

10 81:10,11,16,18

82:2,3,4 98:5,11

115:14

10,000 27:8 29:3,6

38:15,19 44:7,22

50:18 53:7

54:1,5,10,12,15

57:17 58:12 59:9

60:14 66:20

85:15 95:7,14

99:10,13 113:18

115:5,8

100 164:3

1000 1:20

1004 168:12

11 5:4 169:17

192:15

11,000 62:21 63:1

11,732 63:2

110 122:22

12 201:14

12,000 124:22

152:17

12,083 152:19

12:58 195:17

13,000 120:7

13.5 163:18 164:2

134 83:14

135 19:7

139 117:18

14 89:9 90:1

15 81:12

150 5:9

16 103:15,16

163:13 181:15

19 5:7 169:17

2

2 118:21

2.5 164:6

20 75:8

2003 34:16,17

129:5

2005 129:6,8

2009 120:9 129:3,7

2012 134:8 136:2,4

161:19

2013 85:18,21

86:4,9,11 178:14

2014 1:17 41:21

201:14

2021 21:19 22:1

37:4 41:17 71:20

72:2 84:4

118:2,8 127:10

128:9,12,22

130:15 131:2

136:8 153:21

158:22 159:1

163:1 171:19,20

172:8,11 173:17

175:14,16,21

178:16 180:20

181:13,17

182:1,4 185:8

190:22

2022 171:20 172:2

2023 172:2

2050 37:4 41:19

185:10,11

20585 1:21

20-some 197:13

21 32:8,9 127:18

134:19

21.5 137:14,18

23 171:21

25 56:6 118:12

25,000 62:9

26 36:16

3

3 41:20 118:14

187:4,10

3.5 118:7 123:20

146:4,6 147:20

154:8 191:7

30 36:3,6,9,13 37:1

41:18 187:2

30-year 36:12

31 130:16

33 181:12 186:7

191:18

343 184:11

35 191:22 192:1

352 179:20

357 121:18 123:21

365 75:6 76:7

381 80:11

4

4,477 163:14

4.7 57:7

45 160:1 181:15

47 5:8 181:15

48 181:14

49279-01-EE-5B

1:15

4A-104 1:20

5

5 56:6 61:8

62:1,12,15,22

83:7 157:18

5,975 119:12,14

5.8 163:11 164:2

50 122:15,16 123:2

500 80:12,19

520,000 191:20

54 181:13

55 123:2,4

56 36:16 121:18
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590 149:18

5th 61:18,21

6

6 82:12 181:13

60 179:18 182:13

600 80:19

60-year 36:13

7

7 1:17 5:3 41:20

187:4,10

7.5 191:7

70 98:6

73 191:1 192:2,7

75 118:13

750.7 61:22

78 161:9

8

8,600 122:11,12

123:4

80 92:20 109:11

127:21 130:17

141:18

162:21,22

163:1,2 175:17

80.7 163:3 164:2

85 121:18 122:10

190:22 192:2,8

9

9,000 152:17

9.2 98:9

9:00 1:21

90 105:2,13,18,20

106:4 109:11,16

115:9 143:21

144:1,6 145:6,22

146:13 149:15

155:10,15 157:3

158:1 162:19

177:2,6 182:13

186:5

90-day 198:3,5

92 105:20 128:4

144:1,2 157:5

177:3

95 144:3 157:5

95th 61:17 62:6,8

97 118:14 127:19

98 144:3,5

157:5,18 181:14

99 76:12

A

a.m 1:21

AA 19:7

ability 144:5

able 28:10 46:17

47:17,22 59:19

66:7 67:10

74:1,12 79:15

80:7 81:2 99:17

119:19 156:2

165:6 172:16

196:21

above-referenced

1:19

absent 130:22

131:1

Absolutely 88:15

AC 90:17 137:8

access 18:5

accidentally

169:22

account 71:7,21

74:5 101:20

102:2 116:8

163:21 180:6,13

185:18,20

186:3,14 187:9

accounted 16:8

17:20 151:5,10

187:5

accounting 39:22

40:12 180:21

192:4

accounts 189:5

accurate 79:4

across 10:18

action 200:11,16

active 66:15

actual 30:9 35:14

54:2 57:6 64:11

69:7 71:14

114:1,6 117:6,21

119:6 120:3,17

124:21 132:6,16

163:3 168:4

171:2 178:1,11

187:18 191:12

192:16

actually 7:6 19:18

21:12,13

24:12,13

29:3,6,7

30:14,20 31:6

32:11,18,19 35:6

37:20 39:4,7

40:10,16,17

41:22 42:12

43:7,18 50:6

51:18 52:17 54:8

56:8 59:2,16

62:2,8 63:9

64:18

66:10,12,17,21,2

2 67:13,15

68:13,20,21 69:3

70:9 76:2,9 77:4

78:20 80:2

81:1,10,12

82:1,4 83:18

84:10,12,19,20

88:13 91:16 92:1

93:9

95:2,6,12,14,15

96:17 97:7

98:1,8,10

99:5,21

101:9,10,11,14

104:21 106:19

108:6,7

113:17,19

114:2,14

115:17,22 117:7

118:5

119:2,9,12,13

121:10

124:12,13,15,18

127:21 128:3,4

129:10,16

131:22 132:2

137:16,17

138:19 140:5

142:8 145:2

146:16,22 147:7

148:22 149:15

153:11

154:13,16,17

155:2,7,12

157:4,8,9,15

160:7,15

161:15,17

163:4,11 172:20

174:16 179:4

180:21 188:3,11

194:5 198:19

Adam 2:11 9:8

add 39:15 80:5

165:17 170:18
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179:9 183:5

185:7 187:2

191:17

added 155:22

add-in 24:14,20

adding 183:9

add-ins 37:20

addition 20:14

194:4

additional 28:4

37:20 45:21 60:7

81:2 93:13,17

109:22 113:7

131:8 141:4

add-on 39:12

165:8

address 18:9

adds 164:2 189:2

adjourned 199:16

adjusted 115:16

adjustment 136:14

adjustments

136:17

Adroit 3:16 7:19

51:6,7,12 73:16

168:10,17

advanced 79:14

advantage 149:6,7

AEO 42:6

136:4,11 166:22

afterward 168:16

AFUE 30:9 72:7

92:20 104:6

105:2 127:8,9,22

155:11 157:4

158:1

AGA 13:8 197:2

age 68:4,7 104:8

129:5,15 150:20

agenda 110:11

aggregate 183:22

aggregated 187:19

190:9

AGL 3:18 8:7

ago 13:10 167:19

agreed 198:3

ahead 100:6 199:1

AHRI 14:19 85:16

100:6 103:2

106:13 129:18

135:11 138:5

150:6 159:15

air 2:16 3:13 4:3,6

8:4,14 9:18 10:3

88:3 104:17

129:12 134:2

140:6,11

142:3,17 150:16

Alex 3:4 9:6

Alison 4:4 9:20

alive 131:11

Allied 4:6 8:13

allow 11:1

allows 149:3

alluded 153:2

already 14:15

38:20 49:9 83:4

105:7,9 106:10

129:7 153:4,6

181:14

alternative

112:4,13

am 6:5 89:12

200:9,12 201:4

amended 11:8,18

American 3:11,22

7:17

amount 86:6

analyses 10:21

138:6

analysis 10:21

19:12,19,20

20:4,6,14,15,20

21:3,6 24:3,4

27:7 35:9

36:7,8,14 37:2

41:11,18,19

51:15 81:9 85:3

89:22 95:20

96:14 97:13

102:1 119:1

139:15 158:22

159:7,17,18

166:16,18 176:1

179:5 199:5

analytic 10:17

analytical 6:4,9

11:2,5 12:12

16:3 18:4 199:1

analyzed 36:8

analyzing 37:7

71:22 103:17

153:1

and/or 13:2

Andrea 3:17 8:6

annual 21:10

37:11,12 57:22

133:19 144:10

158:15 161:7

175:18

annualized 64:12

answer 6:20 16:13

18:12,16 19:9

53:20 67:21

89:14,21 91:20

102:10 112:16

139:9 149:21

151:16,19,21

158:8 168:18

196:21

answered

196:15,17

197:7,10,15

198:11

answering 10:15

11:14

answers 13:21,22

112:8 197:9

anticipate

197:20,22

anticipated 197:5

anybody 14:17

17:2 19:9

anymore 112:16

anything 23:11

37:20 62:5 64:1

75:6 131:1

147:16 171:5

Anytime 19:4

anyway 110:16

apartment

101:8,13,17

APGA 9:17 13:7,8

197:2

appeared 187:21

appears 200:4

appliance 6:5

10:18 47:19

appliances 47:13

applicable 18:7

109:18

application 100:19
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applied 17:13

83:12 113:21

135:9,11,12,15

173:19

apply 153:7

appreciate 14:3,22

15:17,20 34:20

77:21 197:3

appreciates 13:8,9

approach 24:5

appropriate 13:7

16:6 18:21 34:12

48:4 79:22 80:16

83:10,16 155:18

170:10 185:5

area 41:9 45:22

95:20 113:11

169:5 170:9

177:10 178:10

areas 177:17

aren't 25:13 72:7

192:13

argument 46:19

arise 75:17

ARMSTRONG

2:2 53:20 63:4

78:3

arrow 152:11

articulated 13:17

Ashley 2:2 78:4

aspect 16:1 186:18

aspects 17:19 87:4

113:10

assign 89:2

assigned 105:5

121:13

assigning 91:22

associated 64:17

70:3 94:21 99:4

102:4 104:7

109:8,14 114:17

136:16 139:6

150:14 151:1

193:20

association 2:13

3:11,22 4:1

7:12,18 9:22

10:2 76:21

assume 15:8 89:22

90:1 92:2,4

94:2,6,9 100:7

101:3 104:6

107:18 141:5

148:17 153:20

assumed 96:19

assumes 146:20

assuming 13:15

17:22 73:21 84:4

87:16 101:5

102:13,14

132:12 172:18

183:14

assumption

67:14,15 72:2

74:12 138:1,2

154:5 177:20

assumptions 11:15

12:6 42:1

46:9,18,20 67:20

74:16,17 88:11

91:3 92:5 95:1

96:21 97:19

100:14,20

101:7,16 104:19

105:20 110:4

113:8,12 114:16

116:11,17

132:14 137:12

141:6 166:22

174:5,19 177:19

178:4 179:12

186:11 188:19

197:18

asterisk 28:17

Atlanta 8:7

attic 94:9,10 95:4

attorney 200:13

audio 7:3

available 11:6,21

22:11,13,18

30:22 39:20

94:16 133:1

180:14

Avenue 1:20

average 50:17,19

52:22

53:1,2,6,21

54:4,11 57:7

63:15 65:22 68:4

85:11 97:21

98:10 111:8

115:6,7,9

129:14,16 132:2

134:9,13,22

135:15 158:18

160:15 161:20

162:1 163:5

170:7

aware 18:14

away 37:21 104:22

147:15 160:2

170:2,4

B

background 43:4

75:8

backwards 81:3

ball 24:14,16

25:1,7,9 27:5

38:15 39:12,15

44:22 50:5 51:9

59:17,18,21 60:4

65:13

66:6,9,10,11

67:8,10 68:9,21

73:21 85:2,6

107:18 124:13

126:12 165:3

balloons 157:7

Barbecue 2:13

10:2

barbershop 97:10

base 21:18 37:13

43:8 47:14 49:1

127:8 135:15

161:8 172:1,8,11

175:20

176:13,17

181:17

182:10,22

183:8,9 184:17

185:14,16

186:19,22

187:16

based 53:1 81:14

93:21 96:9,10

97:12 98:6

100:18 111:21

112:12 113:12

117:21 118:3

127:17 129:9

145:5 146:2,4

158:17 165:12

166:17 180:3,13

181:3 182:4

183:19,20 187:3

baseline 83:15

121:19 130:19

176:16

basement 93:11,12

94:1,2,3 107:14

basic 80:21
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109:3,7 113:20

167:6 198:10

basically 21:7,15

24:10 26:22 27:4

29:16 37:7 40:3

43:4 49:7 54:18

64:16 69:11

93:22 95:1

105:14 121:19

125:20 126:19

141:10 143:5

145:21,22

158:16 167:11

168:22 169:3

171:21

179:5,13,14,17,2

1,22 181:6 184:9

185:6 186:20

190:17

basis 121:17 123:9

basket 195:21

battle 46:22

bear 91:10

becomes 21:12

85:8

begin 49:12

beginning 103:12

198:7

behind 154:5

believe 34:16

80:11 145:16

148:7 178:6

belong 170:14

benefit 148:12

Benjamin

201:4,14

BENNETT

200:2,19

Berkeley 2:15

3:4,19 4:5 8:22

9:2,7 19:16

best 22:17 91:20

139:13 148:4

better 14:5 16:19

182:19 196:4

199:5

beyond 112:16

185:14

bigger 133:9

bills 51:20

bit 21:1 24:12

26:12,17 29:9

31:5 37:1 38:10

40:22 43:3 44:13

54:20,22 58:3

69:2 74:11 75:2

79:12 82:15

85:13 94:22 97:3

113:2 114:5

117:19 119:17

127:5 132:16,21

133:18 145:9

152:3 153:12

156:5,10 158:3

159:7

163:4,5,6,7,8

166:16 175:6

177:8 178:3,5

blank 126:13

blue 65:16 73:22

bother 173:4

bottom 28:20

41:10 148:6

152:2,14

bought 63:7

bounds 76:15

boy 96:11

brand 72:16

breadth 102:3

break 75:7,16 76:5

77:8 110:6

114:11

breaking 75:12

154:6

breakout 67:22

breaks 154:8

brick 104:13

brief 128:14

briefly 51:22

59:19 63:20 70:4

87:22 88:19

113:3 115:14

127:3 134:11

160:11 171:15

179:12

bring 113:5

154:21

bringing 19:11

46:12

broad 112:1

broadcast 7:1

broke 76:14

brought 177:15

browsed 150:10

browsing 102:8

Btus 163:13

181:13

bucket 25:15

Bud 7:14 22:10

75:18 90:10

135:18 197:1

Bud's 17:4

bugging 152:9

Builders 4:1 9:22

76:21

building 1:7 2:7

28:4 48:17 53:18

68:12 83:7 91:21

92:12,14,18,21

93:7

96:1,3,8,9,10,17

97:12 104:7

106:22 107:1

116:22

117:1,11,22

118:19,20,21

150:7,12

151:5,9,10 152:1

182:18

buildings 96:7

97:6,7 101:8

117:15

built 35:19 37:16

59:11 84:9 104:8

140:16 182:19

bunch 158:9

button 7:9 42:12

50:4 66:12 76:19

buy 60:3 72:16,17

73:9

C

CAC 133:2 140:14

141:2 145:17

calcs 32:20 114:21

132:8 155:3

calculate 38:22

40:11,16,18

53:8,9 64:13

83:11 92:16,17

133:16 134:16

178:9 184:2,4

185:1 189:20

calculated 29:1,2

34:1 40:5 58:10

64:8 65:20 110:1

134:9 136:21
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145:18 168:3

177:14 187:1

189:16 190:8

calculates 69:10

132:15 155:5

161:18 170:1

calculating

42:20,21 43:7

49:7 64:15 114:3

155:8 176:16

calculation 32:13

43:9 48:17 49:9

63:21 69:6 75:4

83:3 114:1

116:12 133:2

136:18 139:4

143:13 159:13

177:16 178:1

calculations

26:15,18,19

32:13 40:1 43:8

60:17 63:22

64:9,10,12 70:9

82:18 83:4 113:7

116:17,18

132:6,8 133:6

153:9 171:16

176:7,9,13,18

California 69:17

80:5 125:5,7

captured 119:1,4

care 80:10 169:19

170:12 172:19

carefully 24:19

25:4 27:22 34:11

38:8 44:13

133:13 145:9

Carl 110:22

Carlo 10:20 24:5

27:6 44:22

carries 18:6

case 21:19 30:13

31:7 32:6 37:13

38:4 40:8 42:13

43:8,9,19

49:1,16,17 55:17

83:13 84:15,18

103:17,19

105:12,13,15

107:17 109:1,15

117:10 118:7,12

127:8 135:5

140:19 141:18

142:4 145:14

146:10,12,21

147:2 148:13

149:1,19 153:20

155:11,14 161:8

163:18 164:8

172:3 175:20

176:14,17

177:1,5

181:17,18

182:10,22

183:8,9,11,12

184:17

185:15,16,19

186:19,20,21,22

187:13,16

189:11 193:3

cases 37:14 145:12

149:5 176:19

categories 109:2

caution 198:18

CBECS 34:17,18

96:8,16 117:12

119:8,9

ceiling 93:15 97:21

cell 19:7 55:14

64:4

65:13,16,17,19

66:19 67:12

78:13 80:6 82:5

124:9

cells 27:2,5,11

55:13,15,16

73:19 74:1,8,20

75:13,19 76:21

77:5,13,18

78:6,8,12 92:14

113:15 124:18

157:6,13

census 136:10,11

158:17

central 88:3

90:17,18 137:8

140:11 142:3

150:16

certain 6:13 24:6

32:11 38:4 45:6

54:11 70:16

71:22 75:10

88:7,19,22 92:4

94:10 99:17,22

109:2 120:2

121:21 126:9

139:12 145:8

149:11 156:7

173:18 178:14

certainly 15:7

199:14

CERTIFICATE

200:1 201:1

certify 200:3 201:4

cetera 18:6 68:10

80:19 90:12

108:15 111:9,15

158:13,19

change 11:22

30:14,17 31:8,10

41:13,22

42:4,6,13,15

43:21 45:12

46:3,18 47:18

58:18 59:2 71:6

74:8,13,21 75:6

77:5 79:1,20

80:7,8,12

81:4,13 84:10,13

85:14

108:9,10,16

119:10,19,20

124:7,10,11,12,1

4,19 125:1,2,8

137:22 138:1

144:5,7 151:12

154:7,19

158:13,17,18

166:10,19,20,21

167:1,4,7,8

172:5 174:18,19

176:22

177:2,4,18,20,22

178:3 188:18

193:22

194:2,3,21,22

195:9,12,14

changed 78:19

82:9 108:14

changes 22:8 77:7

79:18 84:7 122:6

159:19 166:22

182:2,6,7,8

changing 31:1

47:14 52:15 91:5

138:3 154:8

194:13,15

characteristics

68:7 155:19

characterization

20:7

charged 138:13

chart 19:22 21:4

35:10,11

CHAZ 200:2,19

check 110:11
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167:18

child 61:21

choose 89:12

148:17,22 198:4

chosen 148:2

Chris 3:2 9:5

cite 65:17

cities 33:13

citing 92:6

clarifies 67:6

clarify 44:16

clarifying 18:21

classes 40:4

100:14 103:15

110:2

clear 14:1 38:18

45:16,22 46:22

47:3 48:9 52:15

55:5 80:9 86:19

112:17 122:21

130:8 143:10

clearer 21:2

clearinghouse

173:13

Clearly 112:11

clears 69:4

click 42:15

55:10,22 66:17

67:13,14 84:12

167:12

clicked 174:9

clicking 55:12

climate 136:15

close 49:15 97:2

164:16,17

closely 43:17

closest 101:10

102:15,16

code 84:19,21

co-dependencies

99:11

Cohen 2:4 9:3

14:2

Cohen's 13:12

colleagues 14:14

column 79:21

109:9 120:11,16

121:10,13

122:12 125:21

142:22 143:14

148:6 149:16

152:7

176:10,11,12

187:14

columns 103:4,16

120:1 125:20,21

126:13 132:17

177:8

combination

195:13

combinations

157:10

comes 6:21 20:5

53:22 55:16

57:16 64:2 68:5

81:19 82:8 92:13

93:8 118:15

129:13 141:6

150:17 179:13

coming 6:7 10:11

25:1,7 32:5

58:12 59:1 81:8

97:20 104:6

105:5 115:13

124:17 137:9

151:3 170:14

175:2,3,9,10

181:8 195:19

199:13

comment 12:7,15

198:3,5,14

comments 12:3

18:9

commercial 38:7

39:5,6 44:1

52:12 54:21 55:1

96:1,7,8,11 97:5

117:14 118:15

124:8 128:18

129:5 160:21

170:10 194:22

commercial/

residential

187:9

common 140:12

commonly 100:22

101:2,4 104:16

109:12 141:5,8

142:4

Commonwealth

200:20

Company 2:20

8:12

compare 28:11

41:6 132:1

185:15,21

compared 138:8

149:15

comparing 65:3

105:17,20

186:19

comparison

145:22

competitive

149:14

complain 46:9

complete 59:5,6,9

201:9

completely 45:15

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140:11 142:3

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delineate 45:6

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distribution 24:6

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learning 70:21

71:2 82:7,9,10

84:2,12 86:6

90:11,17,19,20

91:1

least 41:14 112:14

168:21 178:21

leave 112:6,18

134:5

Lekov 3:4 9:6

length 92:16,17

93:3 96:22

101:19

Lennox 2:21 8:17

Leslie 3:5 7:21

14:14 18:20 19:3

74:19,20 75:1

87:2 88:15,16

89:6,9,15 90:5,8

169:14,15

172:14,16,22

173:4

less 20:1,19 27:9

41:1 59:17 60:1

79:15 86:19

91:13,14 103:22

112:4 137:18

145:14 146:9

149:15,17 156:2

158:3 163:13,20

164:10 192:4,5

let's 19:10 26:7

33:14,18 35:8

36:22 39:17

54:18 55:15,17

56:18 57:17

60:19 62:22

65:17 69:7,8

70:4 79:19 80:10

83:18 84:1 86:21

92:2,19 93:22
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97:3 106:18

107:5 110:6

116:22

117:13,19

118:20 119:10

122:11,12,15,22

123:2 124:7,11

125:3 126:3,4,5

127:3,18 128:1

136:13 145:8

146:3,11 147:14

148:13 152:10

155:1 158:2,8

160:4,11 162:18

163:17

164:15,16 165:9

166:9 167:17

172:6,9 175:5

176:9,11 178:4

179:12,17

181:3,8 183:2,15

184:16

189:12,17

190:16 192:5

194:1

level 105:22 140:1

147:8,9

156:14,16,21

162:11,12

175:17 186:4,10

188:1 193:9

195:6

levels 82:3 188:7

193:19 195:3,4

life 17:17 19:19

21:8,16 33:5,7

36:13 44:18

50:17 60:1 61:10

62:11 105:14

150:19

lifetime 21:18 26:1

27:19 49:2 50:12

137:2,4,6,7

174:3 180:1,2,3

light 7:10

likely 128:5

138:12 146:19

limited 15:2

line 97:2 171:6

186:4

lines 47:11

linked 55:12

list 75:19,21 76:9

107:12,15

196:14,20

listed 28:16

little 6:12 7:9

15:22 16:22 18:1

21:1 24:12

26:12,17 29:9

37:1 38:10 40:22

43:3 44:13,17

54:20,22 58:3

69:2 74:11 75:2

79:11 82:15

85:13 94:21

95:20 97:3 110:6

113:2 114:5

117:19 119:17

123:8 127:5

129:21

132:16,21

133:8,18 145:9

152:3 153:12

156:5,10 158:3

159:6 162:8

163:4,5,6,7,8

166:15 175:6

177:8 178:3,5

197:14

live 66:14

located 88:6

location 58:8

93:18,21 94:18

96:18 106:14

107:22 108:3,15

locations 94:12,19

107:20 113:12

lock 74:7,20

locked 75:19,20

76:3,22

77:5,12,14,18

78:5,8,12,13,19

153:16

locking 75:13

logic 76:4 97:13

99:18 118:9

126:6,10 139:11

143:5,9 147:22

148:11

logically 45:5 97:4

153:14

long 3:7 9:15

35:18 36:2

101:19 124:20

longer 167:17

180:9

lookup 128:15

162:5 171:1

look-up 34:4

lose 63:1

losers 62:22

lost 63:2

lot 20:8 21:11

23:14 25:17 26:5

29:8 30:10 31:19

35:7 40:19

42:17,19,21

43:10 46:20

48:15 69:14

79:10 91:11,21

92:10 93:3 102:7

103:21,22

115:15 116:20

117:14,15

126:16 127:2

128:15 132:11

133:14 139:6

156:5 157:1,7

164:11

167:17,20 175:7

190:17,18

lots 198:21

loudly 9:11

low 69:19 80:5

132:14

lower 31:9 92:20

135:8

LPG 164:10

M

macro 27:1,5 29:3

37:16,19

38:13,14,21 39:2

59:11 165:5,22

171:8 174:18

187:6,7,11

magic 60:3 194:17

main 26:15 49:10

63:20,21 168:2

173:5 176:9

177:16 178:22

181:2

mainly 28:7 92:12

176:3

maintenance

48:21 64:17,21

116:7 144:22

161:6 163:8,16

183:15 190:12

major 23:16 60:10

160:9

MALE 60:3 64:9
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111:4 194:16,18

man 19:11

manage 171:13

manager 6:5

manner 31:21

32:16

manual 168:21

manually 193:22

manufactured

20:5 69:12 70:8

manufacturer

69:22 79:20,22

80:22 83:21 89:3

manufacturer's

83:13

manufacturing

3:1,16 7:20 8:9

81:5,16

mapped 99:12

mapping 195:3

march 14:15

marginal 132:3

134:9,15

135:2,5,7,16,19

158:18

market 29:22 52:9

54:21 55:1 91:17

128:16 172:10

179:1,4

180:11,15

market-type 38:6

markup 70:1

83:14,15,22

markups 20:6

28:2 31:22

32:1,2,12

33:3,21 69:12

81:1,21 82:20,22

83:3,11 87:16

Marshall 2:19

8:11 59:21 85:1

142:20 164:18

173:9 187:12

193:8

Massaro 3:8 8:15

22:19

23:5,8,10,18

47:7,10

58:14,16,20

match 79:12 83:19

99:17 132:4

matches 63:16

118:5 125:22

math 121:7

matters 11:16

maximum 57:20

58:1 76:6 115:14

may 46:21 95:19

129:20 156:15

198:9,15

maybe 6:12 15:21

17:11 18:5 43:22

44:17 45:5

46:3,8 77:7

79:11 124:14

125:18

148:15,19

151:13 168:15

174:11 182:2,12

195:12 197:14

198:5

McCarter 3:10

7:16

mean 13:7 17:1

29:12 45:3

56:6,11 65:14

78:3 98:9

106:13,21

137:14 138:14

169:20 177:8

195:5 198:9,18

Meaning 91:1

meaningful 12:15

79:5

means 24:5 27:3

62:12 63:10

65:15,16 81:17

106:19 112:14

117:20 161:2

162:12 188:3

195:6

median 56:6

meet 55:4

meeting 1:13

6:3,15,20,22

10:12,13,17

12:2,10,17,19

13:9

14:4,7,10,22

22:13 28:10

100:11 110:17

111:2 195:22

196:4,14,16

197:4,16

198:10,14

199:3,7

meetings 198:15

mention 12:19

70:11 104:5

162:20

mentioned 87:11

104:15 131:15

196:12

mentioning 58:15

merge 180:17

message 168:11

met 1:19

method 168:13

methodology

69:22 110:18

153:8

mic 55:19 76:18

123:10 194:16

microphone 7:9

middle 102:15

198:5

miller 3:10 7:15,16

13:4 22:10 38:13

61:4,7 70:20

71:2 75:18

90:10,15,21 91:1

95:18 96:5 97:16

125:11 135:18

149:20 150:4

197:1,20 198:8

Miller's 18:9

million 118:7

123:1,3,5,20

163:13 178:17

181:13 184:11

190:10

min 63:4

min/maximum

56:6

mind 16:6 103:10

mind's 18:1

minimize 67:4

minimum 58:1

62:3,4 90:2

176:14

minor 183:7

minus 129:7

minutes 42:16

110:7 113:2

115:14 160:2

167:16

190:14,16
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misinterpreting

100:7

Mislak 3:12 9:18

Missed 103:10

mix 122:15

mobile 11:10 40:6

100:8 103:18,21

110:3 128:2,20

132:9 168:1

model 11:15 47:14

48:20

75:7,9,11,16

76:13,15 77:8

79:4 95:7 105:13

110:14 146:20

148:5 154:6,9,11

178:22

models 10:20

moment 15:1

money 72:20

Monte 10:20 24:5

27:6 44:22

month 131:22

132:4 133:7

135:4,6

monthly 132:1

133:16

134:13,14,20

morning 8:6 19:15

mortgage 138:11

mostly 40:7

111:19 195:22

motor 71:14

motors 72:1

move 19:10 35:3

36:19,22 130:18

moving 131:14

MSP 85:18

multiple 101:15,19

147:19

multiplied 182:3

multiply 70:1 81:1

135:2 183:6

185:2,5 192:7

multiplying

183:8,17 184:10

186:5

Murphy 67:19

muster 13:16

N

N/A 171:8

nation 45:4

national 2:15

3:4,15,19 4:1,5

8:22 9:2,7,22

19:16,20

20:13,17 30:5

35:21 37:8 52:1

54:10 76:20 95:7

160:13,15

165:18,20 170:7

184:2,4 186:14

nationwide 120:13

natural 3:9 8:16

22:20 47:8 58:17

134:14 135:1

Navigant 2:12 3:3

9:5,9

navigate 31:15

52:2 79:15

Neal 7:21

Nebraska 128:5

necessarily 24:16

46:16 87:8

necessary 10:19

35:14 133:22

negative 61:9

62:1,12

149:4,5,18,22

150:2 192:22

Neil 3:5 14:14

18:19,20

74:19,20 80:11

87:1 88:15 90:5

169:15 172:16

neither 200:10

NEMS 75:9

158:15

NES 184:22

Nestor 68:2

net 20:17 35:21

50:13 165:20

184:3

NHAB 97:22

NIA 5:9 27:14

35:3,13,17

38:7,20,21 39:20

45:12 49:6

60:11,12 110:13

112:20

118:6,8,11,15

139:8 153:13

158:4

160:1,4,6,10

164:14,15

165:1,2,4

166:12,13

171:14 173:16

174:20 175:1

nice 75:21 121:2

nicely 56:3,9

nicer 57:18

Nick 3:12 9:18

nine 98:5,7,12

135:12 147:6

Noll 3:14 8:10

nominal 86:15

non 28:12 40:7

42:18 100:15

116:15 126:1

127:15 139:19

140:10,12

141:18,21

143:19 162:12

167:21,22

189:22 191:14

non-condensing

127:20 143:19

153:21 161:10

191:19

non-impacted

106:10

non-weathered

40:17

non-weatherized

11:9 40:5 42:22

87:19 103:17,20

109:21 132:9

138:20 139:21

140:3,8 141:1,16

142:9 144:3,17

190:22 191:5

192:3,12

non-winter 134:16

135:4

NOPR 6:20 73:19

102:21

111:2,10,20

138:15 197:16

198:2,19 199:10

nor 200:10,14

normally 74:7

138:7

north 38:5

39:1,4,5 52:10

54:13,15 60:2
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95:9 160:17

161:1 164:12

165:17 169:12

170:10 175:15

177:4

178:7,8,13,18

179:18

180:19,20 187:8

191:10 194:14

195:9

northern 43:18

52:3 127:13

175:14 195:8

Notary 200:1,20

note 11:11 28:19

65:12 169:6

notes 201:7

nothing 67:12

notice 11:17 55:12

128:15 169:11

November 1:17

201:14

NOX 69:19 80:5

NPV 37:9 184:22

NRDC 8:10

NWGF 168:1

O

objet 168:13

obvious 99:19,20

obviously 12:22

13:21 14:7 16:10

21:11,20 23:16

26:4,8,13

28:6,15 29:11

35:8 36:20 38:19

39:4 40:19 43:2

44:3 48:2

49:17,21 54:19

59:18 62:1 63:13

64:20 66:5 67:5

68:16 69:13

81:16 84:6 87:10

91:19 92:9 93:11

96:13 97:18 99:3

100:21 102:6

104:11 105:19

108:3 113:6

115:11 116:18

119:20 128:18

131:18 133:14

136:2,5 137:1

139:21

140:15,17,22

142:13 143:17

144:12 146:15

150:11 153:19

156:4 157:1

159:9,11 163:14

164:9 171:20

172:4 181:18

184:5 185:2

188:20 190:18

199:11

occur 146:21

163:10

occurred 171:18

occurring 146:11

171:19

179:15,21

180:10 183:6

186:8

occurs 139:4

145:15 146:2

183:16 186:13

odd 197:13

off-chart 15:7

offensive 13:5

offered 11:12

office 1:5,7

2:2,4,6,7,9 8:20

9:4 10:10 97:12

officer 200:2

oh 17:4 23:10

28:11,12 49:15

56:20 57:3 58:16

59:4 61:16

63:5,8 73:3

81:11 85:7 90:22

102:19 103:9

122:2 123:12

152:6,8,15

184:15 188:2,13

191:21 193:10

OIRA 6:18,21

okay 6:2 10:5,9

12:18 14:18

17:2,3 19:10

22:9 23:10 33:16

34:3 36:15,18

44:11 45:3,18

47:1 48:9

51:2,12

53:16,19,20

54:16 57:3 59:13

61:3,7 63:8,12

69:11 71:12

73:4,8,11 79:6

85:7 86:16 90:15

91:7 95:9 97:16

102:19,22

103:3,9 106:3,7

110:8 111:22

117:1 119:22

120:1,10

121:2,5,12

122:4,9 123:18

124:20 126:18

128:11 130:7,10

131:4 136:1

138:17 152:8

159:22 160:1

167:18 168:17

173:12 187:17

188:13 189:13

190:14 191:10

195:15,16

198:17

old 92:21 109:6

129:8

OMB 11:19

ones 25:8 27:22

30:16 77:1

125:19 137:20

160:19 168:5

191:4,20

192:11,13

196:20

one's 80:19

ongoing 11:7

oops 83:19

open 13:2 29:11

33:20 37:18

39:18 43:14,17

48:2 49:15,20

65:15 67:17 69:3

164:15 165:1

196:10 198:16

opening 5:4 13:2

14:18 17:3 18:8

operate 14:6

operating

21:10,17 37:11

48:20 50:12,13

58:9 64:16 116:9

144:10 146:15

148:17 149:15

184:1 185:1,2,12

187:2 193:4

operations 149:7

opportune 197:17

opportunity

12:7,14

opposed 138:11
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196:6

option 31:7 73:20

84:9 112:3 142:7

145:15 146:1

147:3 149:17

168:14

options 27:8 30:15

87:21 88:1,17

96:6 140:18

141:14,15

142:18

143:12,18,22

144:2 145:7

147:19 148:1,4

order 13:20

original 106:17

originally 151:2

182:12

orphan 109:13,15

others 35:17 98:18

133:22

otherwise 51:9

200:15

outcome 200:15

outdated 34:16

outlook 158:16

output 45:9

47:16,19 55:16

56:4 59:5 64:7

95:14 146:22

166:4,9 167:9

168:9,20 188:21

outputs

15:2,5,12,19

20:10,12 27:13

35:9,13,14 38:18

39:17 55:8 57:16

60:11 83:21

160:7 165:21

177:21

outputted 83:17

174:18

outside 75:11

overall 10:13

156:9 165:15

overview 6:11

overwhelming

92:9,10

overwrite 80:16

owners 176:6

179:2,7

180:15,18

184:13

P

Pacific 2:20 8:11

Papageorge 3:17

8:6,7

paper 22:16

parameter 84:3

141:9 154:22

parameters 18:6

27:16 41:12 70:6

77:2 83:3 92:11

107:8 108:8

116:14 119:3

131:21 133:6

136:12,19

137:3,21 139:10

145:2 160:6,9

166:16 167:6

170:12

particular 51:8

59:7 68:14 95:20

105:1 112:1

120:11 133:20

135:5 172:15

173:10

parties 11:1

200:11,14

passed 13:16

paste 39:1 169:4

170:6,8 171:3

pasted 166:1

pastes 39:2

pasting 171:10

patch 7:3

Patio 2:13 10:2

pause 110:6

pay 37:2 62:20

146:6

payback 20:11

21:6,8

26:16,17,19

32:20 51:15

114:20

145:5,8,17,21

146:2,4 149:22

150:2 154:4

155:3 159:11

paybacks 50:16

paying 62:13

payment 138:12

PB 33:1

people 7:1 30:1

60:22 62:2,4,8

63:11 74:21

82:11 112:14

196:2 199:8

per 156:9 184:7,20

186:12

percent 41:20

56:6,7

62:2,12,15,22

76:13

81:10,11,12,17,1

8 82:2,4,5,13

92:20 94:10 98:7

105:2,13 109:11

118:12,13,14

121:3 122:15,16

123:2 127:19,22

128:4 130:6,16

141:18 143:21

144:1,3 145:7,22

146:13 149:16

155:10,15

156:19 157:4

158:1 161:9

162:15,19,21,22

163:1,2,11

164:3,7 175:17

177:3,6 179:18

181:12,13

182:13 186:5

187:4,10 190:22

191:1,19,22

192:2,15 194:19

percentage 62:16

82:1 95:22 123:5

percentile

61:9,17,18,22

62:6,13

percentiles 57:21

58:1 61:5 68:22

Peretti 111:11

perfect 167:18

period 20:11

21:6,8 35:18

36:2,7,8 37:2

41:18,19 154:4

175:22 182:6

198:3,5,14

person 173:10,13

perspective 15:17

24:2

pertaining 11:14

Peter 2:18 9:13

Petrillo 150:5
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Petrillo-Groh 2:16

8:3 100:5 106:12

107:16 108:1,19

150:5 151:4,9

154:2

PG&E 59:21 85:2

142:20 173:10

physics 194:17

pick 34:12 54:1,3

99:17,18

107:8,9,11

155:12 157:7

185:4 189:10

196:7

picked 84:8 98:7

99:11 105:22

106:4 119:16

135:1 143:18

150:21 153:5

168:8 178:12

picking 34:7 60:21

79:21 98:10

115:1 125:20

127:21 128:3

150:12,13,14

153:1 155:10,18

169:12

picks 80:2,3 83:9

piece 32:18 143:13

164:6

pipe 92:16,17

piping 114:5

places 187:22

planned 197:4

play 18:5 58:11

73:20 124:3,4

149:12

154:6,10,17

190:5 199:4

playing 108:5

please 7:11 11:11

34:21 45:20

49:13,18 76:18

88:14 133:12

139:8 196:7

plus 21:16 36:13

141:8 164:2

175:1

point 16:7 17:9

19:10,11 28:15

42:11 48:7 54:19

95:8 97:14 104:3

114:18 124:2

128:14 166:7

178:14 182:8

186:2 195:2

pointed 196:22

points 113:5

policy 11:14

12:5,6

pop 126:9

pops 126:5

populated 124:15

populating 174:20

population

53:1,13 61:9

62:18 120:21

152:20

portion 111:17

pose 15:5

positive 108:16,17

possible 31:19

45:22 73:18

106:20

198:12,13 199:9

potential 153:22

154:6 155:21

potentially 176:6

power 111:14

prefatory 14:20

prefer 196:2

preference 198:6

preliminary 11:6

194:3

present 20:17 21:5

29:9 35:21

37:10,12 41:21

139:13 165:20

184:3

presentation 29:11

70:12 111:17

presented 30:4

presenting 6:9

26:11

presents 21:7

presiding 1:22

press 50:4

presumption

159:5

pretty 31:17 102:6

112:17 158:10

166:20 196:14

previous 23:2

76:12 132:19

price 20:7 23:4,14

27:17,18 28:1,2

31:1 35:15 42:4

50:12 64:5,20

71:5 81:18,20,22

82:13,21

84:1,13,17 85:19

86:1,4 88:4,5,13

115:3 134:15,17

135:3,5,6,8,10,1

1,19 136:8

144:6,13,20

149:13,14

158:12,14

159:1,16 161:6

167:1,2 172:4

173:18 175:19

183:10,11,13,19

185:3,4

prices 35:16

48:19,22 64:4

69:10,11 71:6

83:12,17,18

86:18

87:15,17,18

132:2,3,5

134:6,7,9,10,13,

14,20,21 149:13

158:17,18,19

161:19 163:6,7

170:13

173:16,17

pricing 129:10

158:21

primarily 92:14

primary 20:3,13

21:9 28:9

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summarizes 175:4

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