SEEM Calibration: Revisited

1. SEEM Calibration: Revisited Revising the regression to use continuous heat loss variable Regional Technical Forum December 17, 2013

2. Background SEEM Calibration “Phase I” • Compared SEEM (68°F, day and night) heating energy estimates to billing data estimates. • Restricted to 429 RBSA homes with well-known characteristics, no non-utility fuels, and clear heating signatures in billing data. • Regression used to determine adjustment factors that align SEEM (68°F) with billing data estimates of total heating energy. • Adjustment factors converted to calibrated thermostat settings • Approved by the RTF on May21, 2013. SF RBSA Pie: 1404 Homes

3. Background SEEM Calibration “Phase II” • Independent of Phase I; adjustments apply on top of Phase I adjustments. • Based on billing (VBDD) heating kWh estimates--does not use SEEM estimates. • Identifies variables that drive patterns in electric heating energy among “program-like” RBSA homes. Variables related to: - Non-utility heat sources, - Gas heat sources, and - Phase I filters. • Approved by the RTF on September 17, 2013. • Today’s work applies only to Phase I. It does not affect Phase II. SF RBSA Pie: 1404 Homes

4. Phase I Review (1) • Intended to limit complication in future UES workbooks by choosing variables that correspond with RTF measures. • Wanted to limit to variables well-known through RBSA (e.g., no infiltration). • Regression variables (and adjustment factors) coded as indicator functions. Adjustments for: • Heating equipment, • “Poor” insulation in walls or ceiling, • Uninsulated crawlspace, • Climate Zone.

5. Phase I Review (2) • Regression yields adjustment factors, which are converted to calibrated T-stat values. • Factors converted to calibrated T-stat values using SEEM T-stat sensitivity curves…

6. Adjustment factors T-stat conversion 75% 64⁰ (Day) Calibrated T-stat values

7. Why are we revisiting this? Applications more diverse than appreciated in May. Basicproposal isto trade in some simplicity for realism. • Regression Variable(Main proposed change). Replace insulation step functions with continuous heat loss function. New function treats heat loss from different sources equally: • Magnitude of heat loss matters but path does not; • Includes loss via infiltration (imputed for homes w/o blower door test); • Small changes yield small calibration adjustments (no threshold effects). • T-stat Role (Secondary proposal). Apply adjustment factors directly, rather than converting to thermostat adjustments. • Concern is that thermostat “calibration knob” might bias results; • Adjustment factors would be relative to SEEM (69°F day / 64°F night) rather than SEEM (68°F day / 68°F night).

8. Changing Role of T-Stat (1) • Current calibration begins with SEEM Input = 68°F day/night • This arbitrary value didn’t affect the results much since adjustment factors were converted to t-stat settings. • Proposal would begin with SEEM Input = 69°F day, 64°F night • Valuesbased on survey results from RBSA (not arbitrary). • Not much difference by heating system type, so the same rounded number used for all. • Values would become standard SEEM input (adjustment factors would be applied to output).

9. Changing Role of T-Stat (2) What if we calculate adjustments relative to SEEM (68/68) and SEEM (69/64) and then convert adjustments into t-stat values? Little difference in the end results.

10. Regression Revision (1) Main work is in developing heat loss variable. • Infiltration loss based on CFM-Natural; • CFM-Nat is a SEEM input, derived from blower-door test data; • Blower door tests for about 1/3 of RBSA houses; • Regression-based “averages” for homes w/o blower door tests; • Calculations and regression based on RTF guidance. • Convert infiltration loss to same units as conductive heat loss; add heat loss rates together;normalizeby surface area. • Result is called “Uo-Both”.

11. Regression Revision (2) Developing the heat loss variable... Ran preliminary regressions to see if any additional transform is needed. • Effect very pronounced in the low range of U-values, but going from fairly high heat loss to very high heat loss has little effect. • Final proposed heat loss function equals Uo-Both up to a point, but stays constant beyond that point. • Cut-off value is 0.20 in Z1, 0.175 in Z2, 0.15 in Z3.

12. Regression Revision (3)

13. Regression Revision (4) Example: Home in Zone 1 with Electric-Resistance heat and moderately insulated walls and floors.

14. Comparing Regression Results Z1 Elec. Resistance Current and Proposed Adjustments (Example) R30 – Current R30 (Proposed) R5 (Proposed) R5 - Current

15. Comparing Regression Results Z1 Elec. Resistance Current (unins. wall-ceiling/ unins. crawl) and Proposed Proposed Current 0/0 Current 0/1 Current 1/0 Current 1/1

16. Comparing Regression Results Elec. Resistance Current (R0) and Proposed (R3) All Heating Zones

17. Regression Revision (5) Effect on UES Calculations • Insulation • Current: Different pre/post adjustments for only the cases where Uninsulated  Insulated • Proposed: Different pre/post adjustments for nearly all the cases, even new construction • Windows, Air Sealing • Current: No change in pre/post adjustments. • Proposed: Different pre/post adjustments for nearly all the cases, even new construction • Duct Sealing, Heat Pump Upgrades, and Heat Pump CC&S • Current:No change in pre/post adjustments. • Proposed: No change in pre/post adjustments. • (Central) Heat Pump Conversions • Current: Different pre/post adjustments. • Proposed: Different pre/post adjustments. • DHPs (not a part of this analysis) • Measure Interactivity • Old Method: Adjustment factors vary only when components are uninsulated. • Proposed Method: Adjustment factors are different for each “characteristic scenario”.

18. Bottom Line… • Regression / Heat Loss VariableProposal. Staff sees benefits in the new heat loss function: • Heat loss due to infiltration treated the same as conductive loss; • All forms of conductive loss treated the same; • Small changes yield small calibration adjustments (no threshold effects). Drawbacks are added complication and overhead related to making a change. • T-stat Proposal. Staff is neutral on this one. What do you believe is really driving differences between SEEM and billing data? • If it’s really T-stat settings, then it’s best to implement adjustments via thermostat calibration; • If it’s something else, then adjustment factors are probably better—thermostat calibration could bias some results.

19. Decisions • “I ______ move that the RTF, in its single family calibration method: (choose one) • Switch to using a function based on continuous Uo, as presented. • Continue using the existing step-functions.” • “I ______ move that the RTF, in its single family calibration method: (choose one) • Switch to using adjustment factors directly, along with pre-assigned thermostat setting inputs of 69F day and 64F night. • Continue using ‘calibrated’ thermostat settings.”

20. Additional Slides…

21. Comparing Regression Results Elec. Resistance Current (R0) and Proposed (R3) All Heating Zones

22. Comparing Models in T-stat terms Elec. Resistance Current (R0) and Proposed (R3) All Heating Zones

23. Comparing Regression Results Gas/Heat Pump Current (R0) and Proposed (R3) All Heating Zones

24. Comparing Models in T-stat terms Gas/Heat Pump Current (R0) and Proposed (R3) All Heating Zones