Goodbye, “Last Measure In” Hello, “Option X”?

Goodbye, “Last Measure In”Hello, “Option X”?

Staff Proposal for how to deal with the last measure in conundrum

Regional Technical ForumOctober 15, 2013

2

Last Measure In - History

• Problem: Simple versus Accurate– Programs want simple deemed measures for

weatherization and heat pumps.– With simple deemed measures, we don’t know

• The order in which measures get installed. • The starting point from which the measures get installed

• Solution: – Last Measure In

• While admittedly conservative, LMI allows for a reasonable savings estimate.

– Keep Weatherization separate from Heat Pumps• Weatherization measure savings dependent on existing heating system

3

Then came along the Guidelines…

4

Guidelines “The UES for each measure should be computed under the assumption that all other measures it significantly interacts with are already implemented. Interaction is significant if the RTF determines that it is likely to account for more than 10% of the measure savings. The other measures assumed to be present should be consistent with expected typical conditions at the end of the measure’s effective useful life. This “last-in” requirement may create a downward bias in the short-term savings estimate for a measure.”

Savings, Section 2.3.3.4. Interactions between Measures

5

Faithful implementation of the Guidelines appears to require that improvements in the HVAC system compete with shell improvements as a measure

• Simple logic:– Heat Pumps “significantly interact” with all the weatherization

measures.– So, they should be included in the analysis.

• In order to get the savings right, the RTF needs to agree on a forecast of the future mix of heat pumps.– But how far out in the future should we look?

• Guidelines say EUL, but that’s probably too far.– What about first year savings?

• Heat Pumps don’t play well with Last Measure In.

6

Heat Pump’s Effect on Wx Savings

1344 sq.ft. prototype; Heating Zone 1

Measure Duct TightnessHeating System

Attic Wall Floor Windows InfiltrationHeating Energy

(kWh/yr)

"Actual' savings

Existing Condition RBSA 9.0 HP 19 none 11 0.85 0.75 6594 n/aDuct Tightness PTCS 9.0 HP 38 11 25 0.22 0.35 2570 479Total 4025

Measure Duct TightnessHeating System


(kWh/yr)

"Actual' savings

Existing Condition RBSA FAF 19 none 11 0.85 0.75 11158 n/aDuct Tightness PTCS FAF 38 11 25 0.22 0.35 4724 675Total 6434Fully Weatherized

Fully Weatherized

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… and SEEM “Calibration”.

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Calibration• Calibration required a change in thermostat setting

between poorly insulated and better insulated homes:

• This creates a discontinuity in the relationship between UA and space heating use/savings.– Now a measure could save significantly more if it were the

last measure in.• For an example, see savings for Walls in the “Measure Order” slide in the additional slides section.

Good Floor

Poor Floor

Good Floor

Poor Floor

Good Floor

Poor Floor

Good Floor

Poor Floor

70.3 68.2 66.5 64.5 66.9 64.8 63.1 60.7Daytime Thermostat Setting (°F)

Heating ZoneHeating System

Ceiling or Wall Insulation

Floor Insulation

Good Ceiling and Wall

Poor Ceiling or Wall

Good Ceiling and Wall

Poor Ceiling or Wall

Heating Zone 1Gas/HP Electric Resistance

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ΔT-stat Setting’s effect depends on house UA

• So, insulation measures that begin with R-0 and cause an increase in T-stat setting save less if they’re done “first” (in a High UA house), versus “last” (in a Low UA house).

• This makes the last-in approach the opposite of conservative for these measures.

0

5000

10000

15000

20000

25000

55 60 65 70Heati

ng E

nerg

y U

se (k

wh/

yr)

Thermostat Setting (°F)

High UA Medium UA Low UA

0200400600800

100012001400

55 60 65 70

Slop

e:

ΔHe

ating

Ene

rgy

Use

ΔTh

erm

osta

t Setti

ng

Thermostat Setting (°F)

High UA Medium UA Low UA

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ΔT-stat Setting Affects other Measures, too

• In houses with an R-0 component, savings from other components will be affected based on their order with respect to the R-0 component.

• Example House: Attic R-0, Wall R-11, Floor R-11; • Measure: Floor R-11 to R-25.– Scenario 1: Floors first

• Baseline and Efficient-case T-stat Setting: 66.5°F• Lower savings from floors

– Scenario 2: Floors last (Last Measure In)• Baseline and Efficient-case T-stat Setting: 73.5°F• Higher savings from floors

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Measure Order

Now let’s change the measure order…

Measure Order-specific Calculator

MeasureHeating System


(kWh/yr)Savings

(kwh/yr)Existing Condition Zonal 19 none 11 0.85 0.75 9263 n/aHeating System DHP 19 none 11 0.85 0.75 6534 2729Attic DHP 38 none 11 0.85 0.75 6173 361Windows DHP 38 none 11 0.22 0.75 4967 1206Wall DHP 38 11 11 0.22 0.75 3775 1192Floor DHP 38 11 25 0.22 0.75 3311 463Infiltration DHP 38 11 25 0.22 0.35 2471 840Total 6792

LMI Savings (kwh/yr)

n/a1744392126913394588406041




(kWh/yr)Savings

(kwh/yr)LMI Savings

(kwh/yr)Existing Condition Zonal 19 none 11 0.85 0.75 9263 n/a n/aWallAtticWindowsInfiltrationFloorHeating System

Zonal 19 11 11 0.85 0.75 8540 723 1339Zonal 38 11 11 0.85 0.75 8001 539 392Zonal 38 11 11 0.22 0.75 6182 1819 1269Zonal 38 11 11 0.22 0.35 4965 1217 840Zonal 38 11 25 0.22 0.35 4215 750 458DHP 38 11 25 0.22 0.35 2471 1744 1744

6792 6041Total

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So Now What?

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Primary Options for Calculating Savings

Last-measure in– Simple– Inaccurate

• T-stat settings• Heating system conversions• Requires a forecast• SEEM’s complex interactions

Savings Based on Existing Conditions at each House– Complex

• Requires an audit of the house (just like we used to do)– Most Accurate

• Both methods have many possible methods and sub-options; they can also be combined in different ways.

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Some Options

15

Options Overview

ID Description

1A LMI Status Quo.

1B LMI assuming 85% of cost-effective measures.

1C LMI with measure-specific saturations.

1D LMI on an annual basis.

2A Use Existing Conditions for each house.

2B Use Existing Conditions for RBSA houses.

3 Use Existing Conditions for RBSA houses (2B), but prorate savings using ratio of LMI (1A) to "full package savings."

4 Use Option 3 for weatherization measures, and Option 2A for heat pump measures.

Option Guidelines w.r.t LMI

Program Delivery

Savings Reliability

16

Option 1A – LMI Status Quo

• Last measure in with all measures installed, except heating system known as a part of measure definition.– Separate attic insulation UES’s for a house with zonal heat and a

house with a heat pump.• Pro

– Simple– Familiar– Deals with heating system accurately for weatherization measures

• Con– Doesn’t deal well with uninsulated components’ interactions.– Doesn’t adhere to guidelines with respect to last measure in

• Assumes all measures are installed, not expected measures at EUL

17

Options OverviewID Description

1A LMI Status Quo. ?









Program Delivery

Savings Reliability

18

Option 1B – LMI assuming 85% of Cost Effective Measures

• Include all measures, including heating system, in last-measure in assumption at a rate of 85%.– One attic insulation UES, independent of existing heating system.

• Pro– Simple– Lines up with Council Planning Assumption– Attempts to line up with Guidelines with respect to last measure in

• Con– Assumed high penetration of heat pump conversions will cause a

large underestimate of actual saving if penetration is not achieved. – Doesn’t deal well with uninsulated components’ interactions.– Many Others

19











Program Delivery

Savings Reliability

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Option 1C – LMI with Measure-specific Saturations

• Same as 1B, except estimate a saturation at end of measure’s EUL (different than 85%) for each measure component.

• Pro– Still simple– Lines up with Guidelines with respect to LMI.– Improves reliability (over 1B) of near-term savings by reducing error

caused by heat pump penetration assumption • Con

– Savings reliability is sensitive to the heat pump saturation estimate– Doesn’t deal well with uninsulated components’ interactions.– Still requires a forecast– What should the correct forecast period be?

• Guidelines currently say at end of measure life, but that could be revised.

21











Program Delivery

Savings Reliability

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Option 1D – LMI on an Annual Basis • Like Option 1C, but each measure would have a saturation forecast curve.• This would allow unique savings to be calculated for each year of the

measure life.• Pro

– Still simple program delivery– Reasonable savings estimate accuracy each year (assuming forecast is correct)

• Further improves reliability (over 1c) of near-term savings by reducing error caused by heat pump penetration assumption

– Adheres to guidelines with respect to LMI• But, Which savings estimate would the RTF use?• 1st year, average over the EUL, end of EUL, something else?

– Deals with uninsulated components’ interactions.• Con

– Savings estimate accuracy depends on reliability of forecast– More involved analysis

23











Program Delivery

Savings Reliability

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Option 2A – Existing Conditions for Each House

• Base savings on existing conditions at site.• Pro

– Savings are most accurate (average across all participants)• Eliminates any under counting and double-counting• Accurately deals with uninsulated measure interactions and heating systems.

– Lines up better with billing analyses, standard protocols, and custom measures• Con

– Very involved program delivery• Requires identification of detailed as-found conditions at house

– Requires many more UES values (or Standard Protocol) for each combination of existing conditions

– Data collection/reporting errors could be large– Doesn’t adhere to guidelines w.r.t. LMI

25

Options Overview

ID Description










Program Delivery

Savings Reliability

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Option 2B – Existing Conditions for RBSA Houses

• Calculate savings for each relevant characteristic scenario in the RBSA dataset, take the weighted average.– Example Measure: Attic Insulation R-0 to R-38

• Filter RBSA dataset for houses with R-0 attic insulation• Determine percentage of houses with specific characteristic scenarios

– Example: » 5% have Zonal heat, Walls at R-0, Floors at R-0, etc.» 12% have Zonal heat, Walls at R-11, Floors at R-0, etc.» Etc.

• Pro– Does not impact program delivery– Savings fairly accurate because known distribution of starting points

• Con– Doesn’t deal with multiple measures installed simultaneously

• Heat pumps and weatherization– Component saturations need to be updated before they move too much

• Need new RBSA data. 5 year schedule adequate for Weatherization? heat pumps?– More involved analysis than status quo, many many measures and permutations.– Relies on Program house characteristics matching RBSA– Some data (infiltration, duct tightness) not available on each house– Doesn’t adhere to guidelines w.r.t. LMI

27







2B Use Existing Conditions for RBSA houses. ?




Program Delivery

Savings Reliability

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Option 3 – Use Existing Conditions for RBSA houses (2B), but prorate measure savings using

ratio of LMI (1A) to “full package savings”

• Similar to Option 2B (Existing Conditions for RBSA Houses), except for within each characteristic scenario, calculate savings for the entire “full measure package” (all the way to Attic R-38, Wall R-11, Floor R-25, etc.), then prorate each measures’ LMI savings based on the ratio of the “LMI full measure package savings” to “actual full measure package savings”.

• Weatherization measures would be defined for each existing heating system type

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Attic Wall Floor Window Infiltration Full Package2074 1444 571 804 507 5400

MeasureOption 3 Savings (Ratio X LMI for Attic R-0

Heating System

Attic Wall Floor Window InfiltrationEnergy Use

(Calibrated SEEM)

Characteristic Scenario Zonal R-0 R-0 R-0 u-0.5 0.55 ACHn 9616Full Measure Package Zonal R-38 R-11 R-25 u-0.22 0.35 ACHn 4215

"Actual" Full Measure Package Savings --> 5400

Attic Wall Floor Window Infiltration Full Package2529 1761 696 980 618 6584

Ratio: Actual/LMI --> 0.82

Last Measure In SavingsMeasure

Option 3: Explained With Numbers

Note: Only one Characteristic Scenario is shown here for simplicity. Instead, for each measure, the ratio “actual/LMI” would be determined for each applicable characteristic scenario and weighted according to its frequency of occurrence in the RBSA.

Note 2: Heat Pump conversion measures would be calculated in the same manner, but they would receive their own Actual/LMI ratio’s.

30

Option 3• Pro

– Deals with multiple measures installed at the same time, or over time.

– Early investigation by staff shows this should work well for weatherization measures with a known heating system

• Con– Difficult to explain– More complicated analysis

• New measures may require re-running analysis to develop new ratios– Accuracy can depend on how close houses get to the full

measure package.– Doesn’t adhere to guidelines w.r.t. LMI

31

Options Overview

ID Description







3 Use Existing Conditions for RBSA houses (2B), but prorate savings using ratio of LMI (1A) to "full package savings." ?



Program Delivery

Savings Reliability

32

Option 4 – Use Option 3 for weatherization measures, and Option 2A for heat pump measures

• For weatherization measures, with measure specification tied to existing heating system type:– Use Option 3 – Use Existing Conditions for RBSA houses (2B), but prorate savings using

ratio of LMI (1A) to “full package savings”• For heating system conversion measures:

– Use option 2A – Use existing conditions for each house.• Audit house at each site and use the audit data to calculate a house-specific savings value.

• Pro’s– Keeps weatherization program operation simple– Deals with multiple measures and interactions between measures– Keeps accurate heat pump savings

• Con’s– For weatherization, accuracy depends on definition of “full package”– For heat pumps, significant increase in program complexity

• Heat pump contractors are the likely ones to provide house insulation levels and other necessary characteristics (“necessary” would still need to be defined)

– Doesn’t adhere to guidelines w.r.t. LMI

33

Options Overview

ID Description







3 Use Existing Conditions for RBSA houses (2B), but prorate savings using ratio of LMI (1A) to "full package savings." ?

4 Use Option 3 for weatherization measures, and Option 2A for heat pump measures. ?


Program Delivery

Savings Reliability

34

Ok…

There’s no Perfect Solution

How do we assess reliability so we can pick the least imperfect Option?

(And once we do that, is it sufficiently reliable, or should we revert to program impact evaluation?)

35

Comparing Methods• Given:

– We know Options 3 and 4 won’t give us “Actual” savings every time.• Question:

– On average, how well will each option give us “actual” savings?• Problem:

– What is “average”? We don’t know:• which measures will be installed, and• which houses they’ll be installed in.

– Assumption• For the purposes of the Comparison exercise, let’s assume:• All possible measure combinations have an equal likelihood of being

installed.

36

Setting Up the Comparison• Each Characteristic Scenario,

has its own suite of possible measures, measure orders, and stopping points.

• For each possible measure installation order and stopping point, we can calculate the Ratio: Option’s Savings to “Actual” Savings.

• Then, we can generate a histogram of the results, for each option…

# HVAC Attic Wall Floor Window1 Zonal 19 11 11 0.5 9.9%2 Heat Pump 38 11 11 0.5 9.0%3 Zonal 38 11 11 0.5 8.5%4 Heat Pump 19 11 11 0.5 6.7%| | | | | | |

90 FAF none 11 Basement 0.5 0.1%91 FAF none none none 0.85 0.1%

RBSA Weight

Characteristic Scenario

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Characteristic Scenario #1: Zonal, Attic R-19, Wall R-11, Floor R-11, Windows U-0.50

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Ratio: LMIstatus quo / "Actual" Savings

All possible stopping points, All possible measure installation orders, For this Scenario

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Ratio: Option 3 / "Actual" Savings


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All possible stopping points, All possible measure installation orders, For this Scenario0.61

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Ratio: LMI / "Actual" Savings


Average: 0.71

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Average: 0.71

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Average: 0.71

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Ratio: LMIstatus quo / "Actual" Savings


We do this for each of the 91 Characteristic

Scenarios,

Apply the RBSA Weight for each

Scenario,

# HVAC Attic Wall Floor Window1 Zonal 19 11 11 0.5 9.9%2 Heat Pump 38 11 11 0.5 9.0%3 Zonal 38 11 11 0.5 8.5%4 Heat Pump 19 11 11 0.5 6.7%| | | | | | |

90 FAF none 11 Basement 0.5 0.1%91 FAF none none none 0.85 0.1%

RBSA Weight

Characteristic Scenario

And Generate the following Histograms…

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Average: 0.99

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y



Average: 0.95

0%

5%

10%

15%

20%

25%

30%

35%

40%

< 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 > 2

Freq

uenc

y



Average: 0.95

0%

5%

10%

15%

20%

25%

30%

35%

40%

< 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 > 2

Freq

uenc

y



Average: 0.95.

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0%

5%

10%

15%

20%

25%

30%

35%

40%

<0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 >2

Freq

uenc

y

Ratio: LMIsq / "Actual" Savings

All Scenario's weighted to RBSA, All possible stopping points, All possible measure installation orders

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0%

5%

10%

15%

20%

25%

30%

35%

40%

<0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 >2

Freq

uenc

y



43

0%

5%

10%

15%

20%

25%

30%

35%

40%

<0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 >2

Freq

uenc

y



44

0%

5%

10%

15%

20%

25%

30%

0 0.5 1 1.5 2

Freq

uenc

y

Ratio: Option Savings / "Actual" Savings

Options ComparedAll Scenario's weighted to RBSA, all possible stopping points and installation orders

Option 4Option 3LMIsq

Note: High and Low extremes have been cut off and are not included in the average (these are likely places where the “actual” savings are incorrect.

Weighted AveragesOption 4: 1.00Option 3: 0.98LMIsq: 0.96

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Staff Recommendation• Option 3

– Reliable• While the comparison method is not perfect, it provides some confidence that Option 3 is likely

to provide reliable savings, on average.– Doable

• Program implementation will be mostly unchanged (“average heating system” case will be removed). (Early discussions imply Option 4 could be a deal-breaker for programs.)

• It requires more complicated analysis than LMIsq (Option 1A), but it’s probably worth the extra effort.

Option 1A – LMI Status QuoOption 1B – LMI assuming 85% of cost-effective measuresOption 1C – LMI with measure-specific saturationsOption 1D – LMI on an annual basisOption 2A – Use Existing Conditions for each houseOption 2B – Use Existing Conditions for RBSA housesOption 3 – Use Existing Conditions for RBSA houses (2B), but

prorate savings using ratio of LMI (1A) to “full package savings”

Option 4 – Use Option 3 for weatherization measures, and Option 2A for heat pump measures

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Decision

“I __________ move the RTF use Option ___ in estimating savings for residential heating system and weatherization measures.”

Note: The guidelines are currently being reviewed. Today’s decision will be taken into consideration when proposing edits to the section of the guidelines that deals with last measure in.

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Extra Slides

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Measure Order

Now let’s change the measure order…

Measure Order-specific Calculator



(kWh/yr)Savings

(kwh/yr)Existing Condition Zonal 19 none 11 0.85 0.75 9263 n/aHeating System DHP 19 none 11 0.85 0.75 6534 2729Attic DHP 38 none 11 0.85 0.75 6173 361Windows DHP 38 none 11 0.22 0.75 4967 1206Wall DHP 38 11 11 0.22 0.75 3775 1192Floor DHP 38 11 25 0.22 0.75 3311 463Infiltration DHP 38 11 25 0.22 0.35 2471 840Total 6792

LMI Savings (kwh/yr)

n/a1744392126913394588406041




(kWh/yr)Savings

(kwh/yr)LMI Savings

(kwh/yr)Existing Condition Zonal 19 none 11 0.85 0.75 9263 n/a n/aWallAtticWindowsInfiltrationFloorHeating System

Zonal 19 11 11 0.85 0.75 8540 723 1339Zonal 38 11 11 0.85 0.75 8001 539 392Zonal 38 11 11 0.22 0.75 6182 1819 1269Zonal 38 11 11 0.22 0.35 4965 1217 840Zonal 38 11 25 0.22 0.35 4215 750 458DHP 38 11 25 0.22 0.35 2471 1744 1744

6792 6041Total

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Sunday vs SEEM and LMI• Sunday said: A weatherization measure saves less when it’s installed in a

more efficient house.

– This made sense.• As the house gets more efficient, internal and solar gains meet a larger percentage

of its heating needs.

– This means LMI is always conservative

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Sunday: 1350 sq ft Prototype in Portland

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SEEM• Hourly simulation, models the effects of:

Solar gains, ground contact, crawlspace and attic buffer spaces, thermal mass, infiltration, radiant heat transfer, and duct leakage.

Input:.

Output: Heating Energy Use

Sunday SEEM

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Let’s Compare• Sunday: (again)

• SEEM: “Last Measure In” is no longer predictably conservative.

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Goodbye, “Last Measure In” Hello, “Option X”?

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Transcript of Goodbye, “Last Measure In” Hello, “Option X”?