SEEM Calibration for Manufactured Homes

1. SEEM Calibration for Manufactured Homes Regional Technical Forum July 15, 2014

2. Overview Same basic structure as single-family calibration. • Phase I: Estimating total heating energy. • Align SEEM with billing data for homes with strong and clear heating energy signatures and no off-grid fuels. • Phase II: Estimating electric heating energy in “typical” program homes. • How is electric heating energy affected by the presence of natural gas and off-grid fuels? • What can we say about electric heating energy in homes with weak or unclear heating energy signatures?

3. Phase I: Total heating energy in “well-behaved” homes

4. Phase I general approach Phase I calibration needed because we don’t have perfect knowledge of SEEM inputs. • Built around “SEEM (69/64).” This has… • Some inputs based on RBSA data (location, wall insulation, heating equipment…) • Others based on convention (thermostat settings, internal gains…) • “69/64” refers to inside air temperatures (64°F day and 64°F night), but T-stat isn’t the only standardized input. • Uses regression to understand differences between SEEM (69/64°F) and billing data (VBDD) heating energy estimates. • Regression results provide adjustment factors needed to align SEEM (69/64) with VBDD.

5. Sneak preview! • Adjustment factors look a lot like the SF calibration factors, but… • They’re smooth (no abrupt change of slope); • The Uo variable has been replaced with a more inclusive heating intensity variable.

6. More sneak preview! Measure: Attic insulation from R8 to R19. Examples are randomly chosen RBSA sites, not measure prototypes.

7. Phase I groundwork Phase I analysis is restricted to homes whose RBSA entries… • Include building characteristics needed to build SEEM inputs, and • Suggest VBDD reasonably estimates total in-door heating energy

8. Phase I data filters, part 1 • Some filters not really related to indoor heating energy, don’t really threaten our total indoor heating energy estimates. • Some have a lot to do with indoor heating energy. Need to accounts for these. • Many “other reason” SEEM failures due to unusually high SLF entries. Staff proposes to included these in baseline SLF averages (but a separate calibration adjustment would be redundant).

9. Phase I data filters, part 2 By the numbers….

10. Regression background Single-family calibration found that the difference SEEM 69/64 - VBDD tends to be more greater (more positive) in homes with… • Poor weatherization (high U-values), • Colder climates, • Electric resistance heat (instead of gas or heat pump). The SF regression estimated these variables’ effects individually -- a separate coefficient for each.

11. The Uo trend for manufactured homes… Observations: 1. Looks similar to SF case. 2. Minor variations in data filters lead to a distinct “dip” around Uo=0.12. (partially caused by a handful of homes with 5+ occupants). 3. Smaller MH sample size makes it very hard to separate different variables’ effects (Uo, equipment, climate, other?). Pre-1992 Post-1992

12. The (SEEM 69/64) heating intensity trend for manufactured homes… Observations: 1. Trend is more clear. 2. Consistent across minor data filter variants. 3. Naturally combines several important variables (Uo, equipment, climate, etc.) 4. Steeper drop at far left due to combined effects of efficient equipment, good weatherization, mild climate.

13. Wait, what? SEEM (69/64) is part of the dependent variable, Is it okay for it to also be an explanatory variable? • Goal is to estimate typical VBDD values based on variables that are known to us when we build measure workbooks. • SEEM (69/64) is always known (or knowable) to us, so we can use it however we like in our models.

14. Capturing the (approximate) heating intensity trend 1. Can’t represent loess smoother (black curve) in simple Excel formula. 2. Splines would work, but aren’t necessary… 3. A cubic polynomial (blue curve) captures the trend very well. 4. SF calibration used a piecewise-linear function for this. That works okay but has caused some headaches.

15. Regression model fit (v1) • Dependent variable is (SEEM – VBDD)/SEEM. • Energy intensity variable gives the regression a chance to take care of climate, heating system, and heat loss all at once. • Still had to check to see if the regression treated these variables “fairly”. • Some equipment variables still needed to be included. • Use caution interpreting these variables’ coefficients.

16. Regression model fit (v1) Curviness at right end isn’t really data-driven. (It’s caused by the polynomial form of our model, not a pattern in the data.) May be better to force the graph to flatten to the left of the local max (x≈14). See v2.

17. Regression model fit (v2) Idea is to preserve the shape of the data-driven portion of the v1 polynomial (left part). Method: Define a new variable that equals the v1 polynomial up to the local max, then stays constant to the right. Fit new regression replacing polynomial terms with new variable. Cubic polynomial (v1 output) New curve (v2 input)

18. Regression model fit (v2) Write = SEEM (69/64)/sq. ft. V1 estimates the percent difference as… For , the V2 estimate is… For , the V2 estimate is…

19. Regression model fit (v2) Almost identical to v1 to the left of x = 14.6. Main change is that it’s flattened out to the right. Smallest SEEM.69/sq. ft. values by heat source: HP: 1.8, 1.8, 1.8, 1.8, 2.0 ER: 3.8, 3.9, 4.1, 4.4, 4.6 Gas:5.4, 6.5, 6.7, 7.3, 7.5

20. Phase I Adjustment Factors (v2) Note: Only have about 15 points with SEEM/ft2 < 4. Need to take care around lower x-value range. Not safe to extrapolate beyond observed data.

21. Phase I Calculation Example Measure: Attic insulation from R8 to R19. Examples are randomly chosen RBSA sites, not measure prototypes.

22. Phase I Decision “I, __________, move that for existing manufactured homes, the RTF approves the Phase I calibration described above (v2).”

23. Phase I Decision for NC? “I, __________, move that for manufactured-home new construction measures, the RTF approve the Phase I calibration described above, but with the following modifications…”

24. Phase II: Electric heating energy in “program-like” homes

25. Phase II general approach • Phase I gave us total heating energy estimates for homes with clear VBDD signatures. • RTF measure savings needs average electric energy savings for all program homes. • Phase II uses regression to find out… • How the presence of non-electric fuels affects electric heating energy, • How heating energy differs in homes with unclear VBDD signatures. • Regression focuses on TMY-normalized (VBDD) estimates derived from electric billing data.

26. Phase II data filters, part 1 Sites excluded from Phase-II analysis for three reasons. • Don’t have to worry about SEEM input data because Phase II doesn’t use SEEM estimates. • Don’t want to remove sites with non-electric heating fuels or weak VBDD signatures since our goal is to estimate those features’ effects. • Limit sample to “program-like” homes so we can capture dynamics programs are likely to see.

27. Phase II data filters, part 2 By the numbers….

28. Phase II regression fit

29. Phase II adjustments, part 1 All else equal, VBDD-electric in homes with feature differ from that of homes without feature by a factor of Feature associated with less VBDD kWh.

30. Phase II Decision “I, __________, move that the RTF adopt the Phase II calibration for existing manufactured homes as described in the previous slides.”