Creating a DHP UES Measure

1. Creating a DHP UES Measure Ecotope, Inc. July 16, 2013

2. Agenda Introduction • Overview Study populations Simulation Calibration • SEEM thermostat settings Savings Analysis • Input Assumptions • Savings Estimates Discussion

3. Introduction

4. Overview / Review • Key findings presented to RTF at 21 May 2013 meeting • Technical Potential: • Measure targets ER zonal houses only • RBSA reports 388,847 ER zonal houses across PNW • Estimate 3,000 kWh/yr • Technical Potential ≈ 133 MWa

5. Data Sources & Study Populations

6. Data Sources & Study Populations • 3 Main Populations and Data Sources: • RBSA Single Family Houses (n ≈ 1,400) • Only 170 houses with zonal electric resistance heat as primary heating source • DHP Pilot Study Billing Analysis (n ≈ 4,000) • DHP Metered Group Houses (n = 95) • A subset of the full pilot billing analysis

7. Populations Compared • Compare Populations • All populations screened to exclude supplemental fuels • 90% confidence interval • RBSA regionally representative • DHP Pilot skewed to Zone 1 • DHP Metered skewed to Zones 2 and 3

8. Populations Compared Heating energy use divided by conditioned floor area (heating EUI) across zones and populations Conclusion: Use RBSA characteristics • Statistically relevant across entire region • Baseline energy use in line with DHP studies • More detailed characteristics

9. Calibrating Thermostat Settings in SEEM

10. Thermostat Settings • Two Options • Use calibrated (from RBSA dataset) ER settings for both baseline and DHP simulations: (Note: these are for houses with good insulation levels) (2) Use calibrated (from RBSA dataset) ER settings for baseline and different DHP settings: • Calculate thermostat adjustment (“takeback”) from metering data

11. Thermostat Calibration • Targets: • Energy use calibration targets from the metered group • Billing analysis provides heating energy estimate for pre-DHP installation period • Meters record direct heating energy use for post-DHP installation period • Simulation Results: • SEEM matches the targets if certain thermostat settings used

12. Calibration Method • Approach • Create individual simulations for each house • Set points vary by heating zone, HVAC type, and house insulation levels • In a similar way to RBSA calibration at 21 May 2013 meeting. • Calibrate settings to metered data by varying the thermostat T-stat settings Iterate until simulation output matches targets 95 SEEM Simulations – one for each house

13. Calibration Outcomes & Observations Outcome • The metered 95 dataset suggest using the following T-stat settings: Observations • ER-only houses needed higher set points than those from RBSA calibrated dataset to match targets • DHP houses needed a different (higher) set point than the ER baseline houses to match the metered energy use target

14. Translation to General Housing Population Simulate ER zonal baseline with set points from RBSA calibration • These are not the set points that came out of the calibration of the DHP95 metered houses. That population and the RBSA “general” population differ enough to suggest different Tstat calibrations. We opt to use the RBSA source because it better represents the broader population. • RBSA SEEM calibration for ER zonal heat with good wall/ceiling/floor insulation: Simulate DHP houses using a changed (increased) set point equal to the delta found from the DHP95 dataset • What is the set point delta? How much is the takeback?

15. Calculating Takeback • Takeback for Good Ceiling/Wall/Floor Case • Subtract Electric Resistance Zonal set point from DHP set point

16. Thermostat Settings with Takeback Start with RBSA-derived ER zonal set points for baseline. Then add takeback to produce DHP set points. • 9hr night time setback: ER 4.8F, DHP 4.3F

17. Thermostat Settings: Conclusion • Two Options (1) Settings for both baseline and DHP (2) Settings for baseline and takeback for DHP

18. Simulation Inputs: Prototype Sizes & Insulation Levels

19. Prototypical House Matched to RBSA • RBSA House Prototype Summary Process • Use RTF prototypes to make RBSA bins

20. Prototypical House – Distribution • RBSA Prototype Summary • Floor area - different study groups: • RBSA Zonal Electric Resistance: 1568 ft2 • DHP Pilot Study: 1533 ft2 • DHP Metered Group: 1610 ft2 (categorical data) (continuous data)

21. Prototypical House – Insulation Levels • RBSA U-Value Summary Process • Subset for single-family homes with electric zonal primary heat • RBSA gives current conditions

22. What the book says • Guidelines for the Estimation of RTF Savings, 16 April 2013 • 2.3.3.4. Interactions between Measures • In many cases, the savings of one measure depends on whether another measure is present…. 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. An alternative estimate of UES may be prepared using different assumptions about what other measures have already been implemented. If an alternative is developed, both UES estimates must be presented to the RTF along with the justification for which should be used. The measure’s sunset date may be based on the rate of implementation for the other interactive measures.

23. Prototypical House – Insulation Levels • Insulation Levels Now vs Later • Current conditions found from RBSA • Cost Effective Limit (CEL) for “last-in” • Assume all homes fully weatherized by end of measure (85% achievable) • From RBSA, some homes already at or above goal • Homes near goal not cost effective • Forecast is somewhere between? • 25% in 15 years?

24. Prototypical House – Insulation Levels • Calculate new CEL from RBSA Database • Each component assigned insulation levels • Apply the following logic to data • Incorporate 85% achievable rate • Re-summarize

25. Prototypical House – Insulation Levels

26. Prototypical House Insulation Summary • Insulation Summary Current 15 Years From Now Reference

27. Prototypical House – Forecast • Forecast Case – Why 25% • Assume pre-1980 house has little or no insulation • In 30 years, 65% of these homes still have little or no insulation (from RBSA attic data) • Projecting forward 15 years, 25% additional weatherization could be reasonable

28. Prototypical House – UA • For reference, the DHP Metered Group UA is 514 Btu/hr-°F

29. Energy Savings

30. Energy Savings – No Supplement Fuels

31. Energy Savings – No Screen

32. Energy Savings – With Supplemental Fuels

33. Savings Check: No Supp. Fuel Case • Differences between modeled savings and metered savings • Metered Group Savings: • Source: Ductless Heat Pump Impact & Process Evaluation: Field Metering Report, May 1, 2012, NEEA Report #E12-237 • Current modeling shows regional ave ~4000 kWh/yr for cases w/o supp. fuels • Reconciling Differences (adjustments to current modeling output):

34. Discussion

35. Discussion Questions Is the SEEM DHP calibration appropriate? • Should we use the electric resistance settings from the RBSA/SEEM calibration for the baseline? • Is the method to determine the calibrated setting for the efficient-case appropriate? What is an appropriate insulation level to expect over the 15 year lifetime of the DHP measure? • First year savings are the “RBSA” case but what are the 5, 10, and 15 year savings?

36. Extras

37. Thermostats: Final Note • With the simulation, it is possible to run the pre-DHP baseline with the post-DHP thermostat settings and vice versa. Such an approach allows us to compare the energy use without takeback and match the energy savings observed in the field by directly measuring heat output of the DHP.