1. Estimating Heating Energy from Utility Bills More on the “Phase 2” Adjustment Ecotope, Inc

2. BACKGROUND MATERIAL AND COMMENTS ON VBDD

3. What is Variable Base Degree Day Regression (VBDD)? Base Load Energy = Base Load + Heating Slope * Heating Degree Days Degree Day Base Heating Slope Sometimes it works very well (like for example this home)…

4. And other times it doesn’t work at all.

5. Site level VBDD seems to work pretty well on gas bills! It does not seem to work very well across electric bills. These are all RBSA SF sites, where “Gas n=667” refers to any site with gas bills and “Electric n=699” refers to any site without.

6. Let’s reiterate. Site level VBDD seems to work pretty well on gas bills! It does not seem to work very well on electric bills. Ecotope’s implementation of VBDD here performed a brute-force search over balance points 48F to 70F. Anything that hit the boundary of the search is not believable or usable.

7. So we were looking at all bills before – what about just bills from Josh’s spreadsheet. [These] are homes that have some form of permanently-installed electric heat (FAF, BB, or HP). These are meant to represent an electric utility's program- eligible homes with minimal screening. Shown in vertical dashed line is the cutpoint of 0.45. This looks better than the previous plot for all sites, but it still does not inspire confidence in site-level VBDD with an R^2 cutpoint. BADGOOD

8. Similarly for the balance points, the picture is better for this smaller set of homes, but still doesn’t look good.

9. PRISM: An Introduction. MARGARET F. FELS Center for Energy and Environmental Studies, Princeton University, Princeton, NJ 08544 (U.S.A.) (Received January 1986) In general, the NAC estimate [Normalized Annual Consumption] provides a reliable consumption index from which energy savings and conservation trends may be accurately estimated. The small standard error of NAC for our sample house… is typical of PRISM results. On the other hand, the three parameters, α [base load], β [heating slope], and τ [balance point], which define a house’s energy signature, are less well determined. VBDD, or PRISM, typically works great on total normalized consumption, but gets a bit dicey when you try to pull out the specific parameters such as base load, heating slope, or balance point. The original PRISM people asserted as much:

10. Annualized kWh is the total consumption as reported by bills. Normalized Annual Consumption (NAC) is the weather adjusted total. As seen from last slide, NAC is pretty close to AC. Unlike the normalized heating estimate, NAC doesn’t do crazy things.

11. Back to the R^2 plot, all results from a site-level VBDD investigation must be qualified: BADGOOD Conditional on the R^2 from an unusual regression model exceeding a mostly arbitrary cutpoint of 0.45, here are the savings. What does that even mean? What does that mean for program savings?

12. CORRECTING FOR HOMES THAT GOT CAUGHT IN THE “BILL FILTER”

13. BAD GOOD BAD The Main Idea: We want to estimate heating energy from utility bills. We use the Variable Base Degree Day (VBDD) Regression model to do this. The VBDD model works great on some homes, but does not work great on all homes. To separate the two, we have a “bill filter.” The bill filter decides in a crude way whether the VBDD model was appropriate. It excludes sites with low R^2 or with balance points at the boundary of the search 48-70F. For bill streams set aside in this fashion, we cannot use the VBDD estimates for any purposes. We cannot use the heating estimates from VBDD when we acknowledge that the VBDD model has failed.

14. Back to this example site in which VBDD failed, VBDD cannot tell us anything about the amount of heating energy used at this home.

15. 25% failed bill filter So what can we do? We can look at the difference in annualized energy between the homes that pass the filter and the homes that fail the filter. “Annualized” refers to a weighted average of the observed bills and not a model-based weather normalization. The homes that failed the bill filter used less total energy on average than those that passed the bill filter.

16. CoefficientDescriptionEstimate*Standard Errorp-val β0β0 Intercept10140547<.001 β1β1 Floor Area (ft 2 )3.60.49<.001 β2β2 UA * HDD65 (kWh/yr)0.120.03<.001 β3β3 Heat Pump (TRUE/FALSE)-60750.43 β4β4 Failed Filter (TRUE/FALSE)-279676<.001 100 ft 2 higher area  360 higher annualized kWh 1000 kWh higher UA * HDD65  120 kWh higher annualized kWh Presence of Heat Pump  60 kWh lower annualized kWh (not significant) Failed Bill Filter  2800 lower annualized kWh *Estimated w/ RBSA survey weighting Raw kWhAdjusted kWh Passed Filter1949319449 Failed Filter1788916653 Difference16042796 Adjusted Estimate: 14% less annualized energy

17. Started with primary electric homes Homes with gas usage removed Homes with missing data removed Homes with Tbal 48F set to failed Regression with RBSA Survey weights to adjust for house size, UA, climate, and heat pump Estimate: 14% lower annualized energy in houses that failed the bill filter (19450 vs 16650 kWh / year on average) Assuming houses on both sides of filter use the same proportion of electric use as heat Let X denote heating usage estimated from well-behaved bills Let p denote proportion of well-behaved bills Let C denote a correction factor for misbehaved bills Proposal: C = 1 -.14 = 86%. If we take p=25%, then this would downgrade the usage of 25% of the homes by 14%, for an overall reduction of 0.25*0.14 = 3.5%. Proposed Correction Approach Recap

18. 3.5% 8.3% 35%45% Here are the consequences of the adjustment based on annualized total. It is very sensitive to the assumption of how much heat the “mis-behaved” homes use as a fraction of their total bills. For the well-behaved homes, the heat fraction was estimated as 45%. If you assume the mis-behaved homes also use 45% heat then that leads to a 3.5% overall reduction. As an example, if you assume the mis-behaved homes use 35% heat then that leads to an 8.3% overall reduction. Example Alternate Choice

19. RbsaM Case Studies Summary SiteidReason FailedDiagnosisHeating Energy 10887Tbal 70F and Low R^2Malfunctioning Heat Pump (?)High 11418Tbal 48FProbably realNormal 12507Tbal 70FSpaNormal 13912Tbal 48FNo utility heatingLow 14140Tbal 70FShop (?)Normal 20020Tbal 48FSpaNormal 20230Tbal 70FPossibly realNormal 20469Tbal 48FLarge cooling loadNormal 20998Tbal 48FCooling, Spa, & Well PumpNormal 21143Tbal 48FBad ReadLow 23960Low R^2Bad ReadNormal 24203Tbal 48FProbably realNormal 24375Low R^2Vacations & SpaLow 24684Low R^2Possible Bad ReadsNormal

20. Final Thoughts: When using site level VBDD we must adjust for the homes that got caught in the bill filter – that didn’t show energy use linear in heating degree days. We don’t know anything about how much heating energy those homes used. We know that overall they used about 14% less total energy A crude adjustment based on the 14% less total energy is unfortunately very sensitive to small changes in the assumption of how much heat the “mis-behaved” homes used, something we know nothing about. Large changes require compelling and conclusive evidence. We do not have compelling and conclusive evidence. We recommend, for now, assuming 45% heating energy for the mis-behaved bills and applying an overall downward adjustment of.25*.14 = 3.5% to usage or savings estimates computed from site level VBDD. The 45% heating energy is equal to the fraction of heating observed from the population for which VBDD reports useable results.