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The Saga Continues: Measure Interactions for Residential HVAC and Wx measures Regional Technical Forum April 23, 2014.

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Presentation on theme: "The Saga Continues: Measure Interactions for Residential HVAC and Wx measures Regional Technical Forum April 23, 2014."— Presentation transcript:

1 The Saga Continues: Measure Interactions for Residential HVAC and Wx measures Regional Technical Forum April 23, 2014

2 History Mr. Toad’s Wild Ride AGAIN? The way we used to (currently) handle measure interactions: – “Last Measure In” After the calibration, we searched for a better method – First, we recommended “Option 3”, An improvement over Last Measure In allowed by RBSA (set the starting point) and necessitated by the SEEM calibration (energy use non-linear with UA). – Then we flipped to “Participant or Population Data”, Met our “indicators” to the “guiding principle”: first-year and long-term savings, and based on observable data. (Option 3 didn’t meet the indicators.) – Now we’re proposing flopping back to Option 3 More specifically, allowing it for Residential Weatherization and HVAC measures. The Participant and Population Data method still has merit. 2

3 3 Reminder: “Participant or Population Data” Slides from February meeting: For Res Wx/HVAC, method either requires a lot of data, or a lot of guessing.

4 An Attempted Use of Participant Data 4

5 ETO Dataset – 2013 Home Energy Rating (HER) Program 140 of 2,000 audited homes had measures we’re interested in installed (Wx, HVAC) – Data Collected Audits provide insulation bins – Attics: R11 – Walls: R4 – Floors: Insulated or Not – Windows: Single, Double metal, Double wood Measure installs provide more detail – Amount of insulation pre/post, square footage (for the portion of the component worked on) 5

6 Starting Point 6

7 Next Step: Adjust for Concurrent Measures Staff came up with a method and the ETO data can make it work. 7

8 ETO Data: Concurrent Measures 8

9 Adjusted Baseline 9

10 Next Step Average these baselines and apply them to the prototypes to calculate measure savings. But Wait. Can we still average the baselines and perform just one run in SEEM? 10

11 11 Measure: Attic R0 to R38

12 12

13 13 Trouble with the “Participant or Population Data Method” Application of the ETO dataset taught two things about this method: – Analysis Paralysis SEEM calibration requires modeling each house’s starting and stopping condition. Averaging of insulation u-values doesn’t give accurate answers. – Extreme Data Collection Since houses coming into program vary over time, and since program designs vary across programs and over time, we’d likely need data quite often (annually?) to stay true to this method. We need to know fairly well the u-values of each component, and infiltration levels.

14 So Now What? 14

15 15 Renewed Interest in Option 3 … But what’s Option 3, again? Slides from October RTF Presentation:October RTF Presentation

16 Comparing the Methods Selection Criteria Participant or Population Data Method Billing Analysis Method Option 3 Method Usefulness to Programs UES for Individual Measures Yes Maybe (may require larger sample, additional analysis, assumptions) Yes Data Collection Level of Effort High (Detailed whole-house audit on a representative sample of program participants, ongoing collection) Low/Moderate No Data Collection* Analysis Timing1 to 5 years 5 years + Performed ByRTFUtilitiesRTF Level of Effort High (at first, then depends on consistency of data quality/type) Moderate Low (centralized analysis) Accuracy 1 st -year Savings Moderate (depends on quality and representativeness of data; also depends on calibration’s alignment with measures) High (for the participants we didn’t have to throw out); Low (for participants with low statistical reliability.) Low Long-Term Savings Any (depends on whether calibration represents long-term conditions) Any (depends on whether billing period represents long-term conditions) Any (depends on long- term success of achieving “full measure package” and whether calibration represents long-term conditions) 16

17 Slide from February RTF Presentation:February RTF Presentation 17 Wait! What about the “Guiding Principle” and its “Indicators”? Slides from January RTF Presentation:January RTF Presentation

18 Do we need a 4 th “Indicator”? 4 th Indicator: – Reasonable Level of Effort The method isn’t overly burdensome for programs or the RTF; it balances: – an appropriate level of effort for the savings potential, with (Note level of effort includes data collection and analysis efforts today, as well as in the future to maintain the measure) – validity of savings estimate, with – usefulness to the Region. Guidelines allows for this under “Best Practice” – “A best practice savings estimate is one that relies on the best practical and reliable data collection and estimation methods. Practical means that the required data collection and estimation can be carried out with proven techniques and resources deemed reasonable by the RTF…” 18

19 19 Staff Recommendation Proposal – For handling Measure Interactions associated with the Residential Weatherization and HVAC measures, use “Option 3” where measure identifiers cannot be used. Justification – Option 3, could be considered “Best Practice”. While not perfect, it is an appropriate blend of effort and accuracy, while still providing UES’s useful for planning. It would be difficult to justify the “Participant or Population Data” method over Impact Evaluation (billing analysis) since Impact Evaluation provides equivalent accuracy at less cost. But Impact Evaluation makes planning more difficult since it doesn’t directly provide a UES for each individual measure. Option 3’s short term inaccuracies are probably ok considering the Region’s needs are more long-term. Option 3’s long term inaccuracies are less significant the more program houses achieve the “full measure package” over time.

20 20 Decision “I _____ move the RTF approve the following method of accounting for measure interactions in Residential Weatherization and HVAC UES measures: (choose one) – A. Option 3 (justification: Best Practice); – B. “Participant or Population Data” (no change); or – C. Stop providing UES measures, instead require impact evaluation.”

21 If we choose Option 3… 21

22 Slides from Staff Update at November RTF Meeting:Staff Update at November RTF Meeting Slide from October RTF Presentation:October RTF Presentation Defining “Full Measure Package” 22

23 Defining “Full Measure Package” We still have more work to do; there wasn’t consensus with the subcommittee. Staff has come up with a new proposal to adjust the full measure package definition for each Characteristic Scenario based on cost-effectiveness of measure installation. Our goal is to match what programs encourage for participating houses. – For example, in houses with R-30 in the attic, it’s not cost-effective to add insulation. For that suite of Characteristic Scenario’s, Option 3 would assume the existing attic insulation defines the full measure package (not R-38). Next Steps: Staff’s suggestion is for staff to work on this proposal and bring it back to the full RTF once it’s been fully analyzed (or if we run into further major roadblocks). 23

24 Any Suggestions? Re-naming the “Option 3” method Looking for a descriptive name Some Options: – Adjusted Last-Measure-In – Pro-rated Additive Savings – Others? 24

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