SEEM 94 Calibration to Single Family RBSA Data Analysis and proposed actions

SEEM 94 Calibration to Single Family RBSA Data

Analysis and proposed actions

Regional Technical ForumMay 21, 2013

Overview

• Purpose• History• Methodology– Data– Regression– “Calibrate”

• Discussion• Proposal

Overview - 2

Overview



Purpose - 3

Purpose: Align SEEM with Measured Energy Use• The SEEM model is used to estimate energy savings for

most space-heating-affected residential UES measures using the “calibrated engineering” estimation procedure (see section 2.3.3 of guidelines)– Heat Pumps and Central AC (ASHP, GSHP, DHP)– Weatherization– New Homes– Duct Sealing– Space Conditioning Interaction Factor

• Goal: Ensure SEEM94’s results are grounded in measured space heating energy use of single family homes. Use RBSA as source of measured data.

Purpose - 4

http://rtf.nwcouncil.org/subcommittees/guidelines/Savings%202013-04-16.docx

RTF Savings Guidelines

Purpose - 5

2.3.3.2. Model Calibration

In most cases, calibrated engineering procedures will involve at least one stage of modeling in which baseline and efficient case energy consumption are estimated for the measure-affected end use. For example, the heating load for single-family homes is estimated as part of the derivation of UES for ductless heat pump conversion. A simulation model is used to derive the heating end use for typical homes in different climate zones. Ideally, the model would be calibrated to measured heating end use for a sample of homes. If end use data are not available, the model should at least be calibrated to metered total use for the sample. Calibration should also be performed for samples that have adopted the measure, i.e., the efficient case. For measures that affect new buildings the calibration may be limited to the efficient case or to comparable buildings of recent vintage.

Overview



History - 6

RTF Decision History

History - 7

Date RTF Decision Summary Housing Type T-stat Results Data Sources Used in Calibration

Nov-2009 SEEM 92 model is calibrated. Single Family

HP & Gas FAF70°F Day ; 64°F NightElectric FAF and Zonal

66°F Day & Night

1. Res New Const. Billing Analysis (RLW 2007) 2. SGC Metered Data 3. NEEA Heat Pump Study (2005) Note: Very limited representation of Zones 2 & 3

Apr-2011SEEM 93 model is

calibrated. (implicit decision)

Single Family with GSHP 70°F Day ; 64°F Night 1. Missoula GSHP Study (1996)

Dec-2011 Use updated SEEM94 model

Single Family,Manufactured

Homen/a

Ecotope updated SEEM code to model the physics of the house infiltration, rather than rely on a constant stipulated infiltration rate input in previous versions of SEEM.

Dec-2011 SEEM 94 model is calibrated

Manufactured Home

69.4°F Day61.6°F Night

1. NEEM 2006 2. NEEA Heat Pump Study (2005) 3. MAP 1995 4. RCDP (manufactured homes)

Sep-2012 SEEM 94 model is calibrated Multifamily

Walk-up and Corridor68°F Day& Night

Townhouses66°F Day & Night

1. Multifamily MCS (SBW 1994) 2. MF Wx Impact Evaluation for PSE (SBW 2011) 3. New Multifamly Building Analysis (Ecotope 2009) 4. ARRA Verification for King County (Ecotope 2010)

RTF Decision History (Continued)For “model is calibrated” decisions…Calibration Methodology:1. Use available house and operation characteristics data from

billing/metering studies to develop inputs to SEEM runs;2. Adjust SEEM thermostat setting input to achieve a good

match (on average) between SEEM output (annual heating energy use) and billing/metering study results.

Note: The data sources used were free of (or mostly free of) supplemental fuel usage (wood, propane, oil, etc.) • Collection of reliable electric and gas usage data for space

heat consumption is relatively easy compared to other fuels.

History - 8

Date Forum Topic Outcome Links

1/23/13 Full RTF

Proposal to adopt calibration:

Send staff back to assess calibration needs related to

climate and measure parameters; and

engage subcommittee.

PresentationMinutes

3/20/13 Sub-committee Status update and check in. Presentation

Minutes

5/7/13 Sub-committee

Review staff’s proposal in detail. Decide whether to recommend RTF adoption.

See next slide PresentationMinutes

SF Calibration to RBSA - Recent History

Heating System Type

HeatingHigh °F(day)

HeatingLow °F(night)

Electric Zonal64 64

Electric FAF

Gas FAF 68.6 63.9

Heat Pump 69.6 65.4

History - 9

http://rtf.nwcouncil.org/meetings/2013/01/SEEM94%20Calibration%20to%20RBSA%20Data_v3.pptx

http://rtf.nwcouncil.org/meetings/2013/01/minutes20130123.pdf

http://rtf.nwcouncil.org/subcommittees/seem/SEEM%20Subcommittee_March%2020%202013.docx

http://rtf.nwcouncil.org/subcommittees/seem/SEEMCalibrationSubcommittee7May2013_JDR_07MayEdit.pptx

May 7, 2013 SubcommitteeAttendees

Adam Hadley

Josh Rushton

Bob Tingleff

Jim Maunder

Rick Knori

Bill Koran

Mike Lubliner

David Bopp

Jeff Maguire

Nick O’Neil

Christian Douglass

Dave Roberts

Scott Horowitz

Ben Larson

David Baylon

Mark Jerome

Mohit Singh-Chaabra

Debra Bristow

Mark Johnson

Peter Miller

Cory Read

Paulo Tabares

Tom Eckman

History - 10

• 3.5 hour meeting• Summary:

– The group reviewed an earlier version of this presentation, along with the details of the regression development.

– The group gave recommendations and discussed next steps.• Subcommittee Recommendations (staff completed these)

Describe the regression development in a separate report/memo Correct the uninsulated wall u-value Re-calculate regression using a binned HDD variable, rather than

continuous• Major Issue: Many subcommittee members were very

uncomfortable with some of the very low thermostat setting results.– Problem: No alternative calibration method identified.

• Conclusion: Move forward with presentation to the RTF.

Overview



Methodology - 11

Methodology Overview – Data (Step 1)

Create two data sources to compare estimates of space heating for homes in RBSA dataset:

– RBSA Billing Analysis: Estimates of annual “space heating use” for each house determined by using VBDD• VBDD is a “change-point” regression model which uses billing histories to

estimate temperature sensitive use• VBDD analysis is based on monthly billing data (at least 2 years)

– SEEM Simulation Analysis: Estimated annual space heating energy use for each house based on SEEM engineering model• RBSA individual home characteristics (e.g., thermal envelope, heating system

type, duct tightness) used as model inputs;• Initial model runs use thermostat set to 68°F day & night

– SEEM is a one-zone model, so t-stat setting input represents the average for the entire house– Actual t-stat settings are not well documented (occupant reported settings are unreliable,

especially for zonal systems)– Thermostat setting will be used (step 3) as the “calibration knob”.

Methodology - 12

Methodology Overview – Regression (Step 2)

Use regression techniques to identify building characteristics that drive systematic differences between SEEM(68°F) and Billing Analysis space heating energy use estimates.

Methodology - 13

Methodology Overview – “Calibrate”(Step 3)

Use regression results to determine thermostat set-point that will align (i.e., “calibrate”) SEEM with Billing Analysis annual space heating use.– Calibration based on comparing average of all SEEM

annual estimates to average of all Billing Analysis annual estimates.

– Calibration is based on building characteristics identified in regression.

– SEEM run for each house at varying “day-time” thermostat settings, with “night-time” thermostat settings based on occupant-reported setbacks.

Methodology - 14

Overview



Methodology - Data - 15

Data Sources

• Data Source used in this calibration:– Underlying database* for the Single Family

Residential Building Stock Assessment (2012)• RBSA study’s database offers recent billing analyses

results and detailed house characteristics on 1404 houses in the Region.• This allows well-defined SEEM runs for each individual

house.


* Using a pre-release version of the database for this analysis .

Key Model Input Parameters

RBSA Data Availability

UA Available for each house.

Weather Zip code (available for each house) linked to nearest TMY3 weather station.

Gas Heating Efficiency Available for some houses; used average for remaining houses.

HP Operation & Efficiency Not readily available. Used ARI control & 7.9 HSPF.

Duct System Leakage and Surface Area

Available for some houses; used average for remaining houses with ducts.

Duct System Insulation and Location

Available for each house.

Infiltration Available for some houses; used a floor area-scaled average (by foundation type) for remaining houses

Mechanical Ventilation Not available. Assumed 2 hours /day at 50 cfm.

Non-Lighting Internal Gains Not available. See next slide for details.

Lighting Internal Gains LPD available for each house; assumed 1.5 hours/day.

T-stat Setting Available based on interviews, but used this as the “calibration knob”.


Detail: Non-Lighting Internal Gains• Equation:

• Based loosely on Building America Benchmark*– Used the original equation and values (averaged) to determine average internal

gains for RBSA homes.• Original equation also includes Number of Bedroom and Finished Floor Area terms

– Set Number of Bedrooms and Finished Floor Area terms to zero and adjusted Number of People term to achieve same average internal gains for RBSA homes.

• Building America Benchmark based on– “The appliance loads were derived by NREL from EnergyGuide labels, a Navigant

analysis of typical models available on the market that meet current NAECA appliance standards, and several other studies. ”

– “The general relationship between appliance loads, number of bedrooms, and house size, was derived empirically from the 2001 RECS. ”


*Hendron, Robert. "Building America Research Benchmark Definition, Updated December 20, 2007." NREL/USDOE EERE. January 2008. NREL/TP-550-42662

Realistic SEEM Simulations Not Feasible/Possible for All Homes in RBSA; Some Homes were Filtered Out

Filter # of Sites

More than one foundation type 331

25% > Ceiling Area to Floor Area > 200%, or Missing Ceiling U-value 36

Footprint Area to Floor Area < 20% 36

30% > Wall Area to Floor Area > 200%, or Missing Wall U-value 24

Missing Floor U-value for Crawlspace Foundation 5

Window Area = 0 3

Window u-value = 0 3

• Resulting House Count: 1011– These issues overlap on some houses, so the sum of

the counts cannot be subtracted from 1404 to get 1011.


From Total Cumulative

SEEM Run Realistic Inputs See Previous Slide 393 393 1011

Primary Heating System

eZonal, eFAF, gFAF, HP

Removes gas boilers, wood stoves, etc. 282 190 821

Secondary Heating System Fuel

Electric or GasRemoves wood stoves, propane heaters,

etc.393 233 588

Reported Non-natural gas or non-electric Fuel Use

0Screens out houses with wood, oil, propane, etc. consumption because

billing analysis not performed.475 80 508

Billing Energy Use;Bad SEEM Runs

Billing > 1,500;SEEM > 0

Billing Screen: Intends to screen out partially used or unused houses.SEEM Screen: runs must be valid.

55 31 477

VBDD R2 (electric) ≥0.45 224 8 469

VBDD R2 (gas) ≥0.45 80 9 460

VBDD Balance Point Temp. (elec.)

< 70 97 25 435

VBDD Balance Point Temp. (gas)

< 70 21 6 429

# Houses Filtered OutNotesValues to KeepVariable

Screens out potentially invalid billing analysis results. Screens only apply when:

Electric: electric billing use / (electric billing use + gas billing use) > 30%

Gas: gas billing use / (electric billing use + gas billing use) > 30%

Houses Remaining

Data Filters Excluded some RBSA Homes

Note: Gas Billing converted to kWh/year using reported AFUE Methodology - Data - 20

0

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Billi

ng H

eatin

g E

nerg

y Es

timat

e (k

Wh/

hr)

SEEM Heating Energy Estimate (kWh/yr) Methodology - Data - 21

Final Data Setn = 429

Overview



Methodology – Regression - 22

Regression Overview• Analysis

Identify and quantify any systematic patterns (trends) in the differences between SEEM(68°F) and Billing Analysis heating use estimates (∆ kWh = SEEM kWh ‒ Billing Analysis kWh)

• Systematic means “explained by known variables.” (Example: SEEM(68°F) kWh tends to exceed Billing Analysis kWh in cooler climates.)

• Tacit assumption: Billing Analysis estimates roughly unbiased.• Definitions

• “Billing Analysis kWh” = Heating energy use estimated using the variable-base degree day method; given in RBSA SF dataset.

• “SEEM(68°F) kWh” = Heating energy use via SEEM runs using house-specific characteristics data from the RBSA SF dataset with thermostat set to 68°F


Regression Overview• Problem is multivariate… – A single underlying trend (example: ∆ increasing with

heating costs) may appear in multiple guises (∆ increasing with HDD, or with U-value, or with building heat loss)

• Approach is multiple regression…– Compare Billing Analysis kWh with SEEM kWh when SEEM

is run with a constant T-stat setting (68°F day, 68°F night.)– Y-variable is the percent difference between SEEM(68°F)

kWh and Billing Analysis kWh.– X-variables are physical characteristics known through

RBSA. (Specifying the x-variables is a large part of the work of setting up the regression.)


Setting up the Regression1. The regression is not a physical model – it is intended to capture unknown

effects.2. The y-variable must capture the differences between SEEM kWh and Billing

Analysis kWh.3. Need to deal with Heteroskedasticity.4. Need to acknowledge substantial measurement error (random noise in both

SEEM and Billing Analysis.5. Identify x-variables that “lead to” systematic differences between SEEM(68°F)

kWh and Billing Analysis kWh.A. Process is iterative: A variable may be weakly correlated with raw y-values but strongly

correlated with y’s that have been adjusted to account for some other variable’s influence.

B. Colinearity is to be avoided. Example: Including both heat loss rate and vintage.C. Pursuing Parsimony: don’t include too many variables.D. Some variables (duct tightness, infiltration) aren’t known for all houses.E. Prominent candidates would have characteristics that likely influence differences

between SEEM(68°F) and Billing Analysis estimates (i.e.: Thermal efficiency drivers (U-values, duct tightness, infiltration), Heating system type, Climate (HDDs)


Steps to Generating the Regression1. Define y-variable2. Identify candidate x-variables

– Consideration of physical “common sense” important– Tools:

• Correlation between y-variable and candidate• y-variable plotted vs. candidate

3. Run regression; Check results– Rule of thumb: x-variable “checks out” ok if p-value < 0.05 and no systematic

pattern is evident in a plot of the residuals against the x-variable. If it does not check out, the variable should be dropped or reformulated to reflect the pattern in the residuals.

4. Look for other x-variable candidates– Use same tools, but apply to regression-adjusted values

5. Iterate


• The y-variable

• The chosen x-variables (all indicator variables)– Electric Resistance Heating System Type

• Value of 1, if Heating System type = Electric Zonal or Electric FAF; Otherwise, value of 0 (if Heating System type = Gas FAF or Heat Pump).

– Poor Wall/Ceiling Insulation• Value of 1, if Wall u-value > 0.20, or Ceiling u-value > 0.20; Otherwise,

value of 0.– Poor Floor Insulation

• Value of 1, if Floor u-value > 0.25, and Foundation type = vented crawlspace; Otherwise, value of 0.

– Climate Zone (2 variables)• Heating Zone 2 = 1, if 6000 < HDD65 < 7500; Otherwise value of 0.

• Heating Zone 3 = 1, if HDD65 > 7500; Otherwise, value of 0.

Final Regression Definitions


Variable Estimated Coefficient

Standard Error p-value

Intercept -0.05 0.03 0.05

Electric Resistance 0.28 0.04 0.00

Poor Ceiling or Wall Insulation 0.29 0.06 0.00

Poor Floor Insulation 0.16 0.05 0.00

Heating Zone 2 0.07 0.06 0.24

Heating Zone 3 0.18 0.08 0.02

Regression Results

Methodology – Regression - 28Adjusted R-square = 0.18

𝑦=𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡+𝛽𝑒𝑙𝑒𝑐 .𝑟𝑒𝑠𝑖𝑠× 𝐼𝑒𝑙𝑒𝑐 .𝑟𝑒𝑠𝑖𝑠+𝛽𝑝𝑜𝑜𝑟 .𝑖𝑛𝑠 .𝑐𝑒𝑖𝑙 .𝑤𝑎𝑙𝑙× 𝐼𝑝𝑜𝑜𝑟 .𝑖𝑛𝑠 . 𝑐𝑒𝑖𝑙 .𝑤𝑎𝑙𝑙+𝛽𝑢𝑛𝑖𝑛𝑠 . 𝑐𝑟𝑎𝑤𝑙× 𝐼𝑢𝑛𝑖𝑛𝑠 . 𝑐𝑟𝑎𝑤𝑙+𝛽𝐻𝑒𝑎𝑡𝑖𝑛𝑔𝑍𝑜𝑛𝑒2× 𝐼𝐻𝑒𝑎𝑡𝑖𝑛𝑔𝑍𝑜𝑛𝑒 2+𝛽𝐻𝑒𝑎𝑡𝑖𝑛𝑔𝑍𝑜𝑛𝑒 3× 𝐼𝐻𝑒𝑎𝑡𝑖𝑛𝑔𝑍𝑜𝑛𝑒 3

Other prominent x-variables considered (but not included)

• Insulation interaction term– Relationship too poor to include (low p-value)

• Duct Leakage– Too little data to support inclusion

• Infiltration– Didn’t show a trend

• Billing Analysis’ variable-base heating degree days– Its inclusion would result in circular logic


Translating the Coefficients


• The y-variable in the regression has Billing Analysis kWh tied to it.

• We want to know what factor to multiply SEEM(68°F) by to get a “calibrated” value.

• A little algebra gets us there:

• Here, is the expected y-value for a house with a given set of x-variable values.

𝐴𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡 𝐹𝑎𝑐𝑡𝑜𝑟=(1− �̂�2 )

( �̂�2

+1)

Specific Example (House ID: 21233)• Intercept (applies to all houses)

– Intercept Term= -0.06• Gas FAF

– Electric Resistance Term = 0.00• Ceiling u-value: 0.06; Wall u-value: 0.08

– Poor Ceiling or Wall Insulation Term = 0.00• Floor u-value: 0.23

– Poor Floor Insulation Term = 0.16• Heating Zone: 2

– Heating Zone 2 Term= 0.07– Heating Zone 3 Term = 0.00

• Expected y-value: = 0.17

• Adjustment Factor = = 0.84

• This means we would multiply SEEM(68°F)ID:21233 by 0.84 to get a “calibrated” SEEM heating energy use value for that house.


Regression ResultsAdjustment factors for all possible cases


0%

20%

40%

60%

80%

100%

120%

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Gas/HP Electric Resistance Gas/HP Electric Resistance Gas/HP Electric Resistance

Heating Zone 1 Heating Zone 2 Heating Zone 3

Adju

stm

ent F

acto

r to

App

ly to

SEE

M(6

8°F)

0.84

Example Case

Overview



Methodology – Calibrate - 33

T-Stat Calibration• We then need to translate the adjustment factors into “calibrated” SEEM

thermostat settings.• Method:

1. Run SEEM for each house at multiple temperature settings in 2 degree increments– Daytime Settings: … 58, 60, 62, … – Nighttime Setting = Daytime setting – Average Setback(heating system)

» Average Setback: Use average difference between reported daytime and nighttime t-stat settings in RBSA dataset; by heating system type:

2. Determine relationship of calibration adjustment factors to temperature settings for each of the 24 scenarios.

3. Interpolate to determine “calibrated” t-stat settings.4. Note: 5 of the 24 possible scenarios have n=0 houses. In those cases, the average ratio

of daytime temperature between the next zone was used to determine the temperature setting for that scenario.


Heating System Type Avg Setback (°F)

Electric FAF 6.0 Electric Zonal 4.8 Heat Pump 4.3 Gas FAF 4.8

Step 1: Run each house in SEEM at multiple t-stat’s w/setback


0

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54 56 58 60 62 64 66 68 70 72

SEEM

Hea

ting

Ener

gy U

se E

stim

ate

(kW

h/ye

ar)

Daytime Temperature Setting (°F)

Site-Specific SEEM Runs (Case: GasFAF, GoodCeilingsWalls, BadFloors, HZ2)

- Each line represents the SEEM runs with setback for one of the 12 individual houses within this case .- Each triangle represents the SEEM(68°F) run for that house.

Step 1a: Take the Average


0

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54 56 58 60 62 64 66 68 70 72

SEEM

Hea

ting

Ener

gy U

se E

stim

ate

(kW

h/ye

ar)


Site-Specific SEEM Runs (Case: GasFAF, GoodCeilingsWalls, BadFloors, HZ2)

- Each line represents the SEEM runs with setback for one of the 12 individual houses within this case .- Each triangle represents the SEEM(68°F) run for that house.

Step 2: Determine case relationship for each t-stat setting:avgSEEM(t-stat with setback) avgSEEM(68)


0.00

0.10

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0.40

0.50

0.60

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0.80

0.90

1.00

1.10

1.20

54 56 58 60 62 64 66 68 70 72

Adju

stm

ent F

acto

r to

"Cal

ibra

te" S

EEM

(68°

F)


Adjustment Factor (Case: GasFAF, GoodCeilingsWalls, BadFloors, HZ2)

Process Check: Comparing the case Average with the individual housesavgSEEM(t-stat with setback) avgSEEM(68)


0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

54 56 58 60 62 64 66 68 70 72

Adju

stm

ent F

acto

r to

"Cal

ibra

te" S

EEM

(68°

F)



0.00

0.10

0.20

0.30

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0.50

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0.70

0.80

0.90

1.00

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54 56 58 60 62 64 66 68 70 72

Adju

stm

ent F

acto

r to

"Cal

ibra

te" S

EEM

(68°

F)



Target Adjustment Factor(from regression):

0.84

Adjustment factor = SEEM(t-stat with setback)/SEEM(68)Methodology – Calibrate - 39

66.8

Step 3: Determine case’s calibrated t-stat setting

50

55

60

65

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75

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

GoodFloor

PoorFloor

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Good Ceilingor Wall

Poor Ceilingor Wall

Gas/HP Electric Resistance Gas/HP Electric Resistance Gas/HP Electric Resistance

Heating Zone 1 Heating Zone 2 Heating Zone 3

"Cal

ibra

ted"

Day

time

Ther

mos

tat S

etting

(°F)

Proposed “Calibrated” Thermostat Settings

Note: Categories with transparent bars had zero houses. Methodology – Calibrate - 40

66.8

Example Case

Heating System Type Avg Setback (°F)

Electric FAF 6.0 Electric Zonal 4.8 Heat Pump 4.3 Gas FAF 4.8

Overview



Discussion - 41

Next Steps• If the RTF agrees it’s calibrated, the RTF will be able to use SEEM94 to help

estimate energy savings for residential single family– Heat Pump

• Conversions• Upgrades• Commissioning, Controls, and Sizing

– Weatherization• Insulation• Windows• Infiltration reduction

– Duct Sealing– New Home Efficiency Upgrades

• “Help” is used here because we will still need to deal with “non-electric benefits” for these measures.– This topic is out of scope for today’s discussion. The goal today is simply to determine

whether SEEM has been calibrated to provide reliable results.

Discussion - 42

Discussion

• Proposed Decision: SEEM94 is “calibrated”; it will give reliable heating energy consumption results – for single family houses with the following

characteristics: • Heating System is one or more of the following: Gas FAF,

Electric FAF, HP, zonal electric (no other heating system type); • Occupied/normal houses (PRISM worked);

– if the following inputs are used:• Calibrated Thermostat Settings (see slide above); and• Internal Gains:

Discussion - 43

Overview



Discussion - 44

Proposed Motion

“I _______ move that the RTF consider SEEM94 calibrated for single family houses.”

Proposal - 45

SEEM 94 Calibration to Single Family RBSA Data Analysis and proposed actions

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