Memorandum to: Scott Dimetrosky, eeb evaluation Consultant From



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Electric Demand IE Factor Comparison


Electric demand IE factors are found in the documentation from New York, California, and Minnesota. Values vary substantially from 1.00 to 1.66. This analysis resulted in an average summer peak demand IE factor of 1.05, with values ranging from 1.00 to 1.08. These values are similar to those found in the New York documentation, where an average factor of 1.07 and a range of 1.00 to 1.14 are given.

Table : Comparison of Electric Demand IE Factor Results25

Jurisdiction

Average Demand IE Factor

Connecticut

1.05

New York

1.07

California

1.37

Minnesota

1.25

The electric demand IE factor from this analysis applies to summer peak demand only—the analysis did not show any impact on winter peak demand due to interactive effects. Documentation from the other jurisdictions does not specify how “peak” is defined.

Gas Takeback Factor Comparison


The analysis conducted for this study resulted in an average gas takeback factor of 0.56, with a range of 0.35 to 0.88. Gas takeback factor values range from 0.26 to 0.89 among the other jurisdictions. As Table demonstrates, the analysis conducted for this study resulted in a gas takeback factor value that is equal to the overall average of the factors utilized by other jurisdictions.

Table : Comparison of Gas Takeback Factor Results



Jurisdiction

Average Gas Takeback Factor

Connecticut

0.56

New York

0.68

California (CPUC study) 26

0.67

California (PG&E field study)

0.58

Canada

0.77

Minnesota

0.26

Northwest States

0.87

Maryland (commercial buildings)

0.27

Vermont (commercial buildings)

0.36

Overall average

0.56

The average gas takeback factor for all regions of New York State is 0.68, greater than the average of 0.56 shown by this analysis. The New York documentation suggests that the most likely reason for the difference is substantial geographic variation: gas takeback factors for New York regions range from 0.41 (Binghamton) to 0.85 (Massena). The factors for New York City and Poughkeepsie, the regions closest to Connecticut, are 0.67 and 0.73 respectively.

Sampling and Weighting

This Appendix describes the sampling plan and weighting schemes used for this study.

Sampling Plan

The same 180 single-family homes which NMR audited for the Weatherization Baseline Assessment were used to model interactive effects for the Lighting Interactive Effects study. The Baseline Assessment focused exclusively on single-family homes, both detached (stand-alone homes) and attached (side-by-side duplexes and townhouses that have a wall dividing them from attic to basement and that pay utilities separately).Multifamily units—even smaller ones with two to four units—were excluded from the study due to the complexity and concomitant added costs of including them in the evaluation.

The evaluators relied on a disproportionately stratified design that aimed to achieve 10% sampling error or better at the 90% confidence level across all of Connecticut and also for several subgroups of interest (Table , shaded cells). This level of precision means that one can be 90% confident that the results are a reasonably accurate description of all the single-family homes in Connecticut. All precisions are based on a coefficient of variation of 0.5.27

Table : Sample Design, Planned and Actual (with Sampling Error)



Single-family Segment

Planned Sample Size

Actual Sample Size

Precision

Overall

180

180

6%

Low-income

68

34

14%

Non-low-income

76

146

7%

Income eligibility not identified

36a

0a

n/a

Fuel oil heat

109

111

8%

All other heating fuels

71b

69b

10%

Own

159

177

6%

Rent

21

3

47%

a The survey approach for identifying household income asked respondents if their income was above or below a certain amount based on their family size. This unobtrusive approach meant that the evaluators were able to identify the income status for all participants in the onsite study.

b The evaluators planned for 47 of these homes to heat with natural gas, and 46 of the homes in the final sample actually did so.

The final sample, however, did not achieve 90/10 precision for low-income households—although the sampling error of 14% is close to the desired 10%—and sampled fewer than expected renters (although the evaluators had not expected to achieve 90/10 precision for renters). These are traditionally difficult groups to sample,28 but three factors directly related to this study further limited the evaluators’ ability to achieve 90/10 precision for the low-income households and to visit the expected number of rental households. Two of these factors stem from the HES requirement that renters receive permission from their landlords before receiving HES services.

First, when recruiting for the study, the evaluators informed possible participants that they would have to get landlord approval before taking part in the study; at that point, many renters indicated they did not want to take part in the study. Second, renters that did originally express interest in the study were ultimately unable or unwilling to secure landlord permission prior to the onsite visit. Because a disproportionately high number of households that rent single-family homes also qualify as low-income, the difficulty in securing participants who rent also limited the evaluators’ ability to sample as many low-income households as designed.

A third reason for the lower than expected renter and low-income participation relates to the structure of buildings. When scheduling onsite visits, the evaluators discovered that many interested survey respondents who had originally indicated that they lived in single-family attached homes actually lived in multifamily homes or attached homes that were not completely separate units (i.e., they were not separated from attic to basement or they shared utilities).

NMR achieved 90/10 precision for oil-heated households and for households of all other fuel types combined. This reflects the fact that about 62% of single-family homes in Connecticut are heated with oil, and NMR could not promise—and did not achieve—90/10 precision for any other single heating fuel type with a sample size of 180 (the size chosen by the EEB and DEEP from a list of options provided by the evaluators).

Weighting

The data in this analysis was adjusted to population proportions using two separate proportional weights.

Cooling configuration weight. For the electric energy and electric demand IE factors, a weight based on American Housing Survey (AHS) 2011 estimates of the saturation of ducted central air conditioning systems in Connecticut was applied. This weighting scheme is based on two categories: (1) housing units that have a ducted central air conditioner or heat pump, and (2) housing units that have no cooling equipment or use room air conditioners only.

The central air conditioning saturation percentage in the sample of single-family homes used for this study closely mirrors that of single-family homes in Connecticut, according to the 2011 AHS. This would normally preclude weighting. However, in order for the factors contained in this memo to be applicable to multifamily units in addition to single-family homes, a weight based on the 2011 AHS was applied to adjust for differences in central air conditioning saturation between single- and multifamily units. Table details the cooling configuration weights.

Table : Cooling Configuration Proportional Weights


Weighting Category

CT Population: AHS 2011

Sample

Proportional Weight

Central AC or HP present

134,954

90

0.6412

Central AC or HP not present

285,965

90

1.3588

This study assumes that the interactive effects impact of each bulb upgraded from an incandescent to a CFL or LED would be roughly the same regardless of the physical size or configuration of the home, an assumption which is borne out by the preliminary modeling and research done for this study as well as the 2014 Northeast Residential Lighting Hours-of-Use Study.29

Heating fuel weight. For the heating fuel IE and gas takeback factors, a weight originally developed for use in the Weatherization Baseline Assessment was applied. This weight is based on a count of Connecticut households gathered from the American Community Survey (ACS) 2008-2010 three-year estimates, and broken out by fuel type and income status.

Two categories of primary heating fuel type served as the basis for this weighting scheme: (1) oil, propane, and biomass, and (2) gas and electricity. By combining the income and primary heating fuel categories, the evaluators established four weighting categories: (1) low-income with oil, propane, or biomass heating; (2) low-income with gas or electricity; (3) not low-income with oil, propane, or biomass; and (4) not low-income with gas or electricity.

This weight was applied to the results of this analysis because it corresponds to the original sampling plan under which the data used for this study was gathered. In addition, the four weighting categories resulted in baseline weights that were very close to one for all four categories, suggesting that the sample closely resembled the population in terms of heating fuel even prior to weighting the data. Table details the heating fuel proportional weights.

Table : Heating Fuel & Income Proportional Weights



Weighting Category

CT Population: ACS ’08-‘10

Sample

Proportional Weight

Oil, LP, or biomass (low-income)

128,495

20

1.296

Gas or electric (low-income)

72,766

14

1.048

Oil, LP, or biomass (not low-income)

475,295

98

0.978

Gas or electric (not low-income)

216,042

48

0.908


Savings Adjustment

This section provides examples for how program- or home-level savings can be adjusted using interactive effects factors.



Electric Energy IE Factor

The 2014 Connecticut Program Savings Documentation (PSD)30 provides the following lighting retrofit gross energy savings equation:





where:

AKWH = Annual electric energy savings in kWh/year

WattΔ = Delta watts—the difference between the wattage of the lower efficiency baseline bulb(s) and the wattage of the new bulb(s)

h = Hours of use per day

In order to adjust lighting retrofit gross energy savings for interactive effects, the equation is altered in the following manner:



where:

IEe = Electric energy IE factor

The following example uses overall average hours of use and IE factor values and a delta-Watts of 47 (corresponding to a 13-Watt upgrade CFL and a 60-Watt pre-retrofit incandescent):

In this example, the pre-adjustment electric energy savings would be 47.5 kWh/year per bulb, while the post-adjustment savings would be 49.9 kWh/year per bulb.



Electric Demand IE Factor

The PSD provides the lighting retrofit gross summer peak electric demand savings equation below:





where:

SKW = Summer peak electric demand savings

WattΔ = Delta watts, the difference between the wattage of the lower efficiency baseline bulb(s) and the wattage of the new bulb(s)

CFs = Summer lighting coincidence factor

In order to adjust lighting retrofit gross summer peak electric demand savings, the equation is altered in the following manner:



where:

IEd = Electric demand IE factor

The following example uses overall average hours of use and IE factor values and a delta-Watts of 47 (corresponding to a 13-Watt upgrade CFL and a 60-Watt pre-retrofit incandescent):

In this example, the pre-adjustment summer peak electric demand savings would be 0.0061 kW per bulb, while the post-adjustment savings would be 0.0064 kW per bulb. Winter peak electric demand savings require no interactive effects adjustment.



Heating Fuel IE Factor

The following equation is used to calculate the amount of the additional heating requirement that results from a CFL retrofit in non-electric-heated homes.





where:

AMMBTU = Annual heating requirement increase in MMBtu/year

WattΔ = Delta watts, the difference between the wattage of the lower efficiency baseline bulb(s) and the wattage of the new bulb(s)

h = Hours of use per day

IEh = Heating fuel IE factor in BTU/kWh

The following example uses overall average hours of use and IE factor values and a delta-Watts of 47 (corresponding to a 13-Watt upgrade CFL and a 60-Watt pre-retrofit incandescent):



In this example, the annual increase in heating requirements resulting from the CFL retrofit is equal to 0.09 MMBtu/year per bulb.



1 REM/Rate is a residential energy analysis software that is commonly used to model the performance of residential buildings—the software is most notably used by the ENERGY STAR® Homes program.

2 NMR Group, Inc. “Single-Family Weatherization Baseline Assessment” Submitted to the Connecticut Energy Efficiency Fund, Connecticut Light & Power, and the United Illuminating Company, May 30, 2014.

3 Ccf refers to hundred cubic feet of natural gas.

4 NMR Group, Inc. “Connecticut Efficient Lighting Saturation and Market Assessment.” Submitted to the Connecticut Energy Efficiency Fund, Connecticut Light & Power, and the United Illuminating Company, October 2, 2012.

5 As part of the Weatherization Baseline Study data collection efforts, information was collected on light fixtures but not on light bulbs. For this reason, results from the Efficient Lighting Saturation and Market Assessment were leveraged to estimate the baseline saturation of energy efficient light bulbs.

6 Efficient bulb types used in calculating the baseline or “as-is” average efficient wattages included CFLs, LEDs, and efficient halogens. The inefficient bulb entries included incandescents only.

7 For smaller homes, i.e. those less than 1,400 s.f., the number of upgraded bulbs was 22. For all others, 25 upgraded bulbs were modeled.

8 The limit on the number of efficient bulbs that can be installed during an HES retrofit has fluctuated in the past, and may again in the future. The 25-bulb figure is current as of the date of this memo.

9 http://www.ctenergyinfo.com/2013%20Program%20Savings%20Documentation%20-%20Final.pdf

10 NMR Group, Inc. & DNV GL. “Northeast Residential Lighting Hours of Use Study.” Submitted to Connecticut Energy Efficiency Board on March 14, 2014. Page XVII.

11 The REM/Rate data export provides demand information in kW to six significant digits.

12 “Biomass” refers to wood pellets or cord wood. Five sites in the sample (3%) heat primarily with one of these fuels.

13 The REM/Rate models detected no changes in winter peak demand due to the lighting retrofit. This is most likely because heating from incandescent lighting is essentially replaced with heating from electric resistance.

14 The HERS Index compares homes to the 2004 International Energy Conservation Code (IECC) with some modifications reflecting the 2006 IECC. Scores can range from less than zero to well over 100. A score of 100 indicates that a home was built to the specifications of the 2004 IECC (with 2006 IECC modifications), while a score of zero indicates a net zero energy home. The average HERS score among homes heated with natural gas in this study is 124, while the average among all other homes in this study is 117.

15 Jacobs, P., B. Evans, N. Hall, P. Horowitz, R. Ridge, G. Peach, R. Prahl, 2010, “New York Standard Approach for Estimating Energy Savings from Energy Efficiency Programs”, New York Department of Public Service, October 15, 2010. Page 289.

16 http://www.deeresources.com

17 HOT2000 is an energy analysis and design software for residential buildings that is produced by Natural Resources Canada and used mostly in Canada.

18 http://rtf.nwcouncil.org/measures/res/archive/ResCFLLighting_v2_1.xlsm

19 Hirsch, James J. “A Study of the Sensitivity of DEER HVAC Interactive Effects Factors to Modeling Parameters”. Submitted to California Public Utilities Commission Energy Division, March 28, 2012.

20 CPUC, 2014, “DEER2014-Lighting-IE_and_Adjustment-Factor-Tables-17Feb2014.xlsx”, Database for Energy-Efficient Resources, Version 2014.

21 Parekh, A., M. C. Swinton, F. Szadkowski, M. Manning, 2005, “Benchmarking of Energy Savings Associated with Energy Efficient Lighting in Houses”, National Research Council Canada. NRCC-50874.

22 Minnesota, 2012b, “ResidentialCFLs_v01.xls”, Deemed Savings Database, Minnesota Department of Commerce.

23 Northwest Power and Conservation Council, 2011, “ResCFLLighting_v2_1.xlsm”, posted on the “Current measures” section of the Regional Technical Forum web site, August 30, 2011.

24 Efficiency Vermont, 2013. “Technical Reference User Manual, Measure Savings Algorithms and Cost Assumptions”, Efficiency Vermont, Burlington, VT.

25 In the New York documentation, commercial lighting retrofit savings are adjusted using a summer peak demand IE factor, but the residential savings equations are not labeled as such. There is no indication in the California or Minnesota documentation as to the seasonality of the electric demand IE factors provided.

26 Hirsch, James J. “A Study of the Sensitivity of DEER HVAC Interactive Effects Factors to Modeling Parameters”. Submitted to California Public Utilities Commission Energy Division, March 28, 2012.

27 The coefficient of variation measures the dispersion of data in a series of data points; it is commonly used to estimate sampling error when measuring the efficiency of measures installed in weatherization efforts.

28 Underrepresentation of renters and low-income respondents is common in telephone surveys. For example, see Galesic, M., R. Tourangeau, M.P. Couper (2006), “Complementing Random-Digit-Dial Telephone Surveys with Other Approaches to Collecting Sensitive Data,” American Journal of Preventive Medicine, Volume 35, Number 5.

29 NMR Group, Inc. Northeast Residential Lighting Hours-of-Use Study. Submitted to Connecticut Energy Efficiency Board et al. May 5, 2014.

30 “Connecticut Program Savings Document: 9th Edition for 2014 Program Year”. The United Illuminating Company and Connecticut Light & Power Company. January 6, 2014.
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