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SEEM 94 Calibration to Single Family RBSA Data Analysis and proposed actions

SEEM 94 Calibration to Single Family RBSA Data Analysis and proposed actions. Regional Technical Forum May 21, 2013. Overview. Purpose History Methodology Data Regression “Calibrate” Discussion Proposal. Overview. Purpose History Methodology Data Regression “Calibrate”

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SEEM 94 Calibration to Single Family RBSA Data Analysis and proposed actions

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  1. SEEM 94 Calibration to Single Family RBSA DataAnalysis and proposed actions Regional Technical Forum May 21, 2013

  2. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  3. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  4. Purpose: Align SEEM with Measured Energy Use • The SEEM model is used to estimate energy savings for most space-heating-affected residential UES measures using the “calibrated engineering” estimation procedure (see section 2.3.3 of guidelines) • Heat Pumps and Central AC (ASHP, GSHP, DHP) • Weatherization • New Homes • Duct Sealing • Space Conditioning Interaction Factor • Goal: Ensure SEEM94’s results are grounded in measured space heating energy use of single family homes. Use RBSA as source of measured data.

  5. RTF Savings Guidelines

  6. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  7. RTF Decision History

  8. RTF Decision History (Continued) For “model is calibrated” decisions… Calibration Methodology: • Use available house and operation characteristics data from billing/metering studies to develop inputs to SEEM runs; • Adjust SEEM thermostat setting input to achieve a good match (on average) between SEEM output (annual heating energy use) and billing/metering study results. Note: The data sources used were free of (or mostly free of) supplemental fuel usage (wood, propane, oil, etc.) • Collection of reliable electric and gas usage data for space heat consumption is relatively easy compared to other fuels.

  9. SF Calibration to RBSA - Recent History

  10. May 7, 2013 Subcommittee • 3.5 hour meeting • Summary: • The group reviewed an earlier version of this presentation, along with the details of the regression development. • The group gave recommendations and discussed next steps. • Subcommittee Recommendations (staff completed these) • Describe the regression development in a separate report/memo • Correct the uninsulated wall u-value • Re-calculate regression using a binned HDD variable, rather than continuous • Major Issue: Many subcommittee members were very uncomfortable with some of the very low thermostat setting results. • Problem: No alternative calibration method identified. • Conclusion: Move forward with presentation to the RTF.

  11. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  12. Methodology Overview – Data (Step 1) Create two data sources to compare estimates of space heating for homes in RBSA dataset: • RBSA Billing Analysis: Estimates of annual “space heating use” for each house determined by using VBDD • VBDD is a “change-point” regression model which uses billing histories to estimate temperature sensitive use • VBDD analysis is based on monthly billing data (at least 2 years) • SEEM Simulation Analysis: Estimated annual space heating energy use for each house based on SEEM engineering model • RBSA individual home characteristics (e.g., thermal envelope, heating system type, duct tightness) used as model inputs; • Initial model runs use thermostat set to 68°F day & night • SEEM is a one-zone model, so t-stat setting input represents the average for the entire house • Actual t-stat settings are not well documented (occupant reported settings are unreliable, especially for zonal systems) • Thermostat setting will be used (step 3) as the “calibration knob”.

  13. Methodology Overview – Regression (Step 2) Use regression techniques to identify building characteristics that drive systematic differences between SEEM(68°F) and Billing Analysis space heating energy use estimates.

  14. Methodology Overview – “Calibrate”(Step 3) Use regression results to determine thermostat set-point that will align (i.e., “calibrate”) SEEM with Billing Analysis annual space heating use. • Calibration based on comparing average of all SEEM annual estimates to average of all Billing Analysis annual estimates. • Calibration is based on building characteristics identified in regression. • SEEM run for each house at varying “day-time” thermostat settings, with “night-time” thermostat settings based on occupant-reported setbacks.

  15. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  16. Data Sources • Data Source used in this calibration: • Underlying database* for the Single Family Residential Building Stock Assessment (2012) • RBSA study’s database offers recent billing analyses results and detailed house characteristics on 1404 houses in the Region. • This allows well-defined SEEM runs for each individual house. • * Using a pre-release version of the database for this analysis .

  17. Detail: Non-Lighting Internal Gains • Equation: • Based loosely on Building America Benchmark* • Used the original equation and values (averaged) to determine average internal gains for RBSA homes. • Original equation also includes Number of Bedroom and Finished Floor Area terms • Set Number of Bedrooms and Finished Floor Area terms to zero and adjusted Number of People term to achieve same average internal gains for RBSA homes. • Building America Benchmark based on • “The appliance loads were derived by NREL from EnergyGuide labels, a Navigant analysis of typical models available on the market that meet current NAECA appliance standards, and several other studies. ” • “The general relationship between appliance loads, number of bedrooms, and house size, was derived empirically from the 2001 RECS. ” • *Hendron, Robert. "Building America Research Benchmark Definition, Updated December 20, 2007." NREL/USDOE EERE. January 2008. NREL/TP-550-42662

  18. Realistic SEEM Simulations Not Feasible/Possible for All Homes in RBSA; Some Homes were Filtered Out • Resulting House Count: 1011 • These issues overlap on some houses, so the sum of the counts cannot be subtracted from 1404 to get 1011.

  19. Data Filters Excluded some RBSA Homes Note: Gas Billing converted to kWh/year using reported AFUE

  20. Final Data Setn = 429

  21. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  22. Regression Overview • Analysis Identify and quantify any systematic patterns (trends) in the differences between SEEM(68°F) and Billing Analysis heating use estimates(∆ kWh = SEEM kWh ‒ Billing Analysis kWh) • Systematic means “explained by known variables.” (Example: SEEM(68°F) kWh tends to exceed Billing Analysis kWh in cooler climates.) • Tacit assumption: Billing Analysis estimates roughly unbiased. • Definitions • “Billing Analysis kWh” = Heating energy use estimated using the variable-base degree day method; given in RBSA SF dataset. • “SEEM(68°F) kWh” = Heating energy use via SEEM runs using house-specific characteristics data from the RBSA SF dataset with thermostat set to 68°F

  23. Regression Overview • Problem is multivariate… • A single underlying trend (example: ∆ increasing with heating costs) may appear in multiple guises (∆ increasing with HDD, or with U-value, or with building heat loss) • Approach is multiple regression… • Compare Billing Analysis kWh with SEEM kWh when SEEM is run with a constant T-stat setting (68°F day, 68°F night.) • Y-variable is the percent difference between SEEM(68°F) kWh and Billing Analysis kWh. • X-variables are physical characteristics known through RBSA. (Specifying the x-variables is a large part of the work of setting up the regression.)

  24. Setting up the Regression • The regression is not a physical model – it is intended to capture unknown effects. • The y-variable must capture the differences between SEEM kWh and Billing Analysis kWh. • Need to deal with Heteroskedasticity. • Need to acknowledge substantial measurement error (random noise in both SEEM and Billing Analysis. • Identify x-variables that “lead to” systematic differences between SEEM(68°F) kWh and Billing Analysis kWh. • Process is iterative: A variable may be weakly correlated with raw y-values but strongly correlated with y’s that have been adjusted to account for some other variable’s influence. • Colinearity is to be avoided. Example: Including both heat loss rate and vintage. • Pursuing Parsimony: don’t include too many variables. • Some variables (duct tightness, infiltration) aren’t known for all houses. • Prominent candidates would have characteristics that likely influence differences between SEEM(68°F) and Billing Analysis estimates (i.e.: Thermal efficiency drivers (U-values, duct tightness, infiltration), Heating system type, Climate (HDDs)

  25. Steps to Generating the Regression • Define y-variable • Identify candidate x-variables • Consideration of physical “common sense” important • Tools: • Correlation between y-variable and candidate • y-variable plotted vs. candidate • Run regression; Check results • Rule of thumb: x-variable “checks out” ok if p-value < 0.05 and no systematic pattern is evident in a plot of the residuals against the x-variable. If it does not check out, the variable should be dropped or reformulated to reflect the pattern in the residuals. • Look for other x-variable candidates • Use same tools, but apply to regression-adjusted values • Iterate

  26. Final Regression Definitions • The y-variable • The chosen x-variables (all indicator variables) • Electric Resistance Heating System Type • Value of 1, if Heating System type = Electric Zonal or Electric FAF; Otherwise, value of 0 (if Heating System type = Gas FAF or Heat Pump). • Poor Wall/Ceiling Insulation • Value of 1, if Wall u-value > 0.20, or Ceiling u-value > 0.20; Otherwise, value of 0. • Poor Floor Insulation • Value of 1, if Floor u-value > 0.25, and Foundation type = vented crawlspace; Otherwise, value of 0. • Climate Zone (2 variables) • Heating Zone 2 = 1, if 6000 < HDD65< 7500; Otherwise value of 0. • Heating Zone 3 = 1, if HDD65 > 7500; Otherwise, value of 0.

  27. Regression Results Adjusted R-square = 0.18

  28. Other prominent x-variables considered (but not included) • Insulation interaction term • Relationship too poor to include (low p-value) • Duct Leakage • Too little data to support inclusion • Infiltration • Didn’t show a trend • Billing Analysis’ variable-base heating degree days • Its inclusion would result in circular logic

  29. Translating the Coefficients • The y-variable in the regression has Billing Analysis kWh tied to it. • We want to know what factor to multiply SEEM(68°F) by to get a “calibrated” value. • A little algebra gets us there: • Here, is the expected y-value for a house with a given set of x-variable values.

  30. Specific Example (House ID: 21233) • Intercept (applies to all houses) • Intercept Term= -0.06 • Gas FAF • Electric Resistance Term = 0.00 • Ceiling u-value: 0.06; Wall u-value: 0.08 • Poor Ceiling or Wall Insulation Term = 0.00 • Floor u-value: 0.23 • Poor Floor Insulation Term = 0.16 • Heating Zone: 2 • Heating Zone 2 Term= 0.07 • Heating Zone 3 Term = 0.00 • Expected y-value: = 0.17 • Adjustment Factor = = 0.84 • This means we would multiply SEEM(68°F)ID:21233 by 0.84 to get a “calibrated” SEEM heating energy use value for that house.

  31. Regression ResultsAdjustment factors for all possible cases Example Case 0.84

  32. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  33. T-Stat Calibration • We then need to translate the adjustment factors into “calibrated” SEEM thermostat settings. • Method: • Run SEEM for each house at multiple temperature settings in 2 degree increments • Daytime Settings: … 58, 60, 62, … • Nighttime Setting = Daytime setting – Average Setback(heating system) • Average Setback: Use average difference between reported daytime and nighttime t-stat settings in RBSA dataset; by heating system type: • Determine relationship of calibration adjustment factors to temperature settings for each of the 24 scenarios. • Interpolate to determine “calibrated” t-stat settings. • Note: 5 of the 24 possible scenarios have n=0 houses. In those cases, the average ratio of daytime temperature between the next zone was used to determine the temperature setting for that scenario.

  34. Step 1: Run each house in SEEM at multiple t-stat’s w/setback - Each line represents the SEEM runs with setback for one of the 12 individual houses within this case . - Each triangle represents the SEEM(68°F) run for that house.

  35. Step 1a: Take the Average - Each line represents the SEEM runs with setback for one of the 12 individual houses within this case . - Each triangle represents the SEEM(68°F) run for that house.

  36. Step 2: Determine case relationship for each t-stat setting:avgSEEM(t-stat with setback) avgSEEM(68)

  37. Process Check: Comparing the case Average with the individual housesavgSEEM(t-stat with setback) avgSEEM(68)

  38. Step 3: Determine case’s calibrated t-stat setting Adjustment factor = SEEM(t-stat with setback)/SEEM(68) Target Adjustment Factor (from regression): 0.84 66.8

  39. Proposed “Calibrated” Thermostat Settings Example Case Note: Categories with transparent bars had zero houses. 66.8

  40. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  41. Next Steps • If the RTF agrees it’s calibrated, the RTF will be able to use SEEM94 to help estimate energy savings for residential single family • Heat Pump • Conversions • Upgrades • Commissioning, Controls, and Sizing • Weatherization • Insulation • Windows • Infiltration reduction • Duct Sealing • New Home Efficiency Upgrades • “Help” is used here because we will still need to deal with “non-electric benefits” for these measures. • This topic is out of scope for today’s discussion. The goal today is simply to determine whether SEEM has been calibrated to provide reliable results.

  42. Discussion • Proposed Decision: SEEM94 is “calibrated”; it will give reliable heating energy consumption results • for single family houses with the following characteristics: • Heating System is one or more of the following: Gas FAF, Electric FAF, HP, zonal electric (no other heating system type); • Occupied/normal houses (PRISM worked); • if the following inputs are used: • Calibrated Thermostat Settings (see slide above); and • Internal Gains:

  43. Overview • Purpose • History • Methodology • Data • Regression • “Calibrate” • Discussion • Proposal

  44. Proposed Motion “I _______ move that the RTF consider SEEM94 calibrated for single family houses.”

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