Residential Single Family Weatherization and HVAC Measures

1. Residential Single Family Weatherization and HVAC Measures Progress Reports: Estimating Electric and Supplemental Fuels Savings Estimating Value of Emissions Savings Regional Technical Forum June 18, 2013

2. Estimating Electric and Supplemental Fuel Savings

3. Reminder • The SEEM Calibration applies to a specific sub-set of the RBSA homes. • 30% of the 1404 RBSA homes were used in calibration, the rest weren’t included because: • Incapable of running in SEEM (foundation type, etc.) • Non-utility fuel use or equipment (wood, oil, etc.) • Poor billing analysis results • Note: Gas-heated homes were included in the calibration SF RBSA Pie: 1404 Homes

4. SEEM is Calibrated … Now What? • The next step is to “bring back into the analysis” the houses we expect to come in under utility programs. • Utility Program Requirement: Permanently Installed Electric Heat • No gas, oil, etc. primary heating systems (FAF or Boiler) • Heat stoves and fireplaces are ok (any fuel) • Adjustments Needed • Non-Utility Heating Fuels • Gas Heat Use • Some houses with “Permanently Installed Electric Heat” use gas (i.e. fireplaces) • Remaining Calibration Filters SF RBSA Pie: 1404 Homes

5. 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: Intent: this product should “reliably” estimate average electric heating kWh for the target population(s).

6. Overview (cont.) • A fundamental 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);

7. Methodology Starting point: Easiest approach would be to calculate a single adjustment factor as a simple ratio, 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, ...). 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).

8. 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 to be provided in a self-contained report. • Today’s focus: • General framework; • How we use regression results to estimate adjustment factors; • Uncertainty and limitations.

9. Regression Summary(Model fitto RBSA sites with permanently installed electric heating system and without non-electric central heating systemsand with Electric Heat > 0 kWh/yr) Adjusted R2 = 0.27

10. Interpretation • Regression coefficients in logarithmic models: • Coefficient of describes elasticity means that a 1% increase in is associated with a 0.63% 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: says that (all else being equal) a house that has a heat pump will average about 22% less electric heat kWh than one that does not. • HDDs, UA, and heat pump presence can be specified in SEEM input. • Want to control for (rather than capture) these characteristics’ effects in calculating adjustment factors. • and heat pump variables included in the model so that their effects are not be attributed to other (possibly correlated) variables.

11. Interpretation (cont.) The following adjustments will be made to SEEM outputs to determine electric and other fuels consumption and savings • Non-Utility Heating Fuels • Adjustment based on C3 and C4 and occurrence of non-utility heating fuels within the population we’re interested in • Gas Heat Use • Adjustment based on C5and C6and occurrence of gas heating use within the population we’re interested in • Remaining Calibration Filters • A “filtered out of SEEM calibration for other reasons” variable did not show valid results in the regression, meaning there is no adjustment needed (that we can see) • Electric Heat = 0 kWh/yr • This is a new adjustment, based on the filter applied prior to the regression. • Adjustment based on percentage of population we’re interested in with 0 kWh.

12. Example: All Program-Eligible Houses Step 1 – Determine Adjustment Factors: Step 2 – Use Adjustment Factors to determine Electric Savings, “Wood” Savings, and Gas Savings

13. Many Different Sub-Populations we could Analyze Note: Pass billing screen = True if Total Electric Bill kWh/yr > 4.3 * Square Footage + 1000 (this screen can be edited in the workbook)

14. Can we really differentiate effects by heating zone? (Off-grid fuel example) • These intervals only account for uncertainty in non-utility fuel usage within each group—they do not account for uncertainty related to regression fit.

15. “Wood/Other” Heat Screen • Principles • Has to be “auditable” • Can’t be “How much wood heat do you use”? • Should use data readily available to the utility • Yes • Electric consumption • Square footage • No • Gas usage • UA • Looked at different screens: • Total Bill normalized by square footage • Electric Heat Usage (i.e. PRISM type analysis) normalized by square footage • Didn’t find a good screen definition that showed a significant difference between the adjustments for wood • Note the screen on the previous slide is extreme (only 9,600 kWh of total electric use for a 2,000 ft2 house) and still didn’t show much difference in wood adjustment

16. Discussion • The methodology relies on the space heating behavior of the “Program-eligible” group (green wedge on slide 4) to be similar to the behavior of the “SEEM calibration” group (yellow wedge) • Are we on the right track? • How much should we try to split things up? • All Houses • By Climate • By Measure • By measure efficient and baseline case • By Utility Billing Screen • Combination of the above • Note: The more we split the population, the worse the confidence in the results • For how long should the results be used? • Should we assemble a subcommittee to go through the details and guide the final approach?

17. Estimating Value of Emissions Savings

18. Wood Heat Emissions Valuation • Wood fire produces a large amount of pollution. • The most significant health effect is for small particulates (PM2.5). • Health effects developed over twenty years focused on lung disease (COPD, Emphysema, Cancer) derived for atmospheric exposure • This is among the most significant pollutants from wood smoke.

19. Valuation of health effects from PM2.5 • Primary source used: • http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (EPA, 2013) • The source for woodstove emissions was: • http://www.epa.gov/ttnatw01/burn/woodburn1.pdf (Valenti & Clayton, 1998) • Emission valuation taken as the effect of the incremental particulates added to the atmosphere

20. Emission Value, kWh equivalent

21. Overall Impact on Savings • Valuation of wood savings about ten time avoided cost of electricity • Impact on B/C of individual measures dominated by wood heat offsets • Limited to homes with wood heat • Reduces electric savings but increases B/C ratios • Valuation does not include generation. • Generation reduces emissions offset by 30%

22. National Woodstove Sales Data

23. Woodstove use declining • Air Quality concerns • Emissions regulation in urban areas • Cost of wood rising relative to alternatives • Sales of wood burning device declining nationwide • 75% reduction in 15 years • Improves air quality, increases electric savings

24. Subcommittee • Call for Subcommittee Members for an “Emissions Analysis Subcommittee” • Review the input assumptions to arrive at a method of monetizing wood/other emission savings