Single-Family Heating Energy
SEEM Estimates and RBSA Data
Research in Support of SEEM-Based Savings Estimates for
Weatherization and HVAC Measures Single Family Homes
Heating kWh for Program-Eligible Homes.
How Supplemental Fuels and the Sample
Filters used for SEEM Calibration Affect
Electric Heating Energy
SEEM Calibration Subcommittee
Aug 6, 2013
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2
Reminder: SEEM Calibration (Phase 1)
•
Purpose was to align SEEM heating
energy with RBSA billing data.
– Conclusion: Calibrated SEEM reliably
estimates total heating energy for the type
of homes used in the calibration.
•
975
Calibration based on homes where SEEM
and RBSA can both estimate total heating
energy. Three sample filters applied:
– Omit homes that can’t be modeled in SEEM
due to multiple foundation types, missing
data, etc.
– Omit homes with non-utility fuel use or
equipment (since billing data doesn’t
capture non-utility heat sources)
– Omit homes with poor billing analysis results
(billing data estimates aren’t reliable for
these homes)
(Gas-heated homes were not excluded
from the calibration.)
SEEM Calibration
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Single-Family RBSA Pie: 1404 Homes
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SEEM Calibration: Phase 2
• Purpose is to adjust for electric heating
energy in program-like homes.
• Adjustments need to address:
– Non-utility heating sources;
– Program-permitted gas heat sources (e.g.,
gas fireplaces)
– Other SEEM Calibration filters
• To capture effects on program-like
homes, the present analysis is based on
RBSA homes with:
– At least one permanently-installed
electric heat source;
– No gas, oil, etc. primary heating
systems (FAF or Boiler);
In Utility
Programs, but
not in SEEM
calibration, 423
552
SEEM Calibration
Electric
Heated, 180
Gas Heated,
249
Single-Family RBSA Pie: 1404 Homes
Note that heat stoves and fireplaces
(any fuel) are allowed.
4
Overview
• General approach is to estimate adjustment
factors that account for electric heating energy
differences between the SEEM calibration sample
and program population(s).
• A kWh consumption or savings value will
ultimately be obtained as:
Calibrated SEEM kWh × Adjustment factor(s)
Intent: this product should “reliably” estimate
average electric heating kWh for the target
population(s).
5
Overview (cont.)
• Perennial question: What is the right level of
granularity?
– A single regional true-up factor?
– Separate factors for different subpopulations defined by
geography, program screening criteria, or other variables?
– What can the data reliably support?
• Some known limitations (for the record):
– RBSA data is a snapshot (can’t address changes over time);
– RBSA data is observational rather than experimental (lets
us estimate correlation between building characteristics
and heating energy—not quite the same as estimating
savings caused by program-related measures);
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Methodology
Starting point: Easiest approach would be to calculate a single
adjustment factor as a simple ratio,
Average electric heating energy (program−like sample)
Average electric heating (SEEM calibration sample)
The problem: This captures the two groups’ differences with respect
to all variables that drive heating energy (HDDs, insulation, non-utility
heating energy, equipment, partial occupancy, ...).
Instead, we want adjustment factor(s) to capture some variables’
effects (e.g., partial occupancy, non-utility heating energy). But other
variables (e.g., heating equipment, HDDs, insulation) are specified in
SEEM input. Don’t want to capture these variables’ effects (we want to
control for these variables).
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Methodology (cont.)
• Regression lets us estimate individual variable effects
(when other variables are held constant).
– Staff believes current regression model (next slide)…
• Makes physical sense;
• Faithfully captures main patterns in the data; and
• Is reasonably robust (not overly sensitive to random noise).
– Model development and related technical issues described in
the report.
– Proposed model is “log-scale.” The y-variable is the natural log
of annualized electric heating energy (from RBSA).
• Today’s focus:
– Regression results;
– Adjustment factors derived from the regression results;
– Uncertainty and limitations.
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Regression Summary
(Model fit to RBSA sites with permanently installed electric heating system and without non-electric central heating systems and
with Electric Heat > 0 kWh/yr)
ln ElectricHeat
=
𝐶0 + 𝐶1 ×ln UA×HDD + 𝐶2 ×ln Sq. ft.
+ 𝐶3 ×IHeat.pump
+ 𝐶4 ×IElec.FAF
+ 𝐶5 ×IOff.grid.high + 𝐶6 ×IGas.Heat.High
+ 𝐶7 ×ISEEM.Bill
+ 𝐶8 × ISEEM.Bill + 𝜀
Variable
Definition
Coeff.
Estimate
Std. Error
P-value
Intercept
Intercept
C0
0.127
0.85
0.882
ln UA×HDD
Natural log of UA x HDD65
C1
0.375
0.07
0.000
ln Sq. ft.
Natural log of the area of conditioned floor space
C2
0.446
0.09
0.000
IElec.FAF
Indicator: Has electric forced-air furnace
C3
0.180
0.09
0.047
IHeat.pump
Indicator: Has heat pump
C4
-0.386
0.07
0.000
IOff.Grid.High
Indicator: Non-utility fuel over 40,000 kBtu/yr
C5
-0.481
0.09
0.000
IGas.Heat.High
Indicator: Gas heating energy over 5,000 kWh/yr
C6
-1.021
0.12
0.000
ISEEM.Bill
Indicator: Failed SEEM Calibration billing analysis filter
C7
-0.388
0.07
0.000
ISEEM.Data
Indicator: Failed SEEM Calibration SEEM-input data filter
C8
0.135
0.07
0.041
Adjusted R2 = 0.31
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Interpretation
• Regression coefficients in logarithmic models:
Coefficient of ln UA×HDD describes elasticity
𝐶1 = 0.375 means that a 1% increase in UA×HDD is associated with a 0.375%
increase in electric heating kWh.
Each indicator coefficient estimates (roughly) the factor by which electric
heating kWh typically differs between houses that have the indicated
characteristic and those that do not.
Example: 𝐶4 = −0.386 says that (all else being equal) a house that has a heat pump
will average about 39% less electric heat kWh than one that does not. Based on the
regression, the exact value is exp(-0.386) – 1 ≈ -0.32.
• HDDs, UA, etc. can be specified in SEEM input.
Want to control for (rather than capture) these characteristics’ effects in
calculating adjustment factors.
UA×HDD, sq. ft., electric FAF, and heat pump variables are included in the
model so that their effects are not be attributed to other (possibly
correlated) variables.
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Interpretation (cont.)
Adjustments to Calibrated SEEM output (to obtain electric and other-fuel
consumption for program homes).
• Non-Utility Heating Fuels. Adjustment is based on C5 = -0.481 and rate of
occurrence of high non-utility-fuel users in target population.
• Gas Heat. Adjustment is based on C6 = -1.021 and rate of occurrence of high gas
heat use in the target population
• SEEM Billing Data Filter. Based on C7 = -0.388 and percent of homes with poor
billing data fits (as defined by the filter) in the target population.
• SEEM Data Filter. Based on C8 = 0.135 and percent of homes that fail the SEEM
calibration data filter.
• Electric Heat = 0 kWh/yr.
This is a new adjustment, based on the filter applied
prior to the regression. Adjustment based on percent of homes with 0 kWh (estimated
from billing data).
By default, all rates of occurrence could be taken as the rates observed among program-like
RBSA homes. (E.g., 13.7% of our “program-like” RBSA sample reported high off-grid heat.)
Other values may be justified with external research or more stringent eligibility
requirements.
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Concrete Example
Step 1 – Determine Adjustment Amounts:
I. Percent change II. Percent of
(affected homes) homes affected
III. Net percent
change (I × II)
IV. Adjustment
factor (1+III)
V. Additive
Adjustment
Off-grid high
-38.2%
13.7%
-5.25%
94.8%
-4.93%
Gas heat high
-64.0%
5.9%
-3.78%
96.2%
-3.55%
SEEM Bill Filter
-32.2%
25.4%
-8.17%
91.8%
-7.67%
SEEM Data Filter
14.5%
24.3%
3.51%
103.5%
3.30%
-100.0%
6.6%
-6.55%
93.5%
6.15%
81.0%
81.0%
Zero kWh
Composite adjustment factor:
Step 2 – Allocate kWh Savings. KWh debits due to wood and Gas, plus Electric “grid” savings
Calibrated
SEEM kWh
Base
Case
Efficient
Case
Savings
Adjustments to Obtain Program kWh
Program
kWh
Off-grid
Gas heat
Bill filter
Data filter
Ht. kWh=0
(-4.93%)
(-3.55%)
(-7.67%)
(3.30%)
(-6.15%)
8,000
-394
-284
-614
264
-492
6,479
6,500
-320
-231
-499
214
-400
5,264
1,500
-74
-53
-115
49
-92
1,215
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Quantifying non-electric heat (1)
Interpreting “Wood” debit of 74 kWh:
• Actual Meaning: Because some homes use lots of wood, the
grid will actually see 74 kWh less in savings (on average).
• Not the same as: Average home saves 74 kWh in wood heat
(that’s about 252 kBtu).
• Note: 74 kWh is average over all homes; only 13.7% of RBSA
homes are heavy wood users.
• These have larger debits (around 38% -- Step 1 of previous
slide). For reference, 252 kBtu/0.137 ≈ 1.8 MBtu.
Write ‘Ewood’ for average “Wood” heat efficiency.
Then average “wood” heat savings is 252/E kBtu.
For the record, the calculation was: 252/Ewood = (3.412/Ewood)×74 13
Quantifying non-electric heat (2)
Should we calculate wood heat kBtu as a
constant (e.g., 3.412/ Ewood) times gross kWh?
Matters because gross kWh depends on
equipment type.
• This path would treat wood heat as a percent
of heating energy;
• Alternatively, could treat wood heat as a
percent of heat load.
Staff prefers second option (seems closer to
reality).
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Quantifying non-electric heat (3)
To treat wood heat as a percent of heat load,
use Electric FAF as “reference” heat source.
Then calculate wood MBtu savings separately
for each heat source:
• In E FAF application: kWh × 4.93 × 3.412 / Ewood
• In HP application:
kWh × 4.93 × 3.412 / Ewood × CHP
• In BB application:
kWh × 4.93 × 3.412 / Ewood × CBB
Similar calculations apply to Gas Therm Savings.
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