2014 Demand-Side Management Evaluation Final Report ... · 2014 Demand-Side Management Evaluation...

2014 Demand-Side Management Evaluation

Final Report June 30, 2015

Indianapolis Power and Light

One Monument Circle Indianapolis, IN 46204

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Prepared by: Cadmus

With Subcontractor Opinion Dynamics

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Table of Contents Introduction .................................................................................................................................................. 1

Methodology Overview .......................................................................................................................... 3

Commercial and Industrial Summary ..................................................................................................... 6

Residential Summary .............................................................................................................................. 6

Peer Comparison Report Program ................................................................................................... 7

Multifamily Direct Install Program ................................................................................................... 7

Appliance Recycling Program ........................................................................................................... 8

Home Energy Inspector Program ..................................................................................................... 8

Commercial and Industrial Programs ........................................................................................................... 9

Program Budgets, Goals, and Scorecard ................................................................................................ 9

Methodology Overview .......................................................................................................................... 9

Audit and Engineering Review ......................................................................................................... 9

Sampling, Review, and Analysis ....................................................................................................... 9

Evaluated Ex Post Gross Impacts ................................................................................................... 10

Ex Post Net Savings ........................................................................................................................ 10

Evaluation Findings ............................................................................................................................... 11

Audit ............................................................................................................................................... 11

Engineering Review ....................................................................................................................... 12

Gross Impacts ................................................................................................................................. 13

Net Impacts .................................................................................................................................... 15

Conclusions and Recommendations .................................................................................................... 20

Peer Comparison Report Program .............................................................................................................. 22

Program Description ............................................................................................................................. 22

Program Goals, Budgets, and Scorecard .............................................................................................. 23

EM&V Methodology ............................................................................................................................. 24

Equivalency Analysis ...................................................................................................................... 26

Billing Analysis ................................................................................................................................ 26

Impact Analysis ..................................................................................................................................... 32

Overall Program Savings ................................................................................................................ 32

Paper-only vs. Paper/E-mail Models .............................................................................................. 33

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Channeling Analysis ....................................................................................................................... 34

Process Analysis .................................................................................................................................... 37

Engagement with Report ............................................................................................................... 38

Neighborhood Comparison ............................................................................................................ 39

Energy Saving Attitudes and Energy Tips ....................................................................................... 40

Energy Efficiency Actions Taken .................................................................................................... 43

Customer Satisfaction .................................................................................................................... 45

Demographics ................................................................................................................................ 46

Program Insights and Recommendations ............................................................................................ 48

Multifamily Direct Install Program .............................................................................................................. 50


Program Design Changes ............................................................................................................... 50

Program Budget, Goals, and Scorecards .............................................................................................. 51

EM&V Methodology ............................................................................................................................. 51

Impact Analysis ..................................................................................................................................... 52

Verified Volume ............................................................................................................................. 53

Ex Post Gross Savings ..................................................................................................................... 56

Ex Post Net Savings ........................................................................................................................ 59

Process Analysis .................................................................................................................................... 59

Program Participation .................................................................................................................... 59

Knowledge Increase ....................................................................................................................... 60

Customer Satisfaction .................................................................................................................... 61

Program Influence.......................................................................................................................... 63

Property Characteristics ................................................................................................................. 63


Appliance Recycling Program ...................................................................................................................... 65


Planned PY2015 Program Design Changes .................................................................................... 65

Appliance Efficiency Standards ...................................................................................................... 65

Program Implementation ..................................................................................................................... 66


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EM&V Methodology ............................................................................................................................. 68

Impact Analysis ..................................................................................................................................... 68

Verified Measure Volume .............................................................................................................. 70

Appliance Characteristic Factors and Adjustments ....................................................................... 72

Part-time Use Adjustment Factors ................................................................................................ 74

Ex Post Per-Measure Savings Algorithms ...................................................................................... 75

Gross Energy and Demand Savings ................................................................................................ 79


Home Energy Inspector Program ................................................................................................................ 85



EM&V Methodology ............................................................................................................................. 86

Impact Analysis ..................................................................................................................................... 86

Ex Ante Deemed Savings ................................................................................................................ 90

Ex Post per-Measure Savings ......................................................................................................... 91

Ex Post Gross Savings ..................................................................................................................... 92

Ex Post Net Estimates .................................................................................................................... 95


CoolCents Program—Residential and C&I .................................................................................................. 98

Program Budgets, Goals, and Scorecards ............................................................................................. 98

Appendix A: Self-Report Net-to-Gross Evaluation Methodology ............................................................. 100

Appendix B: Peer Comparison Report—Modeled Estimates.................................................................... 106

Appendix C: Peer Comparison Report Fixed Effects Model Output for Wave 1 ...................................... 108

Appendix D: Peer Comparison Report Fixed Effects Model Output for Wave 1 (Paper vs. Paper/E-mail) .................................................................................................................................................................. 111

Appendix E: Peer Comparison Report Fixed Effects Model Output for Wave 2 ....................................... 115

Appendix F: Peer Comparison Report Fixed Effects Model Output for Wave 2 (Paper vs. Paper/E-mail) .................................................................................................................................................................. 118

Appendix G: Peer Comparison Report Fixed Effects Model Output for Wave 3 ...................................... 122

Appendix H: Peer Comparison Report Fixed Effects Model Output for Wave 3 (Paper vs. Paper/E-mail) .................................................................................................................................................................. 125

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Appendix I: Multifamily Direct Install Program Assumptions and Algorithms ......................................... 129

Appendix J: Appliance Recycling Program Measures, Assumptions, and Algorithms ............................. 140

Appendix K: Home Energy Inspector Program Measures, Assumptions, and Algorithms....................... 143

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Introduction

This evaluation report presents impacts achieved by the Indianapolis Power and Light (IPL) Demand Side Management Portfolio in PY2014.1 Table 1 provides a list of programs evaluated in PY2014 as well as a brief description of each. Table 2, Table 3, and Table 4 present goals, energy savings, and demand savings, respectively, that the portfolio achieved in PY2014.

Table 1. IPL DSM Program Offerings

Program Brief Program Description

C&I DSM Programs

Business Energy Incentive

This program offers customers custom and prescriptive incentives on a wide range of energy efficient equipment including lighting, HVAC, and appliances.

CoolCents- Commercial*

The commercial CoolCents program helps IPL reduce peak load by curtailing air conditioning units during peak periods in the summer. Participating customers receive a credit on their bill for participating in the program.

Residential DSM Programs

Peer Comparison

The program provides information to customers so they can reduce their energy consumption through behavioral changes as well as increased customer engagement

Multifamily Direct Install

The main goal of the program is to direct install energy efficiency measures such as CFLs, low-flow showerheads, and kitchen and bathroom aerators at multifamily premises

Appliance Recycling

The program collects and recycles functioning refrigerators, freezers and room air conditioners to remove less-efficient appliances from the power grid. In PY2014 the program also offered a light-bulb kit with three CFLs to customers who were present at the time of the appliance removal.

Home Energy Inspector

The program offers free energy efficiency measures to customers after completion of an online energy audit

CoolCents – Residential*

The objective of the program is to reduce peak load by curtailing air conditioning during peak usage periods during summer months, between May and September. This program targets residential single-family, multifamily and mobile home premises. Monthly summer bill credit provided based on number of controlled HVAC units.

*The CoolCents program evaluations will be delivered under separate cover, in summer 2015.

1 IPL also offered DSM programs such as Ecohouse, Residential New Construction and Residential renewable

incentive programs. However, these programs were not included in the contracted scope of work for the PY2014 electric evaluation.

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Table 2. 2014 Program Savings Goals

Program PY2014 Energy Savings

Goal (kWh) PY2014 Demand Savings

Goal (kW) Commercial & Industrial Business Energy Incentive- Combined 34,505,911 5,785 Residential Peer Comparison 29,045,262 - Appliance Recycling 7,490,590 908 Multifamily Direct Install 2,272,600 399 Home Energy Inspector 1,779,099 173 Residential- TOTAL 40,587,551 1,480 Portfolio Total 75,093,462 7,265

Portfolio savings values follow below. The detailed program sections of the report that follow present findings and observations regarding these values.

Table 3. 2014 Portfolio Energy Savings Summary

Program Energy Savings (kWh)

Ex Ante Audited Verified Ex Post Gross Ex Post Net Commercial and Industrial

Business Energy Incentive—Custom

24,802,245 26,457,689 26,457,689 24,799,765 16,591,043

Business Energy Incentive—Prescriptive

13,755,234 13,108,870 13,108,870 13,449,495 13,786,618

Business Energy Incentive—Total

38,557,479 39,566,559 39,566,559 38,249,260 30,377,661

Residential

Peer Comparison

29,061,000 29,061,000(a) 29,061,000(a) 28,497,349 (b) 28,497,349

Multifamily Direct Install

5,857,744 5,857,744 5,470,271 5,300,381 5,300,381

Appliance Recycling

2,101,658 2,103,037 1,991,883 2,092,791 1,643,683

Home Energy Inspector

857,939 857,937 753,278 720,763 638,631

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Program Energy Savings (kWh)

Ex Ante Audited Verified Ex Post Gross Ex Post Net Residential—Total

37,878,341 37,879,718 37,276,432 36,611,284 36,080,044

Portfolio- Total 76,435,820 77,446,277 76,842,991 74,860,544 66,457,705

(a) The Evaluation Team could not confirm the ex ante deemed savings value for the Peer Comparison program and, as such, the ex ante energy savings are carried forward for the audited and the verified energy savings. (b) The Peer Comparison program evaluation establishes ex post net savings only. The ex post values are net of attrition.

Table 4. 2014 Portfolio Demand Savings Summary

Program Demand Savings (kW)

Ex Ante Audited Verified Ex Post Gross Ex Post Net Commercial and Industrial

Business Energy Incentive—Custom

3,601.39a 3,582.94 3,582.94 3,496.61 2,339


3,212.18a 3,038.28 3,038.28 2,688.51 2,756

Business Energy Incentive—Total

6,813.57 6,621.22 6,621.22 6,185.12 5,095

Residential Peer Comparison - -(b) -(b) 6,814(c) 6,814 Multifamily Direct Install 697 697 653 593 593 Appliance Recycling 349 364 351 370 289 Home Energy Inspector 86 87 78 81 76 Residential— Total 1,132 1,149 1,063 7,858 7,772 Portfolio-Total 7,946 7,769 7,703 14,043 12,867 a The tracking database served as a source; the utility Scorecard exhibited minor variance from the tracking data (+/- 10 kW). b The Evaluation Team could not confirm the ex ante deemed savings value for the Peer Comparison program and, as such, the ex ante energy savings are carried forward for the audited and the verified energy savings. c The Peer Comparison program evaluation establishes ex post net savings only. The ex post values are net of attrition. Values rounded for reporting purposes

Methodology Overview This evaluation primarily sought to perform an impact analysis for the IPL DSM portfolio, with the report covering the PY2014 program year. The Evaluation Team used the relative consistency among the programs and implementation methods (as well as continuity among Evaluation Team personnel) to

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leverage parameters and findings from previous years’ evaluations. Where possible (or required), the team used information such as in-service rates (ISRs), net-to-gross (NTG) ratios, and similar parameters to inform this year’s evaluation findings.

An impact analysis seeks to accurately quantify demand and energy savings from an efficiency measure or a collection (program/portfolio) of efficiency measures. These analyses employ statistical and engineering-based analysis techniques. Following a progression, analysis results for each savings type correspond to a specific step in the evaluation process. Quantified impacts correspond to the following steps:

• Step 1: Audited savings

• Step 2: Verified savings

• Step 3: Evaluated ex post gross savings impact

• Step 4: Evaluated ex post net savings impact

Figure 1 provides a visualization of the EM&V process that the team has taken for estimating savings from the Portfolio.

Figure 1. Demand-Side Management Impact Evaluation Steps

Table 5 provides definitions and purposes for each evaluation step.

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Table 5. Evaluation, Measurement, and Verification Impact Definitions

Category Definition Purpose

Ex Ante Savings Reported savings values in IPL’s Scorecard. Goal setting

Audited Savings

Checks for tracking system accuracy; program savings based on adjusted program tracking data. The following approach provided this value: • Review of the program tracking databases • Review of a sample of hardcopy program applications to verify

consistency with data recorded in the program tracking databases

Intermediate step only

Verified Savings

Confirms measures have been installed and currently operate. This step uses a random sample of installations selected for detailed analysis. Typical methods for collecting necessary data include telephone surveys and site visits. For installed Custom measures, engineering assumptions may be reviewed to provide a statistically representative sample of projects. This step may be adjusted to address issues such as the following: • Measures rebated but never installed • Measures not meeting program qualifications • Measures installed but later removed • Measures improperly installed

This step further adjusts audited savings as follows: • Check ex ante-deemed savings estimates and calculations to

ensure the implementer/utility applied previously agreed-upon values appropriately/correctly

• Adjust program tracking data as necessary to correct identified errors or omissions

• Recalculate program savings based on adjusted program tracking data

Confirmation of program reach and persistence of installed measures

Ex Post Gross Savings

Calculated using engineering analysis, building simulation modeling, billing analysis, metering analysis, or other accepted methods. Adjustments may include changes to baseline assumptions, adjustments for weather, adjustments to occupancy levels, and adjustments to decreased or increased production levels.

Indication of actual savings achieved by program

Ex Post Net Savings

Determined by adjusting ex post gross savings estimates to account for a variety of circumstances, including savings-weighted,1

freerider,2 and spillover effects.3 The NTG ratio for the two types of net savings estimates derived from the following formula:

Participant Net Savings: Annual Net-to-Gross Ratio = (1- freerider adjustment + participant spillover adjustment)

Program design improvements, planning future programs, cost-effectiveness analysis, calculations of lost revenues and shared savings

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Category Definition Purpose 1 Freerider, spillover, and market effects adjustments to the NTG ratio are weighted to reflect the savings levels

associated with those effects, compared to the savings levels directly achieved from installed measures. Savings are weighted so adjustments to net savings are proportionate to savings levels associated with the actions taken. Thus, small savings actions result in small adjustments and large savings actions result in larger adjustments, depending on the occurrence level.

2 Freeriders would have taken exactly the same action (or made the same behavior change)—installing a measure (or changing a behavior) with exactly the same energy efficiency result—at the same time as they did in taking the program-incented action. Partial freeriders would have taken exactly the same action, but the program expedited that change; or they would have taken similar action, but not at the same efficiency level as the program-incented action; or they would have made the same behavior change, but at a later time than the program-encouraged behavior change.

3 Savings produced due to the program’s influence on participants’ energy use, through technology purchases and usage changes, or through behavioral changes induced or significantly influenced by the program or the portfolio, but not directly incented by the program.

Commercial and Industrial Summary In PY2014, the Business Energy Incentive program exceeded its gross energy and demand savings targets, while spending fewer funds than budgeted. IPL delivers the Business Energy Incentive as a single program with a Custom and Prescriptive track. The Custom track delivered approximately double the savings (24.8 million kWh) of the Prescriptive track (13.4 million kWh); however, Custom freeridership was 33.1% (with no spillover savings); this can be attributed to one of the eight respondents being estimated as 100% freerider, and three of the eight respondents being estimated as 50% freeriders. The full freerider respondent stated that they had already intended to purchase the measure before learning about the program. While the Prescriptive track had lower savings compared to the Custom track, program freeridership was very low (4.9%); the program also had spillover savings, resulting in an overall Net-to-Gross ratio of 103% (i.e., ex post net savings were higher than ex post gross savings).

Residential Summary The IPL DSM programs showed strong growth in PY2014 when compared to the previous year. Overall, the Residential Core Plus portfolio increased energy savings by 13% more than PY2014. All programs expanded in terms of the number of participants, number of measures installed (or recycled), and in trade ally/builder reach. The residential portfolio contributed 36,611,284 kWh in ex post gross energy savings and achieved 7,859 kW in ex post gross demand savings (a 5% increase over the PY2013 ex post gross demand savings of 7,481 kW, excluding the CoolCents programs).

Despite this growth, IPL’s overall portfolio is also in transition. It absorbed some of the statewide programs (as of January 2015). The program also reviewed the viability of ongoing programs, resulting in the closure of three residential programs including:

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• The Residential New Construction program as it has historically incented few homes, and the demand for all electric homes has drastically curtailed with low prevailing natural gas market prices.

• HVAC High Efficiency program (in 2013) because the program was not deemed cost-effective. • The Residential Renewables program due to low historic participation rates

The major upsurge in energy savings are due to the large increase in the reach of the Peer Comparison Report program that more than doubled by reaching 210,961 homes in PY2014. The IPL Peer Comparison program contributed 78% of ex post gross energy savings.

The PY2014 residential evaluation focused on impact results for all programs. In addition, detailed results from participant surveys with Peer Comparison and Multifamily Direct Install (MFDI) program participants are included in this report. As per the focus on conducting participant surveys every other year, no surveys were fielded for the Appliance Recycling and Home Energy Inspector (HEI) programs.

Highlights of each evaluated residential DSM program follow.

Peer Comparison Report Program The Peer Comparison Report program is the largest contributor of savings to the IPL residential Core Plus programs portfolio, achieving an ex post net adjusted energy savings of 28,497,349 kWh, with a realization rate of 0.98. The program achieved an ex post net adjusted peak coincident demand savings of 6,814 kW. The Evaluation Team could not establish a realization rate as the program did not report demand savings. The program sent quarterly Home Energy Reports (HER) to 210,961 households in the IPL service territory (after attrition).

The program more than doubled in PY2014 with the addition of over 120,000 households to the treatment group. IPL reports that the program now reaches upwards of 54% of its total residential population. As the program expands, it now reaches lower energy consuming households, including multifamily and mobile properties.

In order to manage program expenditures, IPL has reduced the number of mailed reports to earlier participants (Wave 1) to twice a year, maintaining a quarterly schedule for the other waves. As with previous evaluations, there does not appear to be an increase in savings in households that also receive e-mail reports compared to those that receive both report types. To test effectiveness of e-mail reports, IPL may consider setting up a treatment group that only receives HER reports via e-mail.

Multifamily Direct Install Program The Multifamily Direct Install (MFDI) program is the second largest contributor to IPL’s Residential Core Plus portfolio, contributing 5,300,381 kWh to the program with a realization rate of 0.90. The program contributed 593 kW in gross ex post demand savings with a realization rate of 0.85.

The program introduced new measures in PY2014 such as programmable thermostats and LED night lights. Thermostats represent 0.36% of program savings.

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Property managers who responded to the participant survey have indicated satisfaction with the program overall and that they would participate in the program again. The leading drivers of satisfaction were the time it took to schedule and complete the installations, the professionalism of the implementer, and the CFLs that were installed. Some property managers have suggested that the program may be enhanced by sharing more program information with the properties’ tenants, adding measures installed through the program such as LED bulbs and fixtures and power strips, and increasing the number of CFLs and programmable thermostats installed.

Appliance Recycling Program The Appliance Recycling program is the third largest contributor to IPL’s Residential Core Plus portfolio, contributing 2,092,791 kWh to the program with a realization rate of 1.0. The program contributed 370 kW in gross ex post demand savings with a realization rate of 1.06.

The Appliance Recycling Program recycled 2,389 appliances and reportedly had customers install 1,775 CFL bulbs (the program distributed 4,425 bulbs, a 126% increase compared to PY2013), establishing a record performance for the program. While there were no major program changes in PY2014, IPL plans to increase the incentive payment beginning in 2015 to $40 per appliance (refrigerators and freezers; Room ACs incentives remain unchanged at $20/unit).

Home Energy Inspector Program The Home Energy Inspector (HEI) program contributes 720,763 kWh to the program with a realization rate of 0.84. The program contributed 81 kW in gross ex post demand savings with a realization rate of 0.95.

The program distributed 2,370 energy kits during PY2014, a slight increase over the 2,355 kits distributed in the previous year. IPL changed kit suppliers during the year, and included LED night lights in addition to standing CFL bulbs and water conservation measures. The program continued operating in partnership with Citizen’s Gas where IPL claims savings only from lighting measures distributed to homes with gas heating.

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Commercial and Industrial Programs

This section presents impact evaluation results for the Commercial and Industrial programs. The Evaluation Team conducted a series of interviews with program staff to learn of program changes, but did not conduct process evaluation activities as part of this assessment.

For Custom projects, prospective applicants must submit a pre-application, documenting the intended project and projected savings. After customers have been approved (based on the pre-application), they must submit a final application following the retrofit; this documents any changes from the original pre-application.

Program Budgets, Goals, and Scorecard Table 6 displays program budgets and expenditures for PY2014.

Table 6. Commercial and Industrial Budgets and Expenditures Program Program Budget Program Expenditures Percentage of Total

Commercial and Industrial Business Energy Incentive—Combined $3,798,417 $3,758,301 96%

Methodology Overview

Audit and Engineering Review The Evaluation Team collected IPL and implementer program savings claims and measured tracking data, reviewing and comparing the data at an aggregate level to ensure alignment among sources. Such activity often detects systematic tracking or documentation issues, such as inconsistent program year definitions, midyear implementation changes, or differing extract and report dates.

The team then collected available program and project documentation (including application files, calculation workbooks, implementer methodology work papers, and other pertinent information) and reviewed the files for the following features:

• Alignment with program tracking data;

• Agreement with the Indiana Technical Reference Manual (TRM), as appropriate;

• Completeness of information;

• Reasonableness of assumptions; and

• Appropriate algorithms and data sourcing.

Sampling, Review, and Analysis The Evaluation Team selected representative samples for each program offering. These samples served as the focus for evaluation analysis, with analysis findings then extrapolated to the larger program population.

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Each sample was random and weighted (stratified or probability proportional to savings), with claimed energy savings at the measure level selected for the weighting factor. Sample targets were set at 80% confidence and 10% precision for each offering (90/10 one-tailed), presuming a 0.5 coefficient of variation for the total population. This target approach yielded 90% confidence and 10% precision when applied at the program level.

The Evaluation Team then performed engineering desk reviews for each sampled measure. The team employed the following review and analysis techniques:

• Recreating claimed savings values using appropriate algorithms and deemed parameters, where applicable;

• Comparing relevant secondary source values, such as TRMs, energy codes, case studies, and other publicly available research;

• Interviewing facility personnel via phone or e-mail to understand operating practices, baseline conditions, and other pertinent details; and

• Researching key data affecting measures, including manufacturer specification sheets and performance data.

Finally, the Evaluation Team coordinated and cross-checked findings to determine spillover savings values.

Evaluated Ex Post Gross Impacts The Evaluation Team determined evaluated ex post gross impacts from synthesizing all prior measurement and verifications activities, all pertinent data, and other applicable secondary sources (e.g., TRMs, energy codes). These values represented the best estimate of true demand and energy savings from the associated measures or programs. The team generated these savings values for the sample populations and then calculated adjustment factors (i.e., ex post savings/ex ante savings) or realization rates (i.e., verified savings/ex ante savings). The team applied the resulting factors to the gross population savings values, thus projecting evaluation findings for the total program.

Ex Post Net Savings The Evaluation Team implemented a NTG methodology to evaluate the Business Energy Incentive Program in 2014. This NTG methodology consisted of two components—freeridership and spillover.

In 2014, the Evaluation Team implemented two surveys—one for each program track. As tracking data for the Business Energy Incentive Program’s Custom option were unavailable at the measure level, the team modified the survey to capture decision-making processes at the project level as a whole.

The following formula provided final NTG ratios for the program:

NTG Ratio = 1 – Freeridership + Spillover

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To determine freeridership, the Evaluation Team drew upon a previously developed approach that ascertained freeridership using patterns or responses to a series of 13 simple questions. The questions—which allowed yes, no, and don’t know responses—asked whether participants would have installed the same equipment in the program’s absence, at the same time, at the same amount, and at the same efficiency level. By assigning freerider scores to question response patterns, confidence and precision estimates could be calculated on score distributions.2

The Evaluation Team calculated participant spillover by estimating the following:

• Savings attributable to additional measures installed; and

• Whether respondents credited IPL with influencing those decisions.

The team counted measures as spillover if customers did not request an incentive, even though the measure qualified for a program incentive.

Appendix A provides a detailed explanation of the team’s NTG methodology, including descriptions of freeridership and spillover evaluation methodologies and full-text versions of NTG survey questions asked of participants.

Evaluation Findings Findings follow for the Business Energy Incentive program, including Custom and Prescriptive offerings.

Audit Program tracking data and program Scorecard values generally aligned very well, albeit with some exceptions. The final iterations of the Scorecard and tracking data were issued in March/April 2015. Updates to these data sources included late, year-end projects and measures. The Evaluation Team found a minor discrepancy (+/-10 kW) between claimed demand savings listed on the Scorecard and totals presented in the final program tracking database: in this case, program tracking data had been used as the claimed savings values of record.

This audit process detected several notable disagreements between the tracking data and spot-checked program files. The database incorrectly tracked a pair of prescriptive HVAC measures. Quantities and savings values were significantly inflated from the actual values presented in the project file, although reasonable reported incentive values were tracked. This likely resulted from an input error during data entry of the measure data as the units’ total tonnage matched the claimed quantity, and the two sets of values were presented side-by-side on the application form. Similarly, the measure count tracked for a sampled lighting controls measure did not match the count from the supporting files.

2 This approach follows methods outlined in: Schiller, Steven et al. “National Action Plan for Energy Efficiency.”

Model Energy Efficiency Program Impact Evaluation Guide. 2007. Available online: www.epa.gov/eeactionplan. The IPL Oversight Board reviewed and approved the evaluation team’s NTG questions and response pattern scoring approach.

http://www.epa.gov/eeactionplan

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Figure 2 and Figure 3 illustrate program population savings details by measure type. This measure type was characterized as “Lighting,” “Non-Lighting” or “Both Lighting & Non-Lighting,” and was determined by the Evaluation Team during the audit process, based on tracking data information.

Figure 2. Custom Population, Ex Ante Savings by Measure Type (kWh)

Figure 3. Prescriptive Population, Ex Ante Savings by Measure Type (kWh)

Engineering Review The Prescriptive and Custom offerings exhibited excellent evaluated savings performance, relative to claimed savings values. Notably, however, the evaluation process found many projects with inaccurate savings claims. The Evaluation Team found many measures with evaluated savings lower than claims,

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but the many projects yielding values higher than the original claimed values counterbalanced these measures.

Energy savings claims generally yielded higher realization rates than demand savings claims. The complexity of peak demand periods and coincidence factors likely drove this outcome. The Evaluation Team applied adjustments relating to demand savings for a number of measures examined; these adjustments included most lighting measure savings calculations evaluated to accurately incorporate coincidence factors and waste heat factors.

For a number of Prescriptive measures, the exact application of the TRM methodology remained unclear. Lighting control measures particularly seemed to use deemed savings values that did not correlate to the TRM methodology or available measure data. Though the TRM methodology may have been used for these calculations, the application was very likely inappropriate. Conversely, nearly all lighting projects used appropriate wattage values that reflected the true power demand of fixtures (including ballast factors) and not just nominal lamp wattages. For Custom measures, a number of deviations were detected during this review process resulting in adjustments to savings. However, no clear patterns of deviations were detected.

The Evaluation Team found most project documents complete and well organized. The review process found several incomplete files, although the implementation contractor was prompt in providing additional information and clarification. Overall, despite a few inconsistencies, the implementer’s tracking and documentation system appeared effective.

Gross Impacts A summary of impact evaluation activities follows, organized by program. In general, a relatively large share of program savings could be evaluated directly, in part due to a weighted sampling scheme.

Table 7. Impact Evaluation Activities

Program Gross

Population Unit Count

Gross Population Measure

Count

Evaluation Sample

Measure Count

Target Sample

Measure Count

Evaluation Sample Energy

Savings Share of Program1

Commercial and Industrial Business Energy Incentive—Custom

273 273 46 36 56%


48,947 524 51 38 38%

1 Represents the proportion of ex ante energy savings from the sampled measure population, relative to the gross program population (sample population energy savings/gross population energy savings).

Summaries follow for each program’s savings values, as does a chart of impact parameters resulting from the evaluation process and analysis. Additionally, a breakdown follows of evaluated adjustment

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factors (ex ante savings divided by ex post savings), illustrating some issues and inconsistencies with claimed savings. In general, though, very good agreement occurred between claimed savings values and evaluated findings.

Table 8. Impact Evaluation Parameters

Program Applied

ISR1

Demand Realization

Rate2

Energy Realization

Rate2

Demand Adjustment

Factor3

Energy Adjustment

Factor3

NTG Ratio4

Commercial and Industrial Business Energy Incentive—Custom

100% 99% 107% 97% 100% 66.9%


100% 95% 95% 84% 98% 102.5%

1 Source: Participant Surveys 2 Source: Review and Surveys = (Verified/Ex Ante) 3 Source: Engineering Analysis = (Ex Post/Ex Ante) 4 Source: NTG Analysis = (1 – Freeridership) + Spillover

Table 9. Business Energy Incentive Program—Custom Offering Savings Summary Savings Type Goal1 Ex Ante2 Audited3 Verified4 Ex Post5 Net6

Custom Offering Peak-Coincident Demand Savings (kW)

Not specified 3,601 3,583 3,583 3,497 2,339

Energy Savings (kWh) Not specified 24,802,245 26,457,689 26,457,689 24,799,765 16,591,043 1 The Scorecard only provides goals at a program level (i.e., Custom and Prescriptive combined). 2 Source: Scorecard and program tracking. 3 Source: audit. 4 Source: review and surveys. 5 Source: engineering analysis. 6 Source: NTG analysis.

Table 10. Business Energy Incentive Program—Prescriptive Offering Savings Summary Savings Type Goal1 Ex Ante2 Audited3 Verified4 Ex Post5 Net6

Prescriptive Offering Peak-Coincident Demand Savings (kW)

Not specified

3,212 3,038 3,038 2,689 2,756

Energy Savings (kWh) Not

specified 13,755,234 13,108,870 13,108,870 13,449,495 13,785,732

1 The Scorecard only provides goals at a program level (i.e., Custom and Prescriptive combined).

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2 Source: Scorecard and program tracking. 3 Source: audit. 4 Source: review and participant surveys. 5 Source: engineering analysis. 6 Source: NTG analysis.

Table 11. Savings Breakdown by Measure Type

Measure Type

Evaluation Sample

Measure Count

Evaluation Sample Unit

Count

Demand Adjustment

Factor

Energy Adjustment

Factor

Prescriptive Offering Air Compressor with VSD 3 22 99% 100% ENERGY STAR LED 15 8,713 102% 128% Exterior Lighting 7 375 101% 92% F32 T8 HB Lighting 6 725 100% 100% High Performance T8 Lighting 2 260 96% 198% Low Watt T8 Lighting w/ CEE Ballast 1 110 101% 137% Occupancy Sensors 7 805 12% 63% Other Efficient Lighting Technologies 2 21 67% 42% Parking Garage Lighting 6 800 100% 92% Rooftop and Unitary AC 2 72 20% 8% Custom Offering Medium Project - $5K-$25K 29 29 97% 99% Small Project - $1-5K 17 17 102% 120%

Net Impacts After conducting 49 Prescriptive participant phone surveys, the Evaluation Team converted participant responses into individual freeridership scores. The team converted scores using the Excel-based matrix approach described in Appendix A and then determined the freerider score by translating a survey respondent’s responses into a matrix value, and applying a rules-based calculation to obtain the final score. The team calculated freeridership scores, confidence intervals, and precision estimates based on distributions of scores within the matrix.

The following tables and figures present all combinations of responses received for the program, along with the score assigned to each combination, revealing participants’ responses did not reflect every potential combination. Responses tended to group around subsets of common patterns.

Table 12 shows freeridership calculation results for the program, indicating an overall 23% freeridership score.

16

Table 12. Freeridership Results Program Option Responses (n) Freeridership Score

Total—Commercial and Industrial 63* 23.2%

* Customers installing more than one measure were asked the freeridership battery of questions for a maximum of two measures, while custom participants were asked about the project as a whole, resulting in 63 unique responses.

Table 13 shows the percentage of total gross evaluated kWh energy savings, total number of survey responses and savings weighted freeridership estimates by participant type. Fewer survey completes were collected for C&I Custom Program compared to the C&I Prescriptive Program due to a small population from which to draw a sample and because the C&I Custom Program participants were difficult to reach during the phone survey process. The C&I Prescriptive Program freeridership estimate of 5% is less than the 12% freeridership estimated in PY2013. C&I Custom Program participant surveys were not completed in PY2013.

Table 13. Freeridership Results by Program

Program Category Evaluated

Gross Percentage of

Total Gross Number of Responses*

Freeridership

C&I Prescriptive Energy Savings (kWh) 13,449,495 35.2% 55 4.9%**

C&I Custom Energy Savings (kWh) 24,799,765 64.8% 8 33.1%** C&I Overall Energy Savings (kWh) 38,249,260 100.0% 63 23.2%*** *Customers installing more than one measure were asked the freeridership battery of questions for a maximum of two measures, while custom participants were asked about the project as a whole, resulting in 63 unique responses. **Weighted by survey sample ex post gross program kWh savings. ***Weighted by population ex post gross program kWh savings.

Table 14 shows the unique C&I Prescriptive Program response combinations resulting from the participant survey, freeridership scores assigned to each combination, and the number of responses for each combination. An x indicates a skipped question (due to the participant’s response to a previous question).

A few common patterns appeared regarding the respondents’ answers to freeridership questions:

• Twenty four respondents would not have purchased any equipment at all without the incentives. As these respondents indicated they would not have purchased the measures at all without the incentive, they could be considered 0% freeriders.

• Three respondents who reported purchasing or installing rebated measures before learning about the program were estimated as 100% true freeriders.

• Eight respondents were estimated as 0% freeriders because they reported they would have installed less efficient equipment in absence of the program incentive.

17

Table 14. Frequency of Prescriptive Participant Freeridership Scoring Combinations

1. In

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t in

cent

ive?

2. A

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pur

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3 Al

read

y pl

anni

ng to

pur

chas

e?

4. In

cap

ital b

udge

t?

5. In

stal

led

sam

e qu

antit

y?

6. In

stal

led

sam

e ef

ficie

ncy?

7. In

stal

led

at th

e sa

me

time?

8. In

crea

sed

ROI?

9. C

onfir

m, w

ould

hav

e in

stal

led

sam

e m

easu

re?

[Ask

if q

uest

ion

9 is

No]

10.

Inst

alle

d sa

me

effic

ienc

y?

[Ask

if q

uest

ion

9 is

No

and

if m

easu

re

QTY

> 1

] 11.

Inst

alle

d sa

me

quan

tity?

[A

sk if

que

stio

n 9

is N

o] 1

2. In

stal

led

at

the

sam

e tim

e?

[Ask

if q

uest

ion

9 is

No]

13.

Incr

ease

d RO

I?

Free

rider

ship

Sco

re

Resp

onse

Fre

quen

cy

Yes Yes x x x x x x x x x x x 100% 3

Yes No Yes Yes Yes Yes Partly No x x x x x 25% 1

Yes No Yes Yes Yes Partly Yes No x x x x x 25% 1

Yes No Yes Yes Yes No x x x x x x x 0% 2

Yes No Yes No Yes Yes Yes No x x x x x 12.5% 1

Yes No Yes No Yes No x x x x x x x 0% 1

Yes No Yes No No Yes Partly No x x x x x 0% 2

Yes No Partly x Yes Partly Yes No x x x x x 12.5% 1

Yes No No x Yes Yes Partly Yes x x x x x 25% 1

Yes No No x Yes Yes Yes No x x x x x 12.5% 1

Yes No No x Yes Yes Partly No x x x x x 0% 1

Yes No No x Yes Partly Yes No x x x x x 0% 2

Yes No No x Partly Yes No x x x x x x 0% 1

Yes No No x No Yes No x x x x x x 0% 1

Yes No No x No No x x x x x x x 0% 1

Partly No Yes No Yes No x x x x x x x 0% 1

Partly No Partly x Yes Yes Partly No x x x x x 0% 1

Partly No Partly x Yes Partly Partly No x x x x x 0% 1

Partly No No x Yes Yes Partly No x x x x x 0% 1

Partly No No x Partly Yes Partly No x x x x x 0% 1

Partly No No x Partly Partly Partly No x x x x x 0% 2

Partly No No x No No x x x x x x x 0% 1

No x x x x x x x Yes No x x x 0% 3

No x x x x x x x No x x x x 0% 24

18

Table 15 shows the unique C&I Custom Program response combinations resulting from the participant survey, freeridership scores assigned to each combination, and the number of responses for each combination. An x indicates a skipped question (due to the participant’s response to a previous question).

A few common patterns appeared regarding the respondents’ answers to freeridership questions:

• Three respondents would not have purchased the measure at all without the incentives. As these respondents indicated they would not have purchased the measures without the incentive, they could be considered 0% freeriders.

• One respondents was estimated as a 0% freerider because they reported they would have installed less efficient equipment in absence of the program incentive.

• Three respondents were estimated as 50% freeriders, as they planned to purchase the measures before hearing about the incentive, planned the purchase in their most recent capital budget, and confirmed they would have purchased the same efficiency and quantity of the measures at the same time without the incentive, but the program incentive increased the return on investment for the measure thereby allowing the project to receive implementation approval.

• One respondent who reported purchasing or installing rebated measures before learning about the program was estimated as a 100% true freerider.

Table 15. Frequency of Custom Participant Freeridership Scoring Combinations

1. In

stal

led

sam

e m

easu

re w

ithou

t inc

entiv

e?

2. A

lread

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ng to

pur

chas

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3 Al

read

y pl

anni

ng to

pur

chas

e?

4. In

cap

ital b

udge

t?

5. In

stal

led

sam

e qu

antit

y?

6. In

stal

led

sam

e ef

ficie

ncy?

7. In

stal

led

at th

e sa

me

time?

8. In

crea

sed

ROI?

9. C

onfir

m, w

ould

hav

e in

stal

led

sam

e m

easu

re?

[Ask

if q

uest

ion

9 is

No]

10.

Inst

alle

d sa

me

effic

ienc

y?

[Ask

if q

uest

ion

9 is

No

and

if m

easu

re Q

TY >

1]

11. I

nsta

lled

sam

e qu

antit

y?

[Ask

if q

uest

ion

9 is

No]

12.

Inst

alle

d at

the

sam

e tim

e?

[Ask

if q

uest

ion

9 is

No]

13.

Incr

ease

d RO

I?

Free

rider

ship

Sco

re

Resp

onse

Fre

quen

cy

Tota

l Ex

Post

Gro

ss k

Wh

Savi

ngs

Yes Yes x x x x x x x x x x x 100% 1 698,516

Yes No Yes Yes Yes Yes Yes No x x x x x 50% 3 575,853

Yes No No x No No x x x x x x x 0% 1 1,223,627

No x x x x x x x No x x x x 0% 3 477,730

19

Individual freeridership scores can be analyzed by examining their distribution, as shown in Figure 4 and Figure 5 for the C&I Prescriptive and Custom Programs, respectively.

Figure 4. Distribution of Prescriptive Freeridership Scoring Combinations

Approximately 84% of C&I Prescriptive respondents did not indicate freeridership; almost 10% of respondents indicated low levels of freeridership (12.5% to 25%), while 5% of respondents were estimated as full freeriders.

Figure 5. Distribution of Custom Freeridership Scoring Combinations

Four of the eight C&I Custom respondents did not indicate freeridership; three of the eight respondents indicated moderate freeridership (50%), one respondent was estimated as a full freerider.

As detailed in Appendix A, the team estimated spillover measure savings using specific information from the evaluation about participants and through the use of the Indiana TRM as a baseline reference. The team estimated the program’s spillover percentage by dividing the sum of additional spillover savings

20

(as reported by survey respondents) by the total incentivized gross savings achieved by all survey respondents from the relevant program track (i.e., Custom or Prescriptive).

Table 16. Surveyed Participant Savings Analysis

Program Track Spillover Savings

(kWh) Surveyed Program

Participant Savings (kWh) Spillover Percentage

Prescriptive 166,050 2,253,566 7.4% Custom 0 2,975,726 0.0%

Overall, surveyed C&I Prescriptive Program participants highly influenced by the program installed three efficient lighting controls, 1,344 LEDs, and 12 CFLs that did not receive funding from IPL. Table 17 displays the number of participants installing these additional measures—considered spillover—and the total KWh savings resulting from these installations. No C&I Custom Program participants surveyed attributed influence to their program participation on additional energy-efficient purchases.

Table 17. Spillover Measures, Quantity, and Savings Spillover Measure Participants Project Energy Savings (kWh)

Efficient Lighting Controls 1 2,443 LEDs 5 162,460 CFLs 1 1,147 Total 1 166,050

Table 18 summarizes the C&I Program’s freeridership, spillover, and NTG percentages.

Table 18. NTG Ratio for C&I Participants

Program Option Responses (n) Freeridership Percentage

Spillover Percentage

NTG Ratio NTG Absolute

Precision C&I Prescriptive 55 4.9%* 7.4% 102.5%* ±7.8% C&I Custom 8 33.1%* 0.0% 66.9%** ±41.9% Overall 63 23.2%*** 2.6%*** 79.4%*** ±37.1% *Weighted by survey sample ex post gross program kWh savings ** Weighted by survey sample ex post gross program kWh savings. Estimate does not meet the 10% absolute precision threshold at the 90% confidence interval. ***Weighted by program category ex post gross population program kWh savings.

Conclusions and Recommendations Conclusion: The Custom and Prescriptive offerings produced relatively accurate claimed savings estimates. Despite some definite inaccuracies and issues, the relative agreement between ex ante (claimed) savings values and ex post (evaluated) savings is a positive outcome. This conclusion especially holds true for a program that offers Custom measures with many varying factors (e.g., weather, production, occupancy).

21

Recommendation

• IPL and its implementers should continue to maintain and improve upon these high standards for measure tracking, savings estimation, and documentation.

Conclusion: Project and measure tracking methods generally proved effective and transparent. The implementation contractor generally applied an effective and understandable organization and documentation scheme. Project files were demarcated with cover pages to organize and label materials appropriately. Where provided, measure savings calculations were presented in a tabular (spreadsheet) format; so stakeholders could understand assumptions and algorithms via direct examination. This year’s final utility Scorecard generally was accurate, but it did not track Prescriptive offerings separately from Custom offerings, and exhibited a minor deviation from totals presented in the final program tracking data.

Recommendation

• Currently, all data sources present generic labels for savings values such as “kW Saved.” IPL and its implementers should consider specifying peak-coincident demand savings in project and tracking materials and in the utility Scorecard to eliminate ambiguity among stakeholders. Additionally, the implementer did not use tracking numbers for Prescriptive measures; amending this policy could provide added clarity regarding savings attribution and avoid variances between claimed savings data sources.

Conclusion: The claimed savings calculation methodologies for some measures were unclear and likely inappropriate.The implementation contractor probably leveraged the TRM and previous evaluation findings to generate claimed savings values, particularly for Prescriptive measures. Custom projects generally employed a standardized workbook, used to collect pertinent data. Prescriptive measures lacked documentation beyond project applications.

Recommendation

• IPL and its implementer should consider publishing methodology “work papers” that detail the calculation methodology and assumptions used for a given measure. Such work-papers often serve as a bridge between programs and TRMs. These documents also can contribute to a feedback loop with evaluation findings informing updates to the applicable TRM.

22

Peer Comparison Report Program

Program Description IPL funds the Peer Comparison Report program to meet the following primary objectives:

• Obtain measurable and cost-effective energy savings

• Increase program participation in select IPL Energy Efficiency, Renewables and Demand Response programs

The program uses behavioral science-based marketing to provide customized energy consumption information to selected IPL residential households. Treated households receive information on their energy consumption as compared to their peers, with the theory that such comparisons will prompt measurable energy savings among treated households. Every quarter, treated households are mailed a hardcopy of a Home Energy Report (HER) that contains customized suggestions for reducing energy consumption. Households are randomly chosen using a randomized control trial (RCT) design. In an RCT, a target population is randomly assigned to treatment and control groups. Due to the randomization process, treatment and control group customers are theoretically “equivalent” and, therefore, any difference between the treatment and control groups’ energy use after receiving treatment is considered attributable to the treatment intervention.

The IPL Peer Comparison program was launched in March 2012. The program initially targeted high-usage households that fell within the top 60% of energy usage in the residential sector. Given that the program met its program goals during its first year and was deemed to be cost-effective, IPL added an expanded group of customers in March PY2013. However, overall usage for this expanded group of customers is almost half that of the original customers (implication of this are discussed in the impact results section). In PY2014, the program added 183,993 treatment households to meet program goals established in the 2014 IURC filing (this reduced to 143,520 after attrition). By extending the program to households not traditionally in the “high-energy users” population, the program now reaches approximately 54% of its overall residential customer population (implication of this are discussed in the impact results section).

The Evaluation Team defines treatment households by waves as defined by OPower, the program implementer, generally based on when they first started receiving HER reports. Table 19 shows that in PY2014, while 273,778 IPL households received HER reports, this number was reduced to 210,961 due to the number of accounts that became inactive, given that as customers move, their account at the premise becomes inactive.

23

Table 19. Peer Comparison Report Wave Count After Attrition Wave Designation Recipient Control First Year Receiving Report(a) Wave 1 20,8137 12,442 February 2012 Wave 2 46,628 13,779 April 2013 Wave 3 143,520 15,968 January 2014 Total 210,961 42,189 Notes: (a) Some households may receive their first HER at a later date where customers a gap in billing data automatically delays generation of first HER.

Before PY2014, treatment households received mailed HERs quarterly. Treatment households for which IPL had e-mails addresses3 (approximately 10% of the treated population) also received monthly electronic reports with similar content as the mailed reports. Upon the program expansion with Wave 3, mailing expenditures became cost-prohibitive. Thus, the program adjusted the frequency of issuing of mailed reports as follows:

1. Wave 1: Semi-annually

2. Wave 2: Semi-annually

3. Wave 3: Quarterly

The program continues to send monthly reports via e-mail to all treatment households with e-mail records, irrespective of the wave in which they are classified.

All treatment customers have access to a web portal that contains the same information as in the reports. Exposure to additional information can occur through customer web portal access, which could cause additional savings. However, since the portal is not widely used or marketed/promoted, this report does not include it in the impact analysis.

Program Goals, Budgets, and Scorecard For PY2014, IPL had a goal of delivering 29 GWh net energy savings from the Peer Comparison program. The program increased its savings estimates considerably from PY2013, increasing the program savings targets by more than 50% (compared to 19 GWh in PY2013). In PY2013, the evaluated ex post savings for the program were about 13 GWh. Therefore, the adjusted goals established for PY2014 required that the program expand rapidly to meet these goals (with the Wave 3 expansion group noted above). This rapid expansion meant that the program deviated from targeting high-usage customers to reaching to a population of medium to lower usage, which the Evaluation Team expects to have an impact on per household observed savings.

3 Opower offered customers the option to provide their e-mail addresss through the web portal.Opower did

not, however, actively promote e-mail collection nor randomly assigned e-mail customers to the treatment or control groups.

24

In its scorecard, IPL reports the program met 100% of the net adjusted energy savings goals while spending 75% of the total implementation budget. Table 20 lists the goals associated with the Peer Comparison program.

Table 20. Peer Comparison Report Goals and Reported Results Metric PY2014 Goal PY2014 Reported (Scorecard) Percent of Goal

Budget 1,784,599 1,345,183 75% kWh 29,045,262 29,061,000 100% kW 0 0 NA Source: IPL DSM Oversight Board Monthly DSM Scorecard, December 31, 2014. Note: the scorecard did not include the participant count and hence it is not included here.

The IPL scorecard is a calendar year based scorecard (reporting program performance from January through December PY2014). Given that Wave 1 and Wave 2 customers are now in their third and second years of implementation (respectively), the savings for Wave 1 and Wave 2 are annual savings for the entire calendar year. However, Wave 3 customers began receiving first reports only after January 2014 across various months throughout the year and thus the savings for this cohort is based on a partial year but annualized for reporting purposes. For this reason, this report presents savings results both in aggregate and by wave.

EM&V Methodology The evaluation efforts for this program, per IPL’s request, are tailored to primarily address program net savings. The Evaluation Team relies on the industry standard for data collection and analytical methods for evaluation measurement and verification (EM&V) of behavioral programs.

Table 21 provides program specific EM&V activities performed by the Evaluation Team for the Peer Comparison program.

Table 21. Peer Comparison Report EM&V Tasks Task Description

Interviews with program managers and implementers

• Interviews with program managers and implementers to obtain a detailed level of knowledge about the program and to document the various program changes that could affect the impact analysis. These interviews allowed for the building of knowledge of general operational systems and procedures of the program, and provided additional information on the design and operations of these systems at a level of detail needed to conduct an impact evaluation.

Review tracking databases and select program materials

• Reviewed program databases and customer profiles for treatment and control populations, identifying and addressing adjustments where required.

Equivalency analysis • Determined equivalency of treatment and control groups for Wave 1, Wave 2 and

Wave 3 households prior to program implementation (usage only).

25

Task Description

Process evaluation • Fielded a participant survey with 211 treatment households (70 each in Waves 1

and 3, 71 in Wave 2) to gauge engagement with the HER report, effectiveness of program in leading to additional energy saving actions and satisfaction with IPL.

Impact evaluation

• Conducted a billing analysis to quantify the energy saved through actions taken among the treatment compared with control group members.

• Applied a channeling adjustment factor to remove any double-counting of savings already captured from participation in other IPL programs.

Each impact analysis effort is explained below.

Billing Analysis with Control Group: Methods to calculate net impact provide the level of change in energy and/or demand due to the activities of the program. Given the experimental design and the use of a control group, the impact estimates are considered net savings and are calculated using billing data, frequently referred to as “billing analysis.” The control group represents energy consumption that would have occurred in the absence of the program. This beneficial approach accounts for interactive effects of multiple actions taken over time. For the purpose of conducting a billing analysis, the Evaluation Team used the following data:

• Account and/or contact information, as well as the date actions were taken, linked to the home where the energy reports were sent;

• Flags designating treatment and control group status;

• Pre-billing data (12 months of pre-program billing data);

• Post-billing data (all available months of billing data following program launch); and

• Large sample sizes (to achieve sufficient statistical power to detect small effects).

This impact analysis relies on a Difference-in-Differences (DID) billing analysis of savings gained by the treatment group above the control group. Through this billing analysis, the Evaluation Team calculated net energy savings.

Channeling Analysis: A billing analysis includes other potential program effects, that is, savings that may have accrued if the treatment and control groups participated in other IPL DSM programs at different rates (e.g., more treatment than control customers participate in other programs). Therefore, the goal of conducting a channeling analysis is to quantify the savings that may be double-counted due to cross-participation in other programs. The savings tips provided in the reports, if effective, could lead to participation in other IPL DSM (managed statewide through Energizing Indiana or locally managed by IPL) among program participants, and possibly a higher rate of participation among the treatment group compared to the control. Increased participation in other programs among the treatment participants would mean that some portion of savings achieved through these other programs may be attributable to some influence by the Peer Comparison program. However, since the savings are claimed by the other IPL programs, they should be removed from the Peer Comparison program (to avoid double

26

counting). Through the channeling analysis, the Evaluation Team developed the adjusted net energy savings.

Equivalency Analysis Given that the net savings for behavioral programs are contingent on the defensibility of the experimental design, the Evaluation Team randomly assigned IPL customers into treatment and control groups for each of the three waves. However, given attribution, the Evaluation Team checked the equivalency of usage prior to program implementation for the treatment and control groups for Wave 1, Wave 2 and Wave 3 households (see Table 22).Evaluation TeamEvaluation TeamEvaluation TeamEvaluation Team

Table 22. Peer Comparison Baseline Usage (kWh/year), after Attrition Wave Treatment Control Equivalent

Wave 1 23,749 24,182 Yes Wave 2 10,826 10,975 Yes Wave 3 11,505 11,483 Yes

Billing Analysis The Evaluation Team conducted a billing analysis to assess changes in energy consumption attributable to the Peer Comparison program. This analysis relied upon a statistical analysis of monthly electricity billing data for all IPL customers who received the HER (the treatment group) and a matched sample of customers who did not receive any information from the program implementer (the control group) as chosen through the randomization process.

This final impact evaluation used a Fixed-Effect (FE) panel regression model that controls for time-invariant differences across individual customers. Appendix B to Appendix H provide the detailed model specifications and results.

The billing analysis included the following modeling efforts:

1. The impact of each wave respectively, i.e. program impact by Wave 1, Wave 2 and Wave 3.

2. The impact of the two report channels (paper vs paper + e-mail) for each wave.

The section below details the billing analysis methodology and the equations utilized for these two analyses. Both models controlled for time-invariant differences across customers via the customer-specific constant term (the fixed-effect), weather (via HDD and CDD terms). The paper-only vs. paper + e-mail model controlled for whether or not the customer had e-mail during the post-treatment period.

27

Savings by Wave The impact analysis for each wave is estimated by the model as:

Equation 1. Fixed Effect Model – Overall Savings of Wave 1, Wave 2 and Wave 3 Combined 𝑘𝑘𝑘𝑘ℎ𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛼𝛼1𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝐼𝐼𝐼𝐼𝑖𝑖 𝐼𝐼𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀_𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑖𝑖 + 𝛼𝛼2𝐶𝐶𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖𝐼𝐼𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀_𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑖𝑖

+ 𝛼𝛼3𝐻𝐻𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖𝐼𝐼𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀_𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑖𝑖 + 𝛽𝛽1𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝐼𝐼𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀_𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑖𝑖 + 𝛽𝛽2𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

Equation 2. Fixed Effect Model – Savings by Wave 𝑘𝑘𝑘𝑘ℎ𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛼𝛼1𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛼𝛼2𝐶𝐶𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐻𝐻𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽1𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖 + 𝛽𝛽2𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where:

PosttParti (β1) = Captures the overall program treatment effect

Part = A dummy variable for participation which equals to one if a customer received reports, 0 otherwise.

Post = A dummy variable which equals to one if the month is in the post-program period

Post = measures during the post-program period how customers changed their consumption

Post*Part = measures the program impact, i.e. during the post-program period how report recipients changed their consumption.

Savings by Report Channel The Evaluation Team also analyzed the impact of report channels, i.e. paper report vs. paper and e-mail report. This model is expressed as:

Equation 3. Fixed Effect Model – Paper Report and Paper/e-mail Savings by Wave 𝑘𝑘𝑘𝑘ℎ𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛼𝛼1𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛼𝛼2𝐶𝐶𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐻𝐻𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽1𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷𝑖𝑖+𝛽𝛽2𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝐼𝐼𝑀𝑀𝑖𝑖

+ 𝛽𝛽3𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷0𝑖𝑖 + 𝛽𝛽4𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where:

PosttParti (β1) = Captures the overall program treatment effect

Part = A dummy variable for participation which equals to one if a customer received reports, 0 otherwise.

Post = A dummy variable which equals to one if the month is in the post-program period

E-mail0 = A dummy variable which equals to one where paper reports (DM) is the only treatment approach available for both treatment and control group members; 0 otherwise.

Emai1l = A dummy variable which equals to one where e-mail is available for both treatment and control group members, 0 otherwise

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Post*E-mail0 = Measures during the post-program period how customers eligible for paper reports only changed their consumption (given e-mail = 0) in different implementation time frame

Post*Emai1l = Measures during the post-program period how customers eligible for e-mail (in addition to paper reports) changed their consumption (given E-mail = 1) in different implementation time frame. Note these consumption changes are NOT caused by the program because it is estimated based on treatment and control group members.

Post*Part*E-mail0 = Measures how the report, during the post-program period, has changed the consumption of recipients who were eligible for paper reports only

Post*Part*Emai1l = Measures how program treatment, (paper report and e-mail), during the post-program period, has changed the consumption of recipients who were eligible for e-mail and paper reports

Post*Part = Measures the program impact, i.e. during the post-program period how report recipients changed their consumption.

In this equation, the effect of direct mail (E-mail0) recipients is the coefficient on the Postt Parti 𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷0𝑖𝑖variable (β1), and the incremental effect of the e-mailed reports is given by the coefficient on the Postt PartiE-maili variable (β2). Also, Post represents a dummy variable equal to one for data points after the first report has been sent and Part represents a dummy variable equal to one for participants and equal to zero for those in the control sample.

Data Preparation The data used in the billing analysis comes from three primary sources:

• Monthly billing data from January 20114 to January 2015, obtained directly from IPL;

• Historical weather conditions (temperature to calculate HDD and CDD) from the National Oceanic and Atmospheric Administration’s (NOAA’s) National Climatic Data Center (NCDC); and

• Program launch date specific to each customer (treatment and control) from OPower.

4 The majority of the billing records start from February 2011, with 31 homes having billing data starting from

January 2011. The billing analysis also included these records.

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To develop the dataset used for the statistical analysis, the Evaluation Team conducted the following data processing steps:

• Determined the usage on a calendar month basis for each customer based upon their read cycle;

• Linked this usage data with the appropriate weather data, where a weather station was assigned based upon the distance between each weather station and the customer’s zip code;

• Linked the usage and weather data with the customer-specific program start date and whether or not the customer has e-mail;

• Removed customers who did not have a first report date;

• Removed customers who were IPL employees; and

• The very first month during which the first report was sent was deleted from the analysis. For example if the first report went out on March 9th within a billing period of March 1st – March 31st, March is considered “dead-band” and is excluded from the analysis. This is a consistent approach across the other Indiana utilities.

In developing this dataset, the Evaluation Team determined that the majority of the reports were first mailed in March 2012 for Wave 1 customers, in April and May 2013 for Wave 2 customers and in January 2014 for Wave 3 customers. Table 23, Table 24, and Table 25 present the distribution for Wave 1, Wave 2 and Wave 3 initial Home Energy Report dates, respectively.

Table 23. Peer Comparison Distribution of Report Start Date – Wave 1

Start Date Number of Customers Percent of Customers

Treatment Control Treatment Control February 2012 4,342 2,636 21% 21% March 2012 16,452 9,800 79% 79% May 2012 1 2 0% 0% June 2012 14 2 0% 0% September 2012 1 1 0% 0% October 2012 1 0 0% 0% November2012 1 1 0% 0% October 2013 1 0 0% 0% August 2014 203 142 1% 1% No Report Date 6 0 0% 0% IPL Employees 4,342 2,636 21% 21% Total 21,022 12,584 100% 100% Households in the Analysis 20,813 12,443 Notes: (a) Although the control group households do not receive actual reports, they are assigned a start date by OPower for the purpose of conducting the billing analysis. (b) The customer counts are net of attrition.

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Treatment Control Treatment Control April 2013 19,717 5,776 42% 42% May2013 2,6754 7,959 57% 58% June 2013 8 1 0% 0% July 2013 46 11 0% 0% August 2013 56 14 0% 0% September 2013 12 5 0% 0% October 2013 5 0 0% 0% November 2013 8 2 0% 0% December 2013 4 2 0% 0% February 2014 1 0 0% 0% April 2014 2 4 0% 0% May 2014 6 1 0% 0% July 2014 3 0 0% 0% August 2014 2 2 0% 0% February 2015 4 2 0% 0% No Report Date 1,583 451 0% 0% IPL Employees 0 0 0% 0% Total 48,211 14,230 100% 100% Households in the Analysis 46,628 13,779 Notes: (a) Although the control group households do not receive actual reports, they are assigned a start date by OPower for the purpose of conducting the billing analysis. (b) The customer counts are net of attrition.

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Treatment Control Treatment Control January 2014 128,394 14,181 89% 89% February 2014 14,985 1,776 10% 11% March 2014 13 0 0% 0% April 2014 19 0 0% 0% May 2014 3 0 0% 0% June 2014 4 0 0% 0% July 2014 4 0 0% 0% August 2014 81 11 0% 0% October 2014 2 0 0% 0% November 2014 4 0 0% 0% December 2014 3 0 0% 0% February 2015 8 0 0% 0% No Report Date 4,463 490 0% 2% IPL Employees 0 0 0% 0% Total 147,983 16,458 100% 100% Households in the Analysis

143,520 15,968

Notes: (a) Although the control group households do not receive actual reports, they are assigned a start date by OPower for the purpose of conducting the billing analysis. (b) The customer counts are net of attrition.

The Evaluation Team inquired about delays in first report dates for a handful of treatment households in each wave as noted in the tables above. In an e-mail discussion March 9, OPower notes that households remain eligible yet may receive a HER where there is a gap in their billing/usage data. Their system likely detected this data gap and the first reports were sent later than intended. As the numbers of treatment households are relatively small, this does not materially affect savings, but OPower is currently investigating this issue.

The analysis eliminated households identified as occupied by IPL employees (six households in Wave 1 and none in Wave 2 or in Wave 3). Notably, the treatment and control group numbers that received treatment through the program have been reduced due to natural attrition.

In summary, each wave included the following treatment and control households:

• Wave 1 (launched February 2012): 33,255 total households:

20,813 treatment households (i.e., 21,022 minus, 6 IPL employees and 203 customers with no first report date)

12,442 control households

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• Wave 2 (launched April 2013): 60,407 total households:

46,628 treatment households


• Wave 3 (launched January 2014): 159,488 total households:

143,520 treatment households


The Evaluation Team further segmented treatment participants within the waves to gauge whether there was a measurable difference in savings between customers who received the quarterly mailed HER (paper report only) compared to customers who, in addition to receiving the quarterly report, also received monthly e-mail-based HERs (paper + e-mail reports), to test the underlying hypothesis that customers who receive information more frequently, or by certain channels, may have more propensity to engage in energy saving behaviors. Notably, the program implementer does not randomly assign treatment/control group customers into receiving or not receiving e-mail reports. Rather, customers who have provided IPL with their e-mail address receive the e-mail reports. The number of customers who received paper vs. paper and e-mail reports are as follows:

• Wave 1:

Paper-only: 14,538 households

Paper/e-mail: 6,275 households

• Wave 2:



• Wave 3:



Impact Analysis This section outlines the modeled estimates from the two analyses described above; i.e., analysis by wave, and report channel analysis by wave.

Overall Program Savings As part of this impact evaluation, the Evaluation Team examined some of the program theory assumptions including:

1. Typically, behavior programs tend to see savings increase during ramp-up period and then plateau and stay approximately at the same level during the treatment period. In this case, Wave 1 customers showed a ramp-up pattern in the first two years after program initiation and stabilize in the third year. Wave 2 customers save approximately the same amount of energy as a year ago and showed minimal ramp-up period before saving plateau. Wave 3 customers saved

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the least among the three groups. These low savings may be due to the variability or variance of their usage. Wave 3 customers have the highest usage standard deviation which leads to more diverse usage pattern and therefore varying acceptance of energy reports.

• Past behavior program evaluations around the country have shown that households with higher baseline usage (high average energy consumption) tend to have higher savings as a result of program intervention than lower energy consuming households. In the Peer Comparison program, Wave 2 and Wave 3 customers have relatively lower energy consumption baseline when compared with Wave 1 customers (see Table 26).

Table 26. Peer Comparison Baseline Usage (kWh/year) Wave Treatment Control

Wave 1 23,749 24,182 Wave 2 10,826 10,975 Wave 3 11,505 11,483

Table 27 shows that the Peer Comparison program recipients (all waves) achieved an unadjusted net savings of 380 kWh per year per household for Wave 1, 125 kWh for Wave 2 and 111 kWh for Wave 3 (before channeling adjustments). This equates to an overall program savings of about 1.04% per household (before channeling adjustments), or 140 kWh per year. The overall program savings are established by aggregating the individual wave savings. As noted, ex post total program savings represent annualized values.

Table 27. Peer Comparison PY2014 Ex Post Net Unadjusted Program Savings

Waves Number of Treatment Customers

Average Percent Savings per Household

Average Net Unadjusted Per household Savings

(kWh)

Unadjusted Ex Post Program kWh

Savings Wave 1 20,813 1.58% 380 7,908,940 Wave 2 46,628 1.15% 125 5,828,500 Wave 3 143,520 0.93% 111 15,930,720 Total 210,961 1.04% 140 29,534,540

Wave 2 and Wave 3 households achieved lower per household saving as compared to Wave 1 households, which, as explained previously, is expected that households with higher baseline usage yield higher savings.

Paper-only vs. Paper/E-mail Models Table 28 shows the savings by report channel. Wave 1 households who received both paper and e-mail reports actually saved less than those who received only mailed reports. Wave 2 had the reverse scenario, with higher savings for households that received both reports. There were no significant difference among Wave 3.

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Table 28. Peer Comparison PY2014 Report Channel Savings (Paper-only vs. Paper/E-mail)

Waves Channel Number of Treatment Customers

Average Percent Savings

Average Net Unadjusted kWh/Savings per Household (kWh)

Unadjusted Ex Post Program kWh Savings

Wave 1 Paper Only 14,538 1.67% 416 6,047,808 Paper/e-mail 6,275 ↓ 1.36% 301 1,888,775

Wave 2 Paper Only 38,315 ↓ 0.98% 105 4,023,075 Paper/e-mail 8,313 1.82% 206 1,712,478

Wave 3 Paper Only 113,590 0.93% 109 12,381,310 Paper/e-mail 29,930 0.94% 117 3,501,810

These results, while consistent with the results of the PY2013 evaluation, are inconsistent with similar analyses of similar programs across the Midwest and the United States, where households that receive both paper and e-mail reports tend to have slightly higher average savings. It may be beneficial to test and see how savings would change if customers only receive e-mail reports. If similar savings could be achieved by e-mail reports without paper report, it could be a more cost effective option to consider when e-mail addresses are available. Notably, the program database only captures a static view of how customers are receiving their report rather than a change over the course of the program year. This could also contribute to the non-significant differences in savings. In addition, as noted, customers receiving an e-mail report are not assigned by the program implementer; rather, they are customers who have an e-mail address on file with IPL. Thus, these results are helpful for determining program processes rather than actual net program savings.

Channeling Analysis The savings tips provided in the HER, if effective, may increase participation in other IPL energy efficiency programs (when compared to participation levels among the control group). Increased participation in other IPL DSM programs among the treatment participants means that a savings already claimed by other programs are included in the Peer Comparison program’s unadjusted net savings estimates. Thus, the channeling analysis determines the portion of savings detected in the billing analysis that has already been claimed captured by other IPL DSM programs. The PY2014 channeling analysis uses the results from the PY2013 evaluation to adjust the net saving estimated through the billing analysis.5 That is, a marginal adjustment of 0.04% is applied to the unadjusted net impact savings to establish final impact values.

5 The PY2014 Peer Comparison program evaluation budget did not support conducting a channeling analysis; as

such, the PY2013 results apply here.

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Table 29 summarizes the saving adjustment analysis results.6

Table 29. Peer Comparison Savings Adjustment – Overall Program kWh Pre-treatment Post-treatment Post-Pre Difference

Treatment 0.25% 0.45% 0.20% Control 0.24% 0.39% 0.16% T-C Difference 0.01% 0.05% 0.04%

Note: Values rounded for reporting purposes

Adjusted Net Impact Energy Savings This section provides a summary of the impact analysis conducted by the Evaluation Team. Applying the channeling adjustments to the impact estimates, the Peer Comparison program had a final adjusted program savings of 1.00% per household or 135 kWh/year per household (Table 30, Table 31, and Table 32).

Table 30. Peer Comparison Net Savings Adjustment Analysis Impact Estimates

Billing Analysis Impacts Net Program Savings (% per HH) 1.04% Net Program Savings (Delta kWh per HH) 141 Channeling Adjustments Incremental Savings from Other Programs (% per HH) 0.04% Net Program Savings (Delta kWh per HH) 5.4 Net Adjusted Impacts Final Adjusted Net Savings (% per HH) 1.00% Final Adjusted Net Savings (Delta kWh per HH) 135

Table 31. Peer Comparison PY2014 Adjusted Net Energy Savings

Average Percent Savings

Net Household Savings (kWh/yr)

Ex Post Net Adjusted Program Savings (kWh)

Unadjusted Net Savings 1.04% 141 29,646,847 Channeling Analysis Adjustment 0.04% 5.4 1,149,497 Final Adjusted Net Savings 1.00% 135 28,479,349

6 Channeling calculations included the PY2013 Integrated Residential and C&I Core Plus Program EM&V Report,

issued to IPL on June 27, 2014. Notably, given there were fewer programs in PY2014 than in PY2013 (Residential HVAC Efficiency Program and Residential Renewables Incentives Program were not active in PY2014), the PY2013 channeling calculations were adjusted to remove these two programs. As such, the evaluation team applied a slightly adjusted PY2013 channeling results to the PY2014 analysis.

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Overall, the program achieved a net adjusted energy savings of 28,479,349 kWh with a realization rate of 0.98.

Table 32. Peer Comparison Report Ex Post Energy Savings Ex Ante Net

Program Savings (Scorecard, kWh)

Verified Participant Households

Ex Post per-Household

Savings (kWh)

Ex Post Net Adjusted Program Savings (kWh)

Realization Rate

29,061,000 210,961 135 28,479,349 0.98

Adjusted Net Impact Demand Savings The Peer Comparison Report program did not claim demand savings in the PY2014 scorecard. Information or behavior change programs pose significant challenges for estimating on-peak kW impacts for the following reasons:

• Unlike technology incentive programs, there is no single action or action-based load shape that can be identified as having occurred upon which the estimation process can focus. Participants take multiple actions and these actions can vary across cohorts, even within the same state.

• There are no data available to identify what action is taken, when an action is taken, how often it is taken, or the duration of that action or savings activity.

• Residential customers do not often have interval meters that can provide time-stamped, customer-specific consumption data to support an hourly-based impact analysis approach. Installing these meters on a large enough population to measure coincident demand would require large increases in the resources available for EM&V, and is thus cost prohibitive relative to the benefits from having that information.

• The degree of savings varies substantially across the participant waves such that each wave typically has different load reduction impact profiles. Some customers increase consumption because of the program, some have no change, and some reduce consumption. Program savings depend on the ability to effectively target and reach customers, and on the distribution of these segments within the targeted customer population. These segments and their distribution profiles can change substantially from one territory to another.

In previous reports (PY2012 and PY2013), the Evaluation Team discussed the challenges around estimating demand for behavioral programs. The Evaluation Team also provided a rough estimate given the existing literature. However, the Evaluation Team has discussed multiple approaches and the possibility of refining the earlier estimate to improve confidence in the estimate by using Indiana-specific load shapes. As a result, the Evaluation Team used the Calibrated DSMore Load-Shape Differences (CLSD) approach. This approach uses the utility-specific residential load shapes built into DSMore, calibrates the load shapes to match the verified annual consumption of the treatment group to equal the annual kWh savings, then identifies and reports the demand reductions during the coincident peak for each utility.

37

This provides a robust and cost effective approach compared to other approaches that require substantial increases in evaluation resources to conduct extensive action-based survey work (to identify actions taken that can be modeled into a load shape reduction) or the installation of interval meters on participating homes to collect real-time (15-minute or hourly data for example) kW data.

The specific steps associated with this approach are as follows:

1. Conduct a pre-post differences of differences (experimental design with randomized control group) billing analysis to identify average participant and program-wide energy (kWh) savings achieved (described above).

2. Calibrate utility-specific residential DSMore load shapes to match the kWh consumption levels of the targeted population.

3. Adjust the load shape such that the annual savings identified in the billing analysis are achieved on that load shape, maintaining the same shape but reducing the amplification of that shape.

4. Record the coincident load reduction on the calibrated DSMore load shape for the peak period defined by each utility.

5. Report that number as the coincident kW reduction.

6. Multiply that peak reduction by the number of participants to report program kW impacts.

Therefore, the resulting summer peak coincident demand savings for this program are estimated to be 0.032 kW per household for the aggregate Wave 1, Wave 2, and Wave 3 program participants.

At the program level, this results in a total summer peak coincident contribution of demand savings of 6,814 kW.

Table 33. Peer Comparison Report Ex Post Demand Savings Ex Ante Net

Program Savings (Scorecard, kW)

Verified Participant Households

Ex Post per-Household

Savings (kW)

Ex Post Net Adjusted Program

Savings (kW)

Realization Rate

0 210,961 0.032 6,814 NA

Process Analysis The Evaluation Team fielded a survey with 211 treatment households, approximately 70 respondents per wave (Waves 1, 2, and 3). The following analysis reports weighted overall results and identifies differences between waves where they are statistically significant (at the 90% confidence level). The primary survey objectives included examining treatment households’ engagement with the home energy report (HER), attitudes and perception of the neighbor comparison feature, and whether the energy tips included in the HER are useful and have prompted recipients to take additional energy savings actions. The survey also collected information on customer satisfaction.

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As mentioned earlier in the report, Wave 1 treatment households started receiving report in 2011, (thus about four years ago), Wave 2 since 2012, and Wave 3 only began receiving reports in late December 2013/early 2014.

Table 34 displays the weights used for reporting.

Table 34. Peer Comparison Survey Weights Survey Weights Wave 1 Wave 2 Wave 3 Total

Unique Accounts within tracking data 27,170 64,978 189,000 281,148 Accounts in Sample with valid phone numbers and other data cleaning

27,054 64,916 189,000 280,970

Completed surveys 70 71 70 211 Survey weights 0.29 0.69 2.03 NA

Engagement with Report Awareness of the program was high overall, and most households (84%) recalled receiving the HER. Wave 1 households still read reports with the highest frequency (50% of Wave 1 households read every HER they received in 2014), this is made even more impressive by the fact that they have received more reports than all other waves.

Figure 6. Reports Read in the Past Year

Those who have read at least one HER in the past year (177 of the 211 respondents) indicated that they are easy to understand (8.7 on a 0-10 scale), a score consistent across waves.

In addition, 78% of households not only read the HER but also read their utility bills to further understand their energy usage. About 60% households report of learning new ways to save energy from the HER.

50%

23% 25%

2%

42%

26% 28%

4%

38%29% 31%

2%0%

20%

40%

60%

80%

100%

Every Report Most Some None

Wave 1 Wave 2 Wave 3

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Neighborhood Comparison The neighborhood comparison is a critical component of the home energy report, employing social norming to induce treatment households to conserve energy. The survey asked several questions to discern treatment households’ perceptions of the reports.

Overall, households anticipate how their energy usage compare to that of their neighbor based on their wave.

Wave 1 households for the most part expect that their relative energy consumption is higher than their neighbors—that is, they realize that on average, they are high energy users.

Figure 7. Households Perceptions of their Energy Consumption Relative to that of Their Neighbors

Notes: (*) Indicates a statistically significant difference at the 90% level

Table 35 shows households’ responses to their comparisons with neighbors. In general, households were somewhat surprised by how their energy use compares to their neighbors, in particular Wave 2 households (Wave 1 and Wave 2 appear to realize that they are high and low users, respectively).

Households had mixed responses (depending on wave) about on the effectiveness, accuracy and satisfaction regarding the HER neighbor comparison feature.

• Wave 1 disagreed that the neighbor comparison was accurate, and thus expressed the lowest satisfaction score related to the comparison feature.

• Wave 2 in general appear more engaged and satisfied with the feature than other waves.

10% 9%

26%

56%*

11%

27%32% 30%

6%

17%

36%41%

0%

20%

40%

60%

80%

100%

(Don't know) Lower About thesame

Higher

Wave 1(n=70)

Wave 2(n=71)

Wave 3(n=70)

7%

19%

34%40%

(Don'tknow)

Lower About thesame

Higher

Overall(n=211)

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In general, all households report that the comparison made them more aware of their own energy usage (7.4 overall, with no statistically significant differences between waves).

Table 35. Perception of Neighborhood Comparison Component Wave 1 Wave 2 Wave 3 Overall

How much do you agree with: The neighbor comparison makes me more aware of my own household energy use

6.8 7.3 7.5 7.4

I was surprised by how my energy use compares to my neighbors

6.5 7.8 W1,W3 6.8 7.0

The neighbor comparison seems accurate to me

4.5 6.9 W1 6.8 W1 6.6

How satisfied are you with: The comparison of your home's usage to similar homes?

4.6 7.4W1 6.9 W1 6.8

Notes: W1 Indicates a statistically significant difference against Wave 1 W3 Indicates a statistically significant difference against Wave 3

Energy Saving Attitudes and Energy Tips This section presents findings on additional energy saving actions households completed as a result of receiving the HER report and their energy efficiency attitude in general.

Information provided in the HER: In general, Wave 1 households found the detailed information provided in the HER to be less comprehensive and less useful than other waves. Overall, all waves report that the detail in the report is good, but could be improved.

Graphs showing overall usage: This metric received relatively higher scores than the level of detail. Wave 1 again, seems to be less satisfied with the information than the other waves

Table 36. Attitudes toward the Home Energy Report How much do you agree that… (n=211) Wave 1 Wave 2 Wave 3 Overall

How much do you agree that: The level of detail in the report is sufficient 6.4 7.6W1 7.9W1 7.7 How satisfied are you with: The graph showing your usage over time? 6.8 8.2 W1 8.2 W1 8.0 Notes: Values rounded for reporting purposes W1 Indicates a statistically significant difference against Wave 1

Attitudes related to Energy Efficiency: Households report that they actively try to conserve energy. Yet, they also report that they are not as inclined to change their energy consumption behavior.

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Table 37. Attitudes towards Energy Efficiency Energy Efficiency

How much do you agree that… (n=211) Strongly Disagree

(0-2)

Disagree (3-4)

Neutral (5)

Agree (6-7)

Strongly Agree (8-10)

Mean

We always try to conserve and want to find more ways to save energy

2.1% 3.3% 12% 24.6% 57.6% 7.7

We are exploring ways to reduce our energy usage, but have taken no action

24.7% 9.6% 13.2% 19.4% 30.9% 5.3

We are not planning to change our actions to reduce our energy usage

24.6% 6.7% 17.9% 10.9% 27.0% 4.7

For the most part, households report that they are only sometimes influenced to change their energy saving behavior, in other words, that they would change energy behavior if it is convenient and easy to do.

Figure 8. Attitudes toward General Energy Saving Actions

IPL’s role in supporting energy efficiency: On average households agree that IPL wants them to help reduce their energy usage, that IPL provides them with useful tips to reduce usage. As with other metrics, Wave 1 households tend to provide relative lower scores than the other waves.

Table 38. IPL Support of Energy Efficiency and Conservation How much do you agree that… (n=211) Wave 1 Wave 2 Wave 3 Overall

IPL wants to help me reduce my home energy use 6.6 8.7W1 8.3W1 8.3 IPL provides useful suggestions on ways I can reduce my energy usage and lower my monthly bills

6.5 8.3 W1 8.0 W1 7.9

Notes: W1 Indicates a statistically significant difference against Wave 1.

4%

24%

41%

21%

15%

3%

11%

51%

21%

17%

0% 20% 40% 60% 80% 100%

Don't Know

Never

Sometimes

Usually

Always

Frequency of actions/behaviors influenced by planned energy use

Frequency of actions/behaviors changed to reduce energy use

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Energy tips: Overall ratings for usefulness of energy tips are moderate.

Table 39. Energy Savings Tips How much do you agree that… (n=171) Wave 1 Wave 2 Wave 3 Overall

How much do you agree: The reports provide enough information to take energy saving actions in my home

5.8 6.9W1 7.3 W1 7.0

The reports remind me to take energy savings actions? 6.1 6.6 7.3W1 7.0 How satisfied are you with: Energy savings tips provided in the report 6.2 7.7 W1 7.5 W1 7.4 Notes: W1 Indicates a statistically significant difference against Wave 1

IPL DSM program awareness. In general, slightly more than one-third of households are aware of IPL energy efficiency programs. Yet, Wave 1 awareness is significantly higher than those of other waves. This difference may be explained by the fact that Wave 1 households have been receiving reports longer than their counterparts.

Table 40. Awareness of other IPL DSM programs Component (n=211) Wave 1 Wave 2 Wave 3 Overall

Yes 61%W2,W3 39% 34% 38% No 39% 61%W1 63%W1 60% Don’t Know 0% 0% 3% 2% Notes: W1 Statistically significant difference against Wave 1.

Figure 9 illustrates awareness level by program. There were no statistically significant differences among the waves. The Home Energy Inspector/Online kit is the most well-known program.

Figure 9. Specific IPL DSM Program Awareness (n=81)

43%

24% 22% 21%17%

0%

20%

40%

60%

Home EnergyInspector

ApplianceRecycling

CoolCents Don't knowprogram names

Other

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Current Participation in DSM programs: One-fifth of households have participated in at least one other IPL DSM program, with Wave 1 households more likely to have done so than other waves.

Table 41. Participation in IPL DSM Programs Response Wave 1 Wave 2 Wave 3 Overall

Yes 40%W2,W3 29% 13% 21% No 58% 71% 83%W1 77% Don’t Know 0% 0% 4% 3% Refused 2% 0% 0% 0% Notes: W1 Statistically significant difference against Wave 1. W2 Statistically significant difference against Wave 2. W3 Statistically significant difference against Wave 3.

Planned Participation in DSM programs: About 20% of households reported planning to participate in another IPL-sponsored DSM program within the next year.

Table 42. Planned Participation in IPL DSM Programs Response (n=81) Wave 1 Wave 2 Wave 3 Overall

Yes 28% 21% 21% 20% No 54% 54% 54% 62% Don’t Know 19% 19% 25% 19%

Energy Efficiency Actions Taken This section addresses energy saving actions and behaviors of treatment households.

Energy Savings Actions: Predictably, Wave 1 households report engaging in energy saving actions at a higher rate than the other waves. That may be so because they have been exposed to the HER, and the comparisons and tips for longer, and have had sufficient time to engage in these actions. The vast majority of Wave 1 (93%) reported engaging in specific actions such as servicing their HVAC unit, sealing doors/windows or installing energy efficiency lighting (compared to 77% of the overall treatment group). One-third reported replacing equipment in their home with energy efficiency versions (compared to 32% of overall treatment households).

Table 43. Energy Efficiency Actions Taken Response (n=211) Wave 1 Wave 2 Wave 3 Overall

Have you replaced equipment in your home with an ENERGY STAR grade or higher model? Yes 66%W2,W3 35% 26% 32% No 33% 65%W1 73%W1 67% Don’t Know 1% 0% 1% 1%

44

Response (n=211) Wave 1 Wave 2 Wave 3 Overall Have you taken any energy saving actions, such as servicing your central A/C unit, sealing doors or windows, or buying efficient lighting for your home in the last few years? Yes 93%W2,W3 79% 74% 77% No 7% 18%W1 26%W1 22% Don’t Know 0% 3% 0% 1% Notes: W1 Indicates a statistically significant difference against Wave 1

Energy Saving Behavior: Wave 1 reported engaging more frequently in behavior such as researching how to save energy, visiting the IPL website, and contacting IPL specifically for the purpose of learning of energy saving opportunities.

Table 44. Energy Efficiency Behaviors Statements (n=171) Wave 1 Wave 2 Wave 3 Overall

Have you had any discussions about energy use in your home? 69%W3 58% 54% 57%

Have you contacted IPL about how to save energy in your home? 19% 16% 9% 11%

Have you visited IPL's website? 37% 27% 35% 34% Have you researched specific actions, equipment, or technologies to save energy in your home? 64%W2,W3 38% 37% 40%

The most frequently cited energy savings behavior included adjusting thermostat, and taking shorter and colder showers.

Figure 10. Energy Usage Behaviors

Future plans for equipment improvement: Only about 20% plan future energy efficiency equipment upgrades, with Waves 1 and 2 more likely to note these plans.

74%

53%

41%

17%

1%0%

20%

40%

60%

80%

100%

Regularly turnthermostat torecommended

set points

Regularly takeshortershowers

Reducedwater heatertemperature

None of theabove

Don't Know

45

Table 45. Planned Upgrades with Energy Efficiency Equipment Component (n=211) Wave 1 Wave 2 Wave 3 Overall

Yes 27%W3 30%W3 14% 19%

No 64% 65% 71% 69%

Don’t Know 9% 6% 14%W2 12%

Notes: W2 Indicates a statistically significant difference against Wave 2. W3 Indicates a statistically significant difference against Wave 3.

Customer Satisfaction This section addresses satisfaction with the HER report and general satisfaction with IPL. In general, customers are satisfied with the HER. Waves 2 and 3 however are significantly more satisfied with the HER report than Wave 1 households.

Satisfaction with IPL among all households is high, with a score of 8.3.

Table 46. Customer Satisfaction

Component Not at All Satisfied

(0-2)

Not Satisfied

(3-4)

Neutral (5)

Satisfied (6-7)

Very Satisfied

(8-10) Mean

The report overall 4% 4% 11% 15% 65% 7.7 W1 (n=59) 19% 3% 17% 20% 41% 6.1 W2 (n=54) 4% 2% 9% 17% 69% 7.9 W3 (n=56) 2% 5% 11% 14% 68% 7.8 IPL 1% 3% 8% 9% 41% 8.3 W1 (n=69) 7% 6% 13% 10% 33% 7.0 W2 (n=71) 3% 1% 4% 5% 42% 8.6 W3 (n=69) 0% 3% 8% 9% 38% 8.4

More than half of treatment households note that they feel more favorable towards IPL after they began receiving HER. As with other metrics, Waves 2 and 3 exhibit higher changes in favorability when compared with Wave 1 households.

46

Figure 11. Favorability toward IPL since Receiving the HER (n=177)

Notes: W1 Statistically significant difference against Wave 1.

Demographics Household profiles differ across waves as follows:

• Premise type: 87% of Wave 1 reside in single-family homes, compared to (66% of Wave 2 and 57% of Wave 3, respectively). This support the theory that Wave 1 are the highest energy users among all cohorts.

• Fuel type: 70% of Wave 1 households use electricity as their primary fuel type. This is almost reversed for Wave 2 where 72% of participants use natural gas. Wave 3 tends to have both proportions of natural gas and electricity usage as the primary fuel type. This also supports why Wave 1 tends to be higher energy users; Wave 2 may have been selected to maximize gas savings; Finally, Wave 3 is so large, that it encompasses all fuel types.

• Household Income: 25% of Wave 1 tend to be higher-income households than other waves (with 25% indicating a salary higher than $100,000 a year; compared to 13% and 9% of Wave 2 and Wave 3 respondents).

• Residence type: Wave 1 customers are more likely to live in single-family premises, than both Wave 2 and Wave 3 customers, which supports the hypothesis that Wave 1 households are larger consumers in part due to the housing type. For Wave 3, as IPL extended the program to a larger swath of its population, it is also reaching a diverse set of housing type, which traditionally consumes less energy on average (and thus have less energy to reduce as a result of the program intervention).

3%7%

53%W2,W3

23%

1% 3%

25%

52%W1

1% 0%

34%

49%W1

0%

20%

40%

60%

80%

100%

Don't Know Less favorable The same Morefavorable


2% 1%

34%

47%

Don'tKnow

Lessfavorable

The same Morefavorable

Overall

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Figure 12. Residence Premise Type

Notes: W1 Statistically significant difference against Wave 1. W2 Statistically significant difference against Wave 2. W3 Statistically significant difference against Wave 3. Values rounded for reporting purposes.

Figure 13. Primary Fuel Type

87%W2,W3

66%57%

6%

20% 34%W1,W2

3%4%

4%1%

6%3%4%

3% 1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Single family Apartment/Condo

Townhouse or Row House Duplex or two-family

Mobile home, house trailer Other/Don't Know

Natural Gas Electric Don't Know/Other

27%

70%

3%

Wave 1

72%

27%

1%

Wave 2

56%

43%

1%

Wave 3

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Figure 14. Residence Premise Treatment Households Income Level

Program Insights and Recommendations This section highlights the insights from the impact analysis, as well as several findings related to the design of the experiment and the tracking of data.

Conclusion 1. The evaluation did not find significant differences between customers receiving paper-only reports with those receiving paper and e-mail reports. Wave 1 households who received both paper and e-mail reports actually saved less than those who received a paper report only. Wave 2 had the reverse scenario, with higher savings for households that received both paper and e-mail reports. There were no significant difference among Wave 3 households. This, however, does not necessarily mean that e-mail is not an effective channel; since the current evaluation only looks at a static picture of households receiving paper-only reports or both paper and e-mail reports and only at customers with an e-mail address on file with IPL. These results, while consistent with the results of the PY2013 evaluation, are inconsistent with similar analyses of programs across the Midwest and the United States, where households that receive both paper and e-mail reports tend to have slightly higher average savings.

Recommendation: To test effectiveness of the e-mail report, the program could establish a wave to test e-mails only.

Conclusion 2. Higher baseline usage households tend to save more than those with lower baseline usage. Results from the comparison of savings across Wave 1 and Wave 2 households’ energy usage prior to participating in the program indicate that customers with higher pre-participation usage tend to have higher energy savings. Wave 1 households contributed higher savings per household than Wave 2 households (both in percentage and in absolute savings), primarily because they have a higher level of energy consumption to begin with and, therefore, percentage savings are applied to a larger base. The

40%

28% 32%

0%

20%

40%

60%

80%

100%

<$50,000

$50,000-

$100,000

>$100,000

Wave 1(n=57)

49%

35%

16%

0%

20%

40%

60%

80%

100%

<$50,000

$50,000-

$100,000

>$100,000

Wave 2(n=57)

59%

30%

11%

0%

20%

40%

60%

80%

100%

<$50,000

$50,000-

$100,000

>$100,000

Wave 3(n=56)

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Peer Comparison Report program is usually geared for high-usage single-family homes to obtain highest savings. Yet, increasingly multifamily and mobile properties are also included in the treatment group. To reach aggressive savings goals, IPL has significantly expanded the treatment group where it is now comprised of multifamily family, townhomes as well as mobile homes properties. These properties, in general, are lower energy consumers than single-family homes. As a result, they are more likely to contribute less in absolute terms in terms of energy savings than higher consuming households. Therefore, their inclusion has the potential to reduce average savings by treated household

Recommendation: Target future program participants based on baseline energy usage, such as households with baseline usage closer to that of Wave 1 households. However, should the program have exhausted the high usage target population, consider assessing the cost-effectiveness of future expansions, given the lower expected average baseline of proposed future expansion groups.

Conclusion 3. Wave 1 households appear to be the least satisfied among all cohorts, have a higher rate of not agreeing with the accuracy of the neighbor comparison, and note that they have already changed behavior and/or upgraded their homes with energy efficiency equipment when compared with Waves 2 and 3. Yet, they still contribute the highest average savings per households than other cohorts. For the most part, Wave 1 households have been in the program for a longer period of time, thus having had the time and multiple HER touch to engage in energy saving behaviors. IPL has recognized this, and now has reduced the frequency of mailed HER reports to these households to only twice a year (compared to quarterly for the other waves).

In essence, Waves 2 and 3 seem to be the most engaged and satisfied with the report. However, in general, while they note the desire to engage, their level of energy consumption is lower (relative to Wave 1 households) yielding less opportunity for reduction in energy consumption.

Recommendation: Despite these reported results, Wave 1 households still have the highest average savings when compared to the other waves, thus the Evaluation Team recommends that IPL continue to monitor Wave 1 average savings and satisfaction levels in future surveys.

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Multifamily Direct Install Program

Program Description The Multifamily Direct Install (MFDI) program targeted energy efficiency in large rental apartment buildings. IPL jointly administered the program with Citizens Energy in premises that received IPL electric service and Citizens Energy gas service.

For buildings that participated in the program, tenants received measures that were direct-installed by the implementer during a property walk-through. Measures installed through the program included:

• Up to five compact fluorescent lamps (CFLs, standard and specialty such as globes) • Low-flow showerhead • Kitchen and bathroom aerators • LED night lights • Programmable thermostats

Wisconsin Energy Conservation Corporation (WECC) implemented the program. The WECC program manager was responsible for the management of subcontractors used to perform installations, ensured that the program’s goals were being met, and helped to maintain participation goals and program budgets. IPL program staff resolved issues with the program, managed the program budget, oversaw ex ante deemed savings calculations, and oversaw the implementation contractor.

The implementation process began when WECC’s subcontracting installer, Thermoscan Inspector Incorporated (TSI), identified specific buildings to target from IPL’s customer list of multifamily properties within the service area. TSI reached out to potential properties by mailing letters, phone, and using contacts through the Indiana Apartment Association. Once a multifamily property decided to participate in the program, TSI scheduled an appointment with facility maintenance employees. The apartment company then sent out notifications to inform tenants when installers will be visiting their apartment units. Together, TSI and maintenance facility employees approached each unit in the building, knocked on doors, and installed low-flow measures (faucet aerators and showerheads), CFLs, and LED night lights in the property’s tenant units.

In addition to the work and information left behind, the program offered a one-year warranty on equipment installed.

Program Design Changes The program had the following program design changes in PY2014:

• Link with Citizen’s Gas: IPL jointly offered the program with Citizen’s Gas in multifamily properties that had gas heating. This was critically important for IPL as the number of all-electric properties in IPL’s service territory is relatively limited. Program staff reported that the program nearly reached a saturation point in the all-electric property market. In addition, given the

51

historically relatively low current and future pricing for natural gas, most newly constructed properties were built with gas heating, which further limited the program’s market size.

• Introduction of New Measures: In PY2014, the program introduced LED night lights and programmable thermostat measures.

Program Budget, Goals, and Scorecards The MFDI program aimed to reach more than 8,700 tenant units with the goal of achieving 7.490 MWh in energy savings and 908 kW in demand savings. The program reached 77 unique multifamily properties, serviced a total of 9,178 tenant units) while spending 60% of its budget. The program achieved 78% of energy savings and 77% of demand savings goals. Table 47 shows the reported energy and demand savings goals and reported performance for the IPL MFDI program.

The MFDI program was the second largest contributor to energy savings in the IPL DSM residential programs portfolio.

Table 47. MFDI Goals and Reported Results Metric PY2014 Goal PY2014 Reported (Scorecard) Percent of Goal

Budget $1,037,299 $620,860 60% Tenant Units serviced by programa 8,756 9,178 105% kWh 7,490,590 5,857,744 78% kW 908 697 77% Notes: (a) Number of apartment units that participated in the program Source: IPL DSM Oversight Board PY2014 Scorecard

EM&V Methodology This section summarizes the evaluation efforts for the MFDI program. Table 48 lists the evaluation tasks for the program.

Table 48. MFDI EM&V Tasks Task Description

Program Manager and Implementer Interviews

• Interviewed IPL program manager • Interviewed program implementer (WECC)

Program Materials Review • Reviewed program materials including the program operating plan. Program Database Review/ Verification

• Reviewed participant data-tracking databases for the IPL only and the IPL Joint (with Citizens Gas)

Process Analysis

• Fielded a survey with 15 property managers to establish measure installation rate and gauge program processes and customer satisfaction. Where there were insufficient numbers to establish installation rates for seldom installed measures, the Evaluation Team set the installation rate at 100%.

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Task Description

Impact Analysis

• Reviewed ex ante deemed savings values by measure • Established installation rates through interviews with property managers • Used Indiana TRM algorithms for lighting and water conservation measures

with adjusted input variables to establish ex post gross savings • Assigned a NTG of 1.0 under the premise that given that this program was

acted upon by the property manager, that the tenant would not have installed the program measures on their own

Impact Analysis This section outlines the evaluation steps to establish ex post gross and net energy and demand savings. To establish impact savings for the MFDI program, the Evaluation Team completed the following steps:

Step 1: Identified quantity of measures in tracking database. Reviewed contents of the tracking database to identify the quantity of measures and the total ex ante energy (kWh) and demand (kW) savings. Compared tracking data with reported scorecard values.

Step 2: Verified volume of measures. Applied installation rates established through the participant survey to calculate verified volume of program measures –that is, the number of measures still installed in the property.

Step 3: Updated variable assumptions to calculate ex post savings. Revised ex post variable assumptions for water conservation measures with data from recent studies (refer to Appendix H for a detailed discussion).

Step 4: Calculated ex post per-measure savings. Used variable assumptions established in Step 3 with algorithms from the Indiana TRM to derive ex post savings for measures offered in past program years.

Step 5: Determined ex post savings for new measures. Derived ex post per-measure savings for three new measures offered in PY2014 (i.e. LED night lights, CFL candelabras, and programmable thermostats).

Step 6: Determined gross program ex post energy and demand savings. Summarized reported ex ante and ex post gross energy and demand savings, and calculated a program realization rate.

Step 7: Calculated net energy and demand savings. Applied a NTG of 1.0 to establish net ex post energy and demand savings.

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Verified Volume In PY2014, the program reported installing 105,675 measures in 77 multifamily properties. To verify the number of measures installed, the Evaluation Team reviewed the program tracking database and application of ex ante values, making adjustments as needed through the following steps:

Step 1: Checked for duplicates within the program tracking database. The Evaluation Team confirmed that no duplicate records appeared in the database. No adjustments made to ex ante volume.

Step 2: Apply installation rates to determine verified measure volume. Applied installation rates derived from a PY2014 survey with 15 participant property managers. The survey read-in property specific information on installed volume per measure type, and asked the property managers to confirm whether these measures were still installed in the quantities noted. The installation rate is the difference between what the tracking database noted as installed and what the property manager noted as still installed in their properties. Where there were insufficient respondents to establish installation rates (such as in common areas, CFL candelabras, and programmable thermostats), the Evaluation Team set the installation rate at 100%. Given that this estimate is a self-report of the property managers’ assessment of overall installed percentages, there is a possibility of measurement error. This chapter notes in the recommendation section that IPL consider on-site verification for the PY2015 evaluation.

For the most part, the majority of program measures remained installed. Property managers indicated removal of select measures due to malfunction (particularly for showerheads and aerators) and, in some instances, the tenant’s preference for the original measure.

Table 49 shows installation rates used to establish verified measure volumes.

Table 49. MFDI Program Measure Installation Rates

Measure Volume Installed (Respondents Tracking Data)

Quantity Remaining (Self-report)

Installation Rates (PY2014)

CFL Bulbs 12,274 11,692 95.26% CFL Globes 14,865 13,289 89.40% CFL Candelabra NA NA 100.00% LED Night Lights 3,050 2,365 77.54% Showerheads 1,701 1,616 94.98% Bathroom Aerators 1,445 1,378 95.38% Kitchen Faucet Aerators 1,202 1,110 92.33% Common Area - CFL Bulbs 454 454 100.00% Common Area - CFL Globes 42 42 100.00% Programmable Thermostats NA NA 100.00% Note: Values rounded for reporting purposes.

Table 50 highlights audited volume, applied installation rates, and the resulting verified volume per measure. The scorecard does not report measure volume, but instead reports the total number of apartment units with installed measures. As a result, the Evaluation Team used data from the program

54

tracking database as the starting point to establish the total volume of installed measures. In summary, the Evaluation Team applied ex post savings to 96,461 verified measures.

Table 50. MFDI Verified Volume Measure Audited Volume Installation Rate Verified Volume

IPL Electric CFLs (In-unit) 20,643 95.26% 19,664 CFLs Globe (In-unit) 23,973 89.40% 21,432 CFLs (Common Area) 1,709 100.00% 1,709 CFLs Globe (Common Area) 299 100.00% 299 LED Night Light (In-unit) 6,972 77.54% 5,406 Bathroom Aerators 5,363 95.38% 5,115 Kitchen Aerators 4,735 92.33% 4,372 Low-Flow Showerheads 5,924 94.98% 5,626 Programmable Thermostat 183 100.00% 183 IPL Joint CFLs (In-unit) 16,311 95.26% 15,538 CFLs Candelabra (In-unit) 192 100.00% 192 CFLs Globe (In-unit) 11,252 89.40% 10,059 CFLs (Common Area) 1,289 100.00% 1,289 CFLs Globe (Common Area) 1,245 100.00% 1,245 LED Night Light (In-unit) 5,585 77.54% 4,331 TOTAL 105,675 NA 96,461 Note: Values rounded for reporting purposes.

Ex Ante Deemed Savings Table 51 shows the ex ante deemed savings per measure. Through review of the tracking database, the Evaluation Team confirmed that the program staff correctly applied ex ante deemed savings values for each measure.

Table 51. MFDI Ex Ante Deemed Savings Measure Energy (kWh) Demand (kW)

IPL Electric CFLs (In-unit) 26.89 0.006 CFLs Globe (In-unit) 26.89 0.006 CFLs (Common Area) 51.00 0.010 CFLs Globe (Common Area) 51.00 0.010 LED Night Light (In-unit) 13.60 - Bathroom Aerators 52.07 0.008 Kitchen Aerators 103.40 0.008 Low-Flow Showerheads 343.30 0.023 Programmable Thermostat 120.82 -

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Measure Energy (kWh) Demand (kW) IPL Joint CFLs (In-unit) 51.80 0.006 CFLs Candelabra (In-unit) 16.51 0.010 CFLs Globe (In-unit) 51.80 0.006 CFLs (Common Area) 51.00 0.010 CFLs Globe (Common Area) 51.00 0.010 LED Night Light (In-unit) 13.60 - Note: Values rounded for reporting purposes.

Ex Post per-Measure Savings The Evaluation Team referred to the Indiana TRM to establish assumptions to calculate ex post energy and demand savings. Where data was not available in the IN TRM, the Evaluation Team referenced data from the 2012 Residential Indiana Baseline Study, Michigan Water Study, or other sources. Appendix H contains specific algorithms, variable assumption values, and specific references for all program measure ex post variables. The Evaluation Team updated variable assumptions for water conservation measures that were applicable to the IPL service territory. Table 52 lists ex ante deemed savings and ex post per-measure savings.

Table 52. MFDI Ex Ante Deemed and Ex Post per Measure Savings

Measure Ex Ante Deemed Savings Ex Post per Measure Savings

kWh kW kWh kW IPL Electric CFLs (In-unit) 26.89 0.006 25.54 0.006 CFLs Globe (In-unit) 26.89 0.006 19.09 0.005 CFLs (Common Area) 51.00 0.010 168.53 0.006 CFLs Globe (Common Area) 51.00 0.010 125.99 0.005 LED Night Light (In-unit) 13.60 - 13.14 - Bathroom Aerators 52.07 0.008 58.33 0.011 Kitchen Aerators 103.40 0.008 141.96 0.007 Low-Flow Showerheads 343.30 0.023 201.49 0.017 Programmable Thermostat 120.82 - 105.19 0.157 IPL Joint CFLs (In-unit) 51.80 0.006 49.22 0.006 CFLs Candelabra (In-unit) 16.51 0.010 40.36 0.004 CFLs Globe (In-unit) 51.80 0.006 36.79 0.005 CFLs (Common Area) 51.00 0.010 324.81 0.006 CFLs Globe (Common Area) 51.00 0.010 242.82 0.005 LED Night Light (In-unit) 13.60 - 13.14 - Note: Values rounded for reporting purposes.

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The input assumptions to derive the deemed ex ante savings are unknown. While IPL program staff indicated that the implementer referenced the Indiana TRM, these savings are not replicable. As a result, the Evaluation Team is unable to identify the reasons for discrepancies between the ex ante deemed and ex post per-measure savings. The Evaluation Team calculated distinct ex post savings for those with electric heating (IPL Electric customers) and for those with gas heating (IPL Joint customers). Additionally, the program installed varying wattages for CFLs of the same type (i.e., standard, specialty globe, specialty candelabra, etc.), but the program tracking database did not include the quantity of installed lamps by wattage. Because of this, the Evaluation Team calculated an average ex post per-measure savings by CFL lamp type accounting for the multiple installed CFL wattages.

Ex post per-measure savings include waste heat factors for all program CFLs based on the customer type (i.e., IPL Electric, IPL Joint). The customer type indicated the primary heating fuel type for apartment units, which allowed the Evaluation Team to appropriately set which waste heat factor to use. However, the program installed CFLs in both apartment units and common areas, and the Evaluation Team had no insight into whether the common areas were conditioned (and so should have a waste heat factor applied). Ultimately, the Evaluation Team assumed the program had installed the bulbs in conditioned space such as hallways or entrances and, therefore, the customer type represented the primary heating type for common areas.

Ex Post Gross Savings Table 53 and Table 54 show the program’s ex ante reported, audited, verified, and ex post gross savings for the MFDI program. The program achieved approximately 5,300,381 kWh in ex post gross energy savings and 593 kW in ex post gross demand savings, with realization rates of 0.90 and 0.85, respectively.

While the program scorecard included volume counts, they reflected number of tenant units serviced, not total number of measures installed. Thus, ex ante volume is excluded from the impact tables.

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Table 53. MFDI Ex Ante and Ex Post Gross Energy Savings

Measure Audited Volume

Verified Volume

Ex Ante Deemed Savings (kWh)

Ex Ante Reported

Savings (kWh)

Audited Gross

Savings (kWh)

Verified Gross

Savings (kWh)

Ex Post per Measure Savings (kWh)

Ex Post Gross

Savings (kWh)

IPL Electric CFLs (In-unit) 20,643 19,664 26.89

Scorecard does not report savings per

measure

555,090 528,774 25.54 502,157 CFLs Globe (In-unit) 23,973 21,432 26.89 644,634 576,304 19.09 409,142 CFLs (Common Area) 1,709 1,709 51.00 87,159 87,159 168.53 288,024 CFLs Globe (Common Area) 299 299 51.00 15,249 15,249 125.99 37,671 LED Night Light (In-unit) 6,972 5,406 13.60 94,819 73,525 13.14 71,038 Bathroom Aerators 5,363 5,115 52.07 279,251 266,338 58.33 298,358 Kitchen Aerators 4,735 4,372 103.40 489,599 452,056 141.96 620,634 Low-Flow Showerheads 5,924 5,626 343.30 2,033,709 1,931,528 201.49 1,133,658 Programmable Thermostat 183 183 120.82 22,110 22,110 105.19 19,250 IPL Joint CFLs (In-unit) 16,311 15,538 51.80

Scorecard does not report savings per

measure

844,910 804,854 49.22 764,699 CFLs Candelabra (In-unit) 192 192 16.51 3,170 3,170 40.36 7,750 CFLs Globe (In-unit) 11,252 10,059 51.80 582,854 521,072 36.79 370,104 CFLs (Common Area) 1,289 1,289 51.00 65,739 65,739 324.81 418,681 CFLs Globe (Common Area) 1,245 1,245 51.00 63,495 63,495 242.82 302,310 LED Night Light (In-unit) 5,585 4,331 13.60 75,956 58,898 13.14 56,906 Total 105,675 96,461 NA 5,857,744 5,857,744 5,470,271 NA 5,300,381 Realization Rate 0.90 Note: Values rounded for reporting purposes

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Table 54. MFDI Ex Ante and Ex Post Gross Demand Savings


Verified Volume

Ex Ante Deemed

Savings (kW)

Ex Ante Reported

Savings (kW)

Audited Gross

Savings (kW)

Verified Gross

Savings (kW)

Ex Post per Measure

Savings (kW)

Ex Post Gross Savings

(kW)

IPL Electric CFLs (In-unit) 20,643 19,664 0.006

Scorecard does not

report savings per measure

124 118 0.006 119 CFLs Globe (In-unit) 23,973 21,432 0.006 144 129 0.005 97 CFLs (Common Area) 1,709 1,709 0.010 17 17 0.006 10 CFLs Globe (Common Area) 299 299 0.010 3 3 0.005 1 LED Night Light (In-unit) 6,972 5,406 0.000 0 0 0.000 0 Bathroom Aerators 5,363 5,115 0.008 43 41 0.011 54 Kitchen Aerators 4,735 4,372 0.008 38 35 0.007 32 Low-Flow Showerheads 5,924 5,626 0.023 136 129 0.017 95 Programmable Thermostat 183 183 0.000 0 0 0.157 29 IPL Joint CFLs (In-unit) 16,311 15,538 0.006

Scorecard does not


98 93 0.006 94 CFLs Candelabra (In-unit) 192 192 0.010 2 2 0.004 1 CFLs Globe (In-unit) 11,252 10,059 0.006 68 60 0.005 46 CFLs (Common Area) 1,289 1,289 0.010 13 13 0.006 8 CFLs Globe (Common Area) 1,245 1,245 0.010 12 12 0.005 6 LED Night Light (In-unit) 5,585 4,331 0.000 0 0 0.000 0 Total 105,675 96,461 NA 697 697 653 NA 593 Realization Rate 0.85 Note: Values rounded for reporting purposes

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Ex Post Net Savings The ex post net savings estimates are a reflection of savings attributed to the program after free ridership and spillover adjustments occur through the application of a net-to-gross (NTG) ratio estimate.

Evaluators typically calculate NTG estimates using survey participant self-reported answers to questions about what they would have done in the absence of the program and the influence the program had on their decision to implement additional energy efficiency projects after their participation in the MFDI program. While the program places measures directly in tenant spaces, the Evaluation Team did not survey tenants. Instead, the Evaluation Team assumed that, absent the program, the property managers would not have purchased and installed the measures provided by the program. In this situation, the NTG is 1.0.

However, as MFDI increasingly contributes more savings to the IPL DSM program portfolio, it will be important to establish NTG through a tenant survey in future evaluation years.

With an NTG of 1.0, the ex post net savings are the same as the ex post gross savings of 5,300,381 kWh and 593 kW for energy and demand savings, respectively.

Process Analysis Opinion Dynamics completed in-depth interviews with the MFDI program staff and implementers. The Evaluation Team also fielded a participant survey with 15 property managers who oversaw 15 of 77 participating program properties.

The survey’s primary objectives were to verify installation, establish installation rates, and collect information on program participation, knowledge increase, and customer satisfaction. Given the relatively few number of survey respondents, this report presents the data in absolute numbers (rather than percentages), and refrain from extrapolating this information to all the program participants.

Program Participation Almost all property managers reported receiving clear explanations regarding the program (n=14). Two-thirds (n=10) received program materials to share with their tenants, including letters containing information such as program benefits, the installation process, and schedule. Some report (n=3) receiving information on the program’s measures, and receiving additional program materials such pamphlets. One property manager distributed a notice to tenants about the program measures.

Table 55. MFDI Number of Participants who Received Program Information

Do you agree that… Yes No Not Involved

in Process Don't Know

Program staff clearly explained participation process (n=15) 14 0 1 0 I received program materials to share with tenants (n=15) 10 2 1 2

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Property managers overwhelmingly noted that the information they received was very useful, however, two respondents were either neutral or felt that the information was not very useful.

Table 56. MFDI Usefulness of Program Information Received

Component Not at All

Useful (0-2)

Not Very Useful (3-4)

Neutral (5)

Useful (6-7)

Very Useful (8-10)

Mean

Usefulness of Information (n=10) 0 1 1 0 8 8.3

Most properties installed program measures in at least 90% of the tenant units. The size of the properties varied, ranging from 37 to 532 tenant units, with a median of 228 units. Installations were as follows:

• One-half installed measures in all their tenant units.

• Seven installed in more than 90% of their units.

• One property installed in 75% of their units.

Where installations were not in all units, it was primarily due to refusal of residents to receive program measures.

Table 57. MFDI Reasons for Partial Installation of Program Measures Reasons Count (n=8)

Resident declined 5 Resident unavailable 2 Measure/s previously installed 1 No access to unit/apartment 1 Vacant unit/apartment 1

Knowledge Increase Property managers reported having some previous knowledge about energy efficiency prior to program participation. Yet, most reported an increase in knowledge through their participation in the program. Specifically, they noted learning of additional ways to save energy/cost, how to render buildings more energy efficient, and the amount of energy or water that can be saved through a collective effort.

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Figure 15. Energy Efficiency Knowledge Before and After Program Participation

Customer Satisfaction Overall, most property managers were satisfied with the program (Table 58).

Those who were satisfied (n=12) also indicated they would participate in the program again and that they would recommend the program to other property managers.

Table 58. MFDI Property Manager Satisfaction

Component Not at All Satisfied

(0-2)

Not Satisfied

(3-4)

Neutral (5)

Satisfied (6-7)

Very Satisfied

(8-10) Mean

Program Overall (n=15) 1 1 1 3 9 7.3

Drivers of positive satisfaction scores included:

• Time to complete installations (n=12)

• Time to schedule installations (n=11)

• The professionalism of TSI (n=10)

• The measure installed, in particular CFL bulbs (n=9)

• The program information (n=7)

Two property managers expressed dissatisfaction with the program overall (one with an 80-unit property, while the other with a 320-unit property). Primarily they were dissatisfied with the showerheads installed and reported they were leaky and caused additional work, while some CFLs took a long time to light up or did not work. . They also reported that participating in the program was onerous and felt dissatisfied with the lack of communication with program staff.

About half of the property managers felt more satisfied with IPL after participating in the program. Only one felt less satisfied.

Most property managers believe that their tenants were also satisfied with the program. Ten property managers indicated that their tenants were satisfied with the measures they received through the

3

75

6

13

5

Nothing Very little Some A lot

Level of Knowledge Before Program Knowledge Acquired through Program

n=15

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program; however, there were eight property managers who indicated program satisfaction who also reported that their tenants noted having issues with some of the measures installed (i.e., poor quality of showerheads and CFLs). Some tenants reported the poor installation of measures, measures being installed without permission, and that bulb shards were left on the floor after breaking during installation.

Figure 16. MFDI Favorability towards IPL since Participating in the Program (n=15)

Property managers suggested improvements to the MFDI program such as better communication or information sharing about the program (including program information to managers and tenants), higher quality measures, and proper installation of program measures.

Table 59. MFDI Suggestions to Improve MFDI Program Description Count (n=10)

Improve communication/information sharing 3 Install measures with better quality 3 Hire better implementation team 2 Make sure measures are installed properly 2 Install measures at a better time of the year 1 Offer LED lights 1

In addition, eight managers also suggested increasing the program measure offerings by including LED bulbs/fixtures and other lighting options, among others. Interestingly, two suggested adding regular maintenance to installed program measures

1 1

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0

2

4

6

8

Don't Know Less Favorable The Same More Favorable

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Table 60. MFDI Suggested Additional Program Measures Measure Count (n=8)

LED fixtures/bulbs 2 More energy efficient light bulbs 2 Maintenance of measures installed 2 Furnace filters 1 Power strips 1 Programmable thermostats 1 Toilet flappers 1

Program Influence Three property managers reported taking additional actions to make their buildings more energy efficient without incentives or rebates from IPL. These included installing additional lighting measures (including exterior and hallway lights), and adding toilet flappers. Only one of the managers reported that participating in the MFDI program highly influenced their decision to install additional energy efficient measures.

Table 61. MFDI Additional Energy Efficient Measures Installed (multiple response) Measure Installed Count (n=3)

New light fixtures 2 Toilet flappers 1 Hall lights 1 Exterior lighting 1

Property Characteristics All properties consist of rental units. The majority of the properties use electricity for heating and water heating (electricity n=10; gas n=5). Tenants pay their electric bills for the majority of properties (n=10).

Table 62. MFDI Tenant Payment for Gas and Electricity Payment of Gas and Electric Bill Count (n=15)

Tenants pay electricity only 10 Tenants pay both gas and electricity 4 Other 1

Ten of the 15 property managers reported that most tenants leave the energy efficient measures when moving out of the property.

Program Insights and Recommendations The MFDI program is the second largest contributor to IPL’s energy saving portfolio. The program exceeded its volume goal reach by serving more than 9,100 apartment units, and introduced new

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measures such as programmable thermostats and LED night lights. This section provides recommendations for reporting of program measure volumes and for tracking updates.

Conclusion 1. In general property managers indicated satisfaction with program measures and the program overall. In a few properties there were complaints about the low quality of some measures (most notably showerheads and aerators) and lack of contractor professionalism (where shards of broken bulbs were left behind). Managers also suggested expanding the measure offering to include energy saving measures such as power strips and LED bulbs and fixtures.

Recommendation:

• Ensure that program measures are operational (to prevent installing malfunctioning measures) and tested after installation (to identify poor installation) to both reduce removal of installed measures as well as increase satisfaction with the program.

• Consider expanding program offering to include cost-effective measures such as LED bulbs/fixtures and power strips.

Conclusion 2. The scorecard tracks number of tenant units serviced. The PY2014 scorecard tracked number of apartment units that participated in the program at 9,178. This data point, while interesting and which may provide some insights into the overall penetration rate of the program, is expected to be highly variable from year to year because properties are not built with consistent number of apartments. The number of total installed measures is a better indication of effectiveness of program reach and marketing, and consequently is a more effective metric of the levels of savings the program can expect.

Recommendation: The program scorecard should include total volume of measures installed as well as number of apartment units reached by the program.

Conclusion 3. The tracking database does not contain all necessary data to support evaluation. While the program offers CFLs with different wattages (i.e. 13-watt or 19-watt standard CFLs, between 9-watt and 14-watt globe CFLs, and 7-watt or 9-watt candelabra CFLs), it does not provide the number of CFLs by wattage in the program tracking database, therefore, for ex post purposes the Evaluation Team used an average of installed bulbs.

Recommendation: For a more accurate calculation of the program savings, the program database, to the extent feasible, should include the quantity of CFL measures by wattage, lamp type (i.e., standard, globe, candelabra), customer type (i.e., IPL Electric, IPL Joint), and area of installation (i.e., apartment unit, common area).

Conclusion 4. NTG is set at 1.0 because interviews focused on property managers.

Recommendation: The PY2015 EM&V efforts should include on-site visits to establish NTG values and installation rates. IPL should consider coordination with Citizen’s Gas to reduce EM&V costs of conducting the on-sites.

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Appliance Recycling Program

Program Description The IPL Appliance Recycling Program collects and recycles functioning primary and secondary appliances, such as refrigerators and freezers, with the intent of reducing energy load on the grid. The program also recycles room air conditioners (Room ACs) as an add-on service but only for customers who also recycle a refrigerator or freezer. The program implementer, JACO, removes the appliance from the customer’s residence, and disposes of the material in an environmentally friendly manner, recycling parts to the extent practical, all at no cost to the customer. The program also distributes CFLs.

The ultimate goal of the program is to remove inefficient appliances from the grid, thus reducing energy consumption associated with the measure.

Qualifying appliances must be in working condition, empty at the time of pick-up, and between 10 and 30 cubic feet in size (although the program explains the range to customers as “an average size appliance”). The program only accepts operating appliances, with the purpose of reducing load on the grid.

The program offers the following incentives and measures

• A $30 incentive per refrigerator/freezer (up to two units per household).

• A $20 incentive per room air conditioner (collected only from customers who also recycle either a refrigerator and/or a freezer).

• Free CFL kits (one kit with three 18W CFL lamps) to homeowners present at the time of appliance pick-up. Customers have the option to decline CFL kits.

Planned PY2015 Program Design Changes To increase volume of recycled appliances, the program increased the incentive payment per recycled appliance (refrigerators and freezers) to $40.00 as of January 2015. Incentives for Room ACs remain the same.

Appliance Efficiency Standards Refrigerators and freezers have become increasingly more efficient overtime, but especially after the national appliance standards were promulgated in 1990 (Table 63). Despite the increase in efficiency, the program removes older, inefficient appliances, as well as newer appliances that may have become secondary units.

Table 63. Refrigerator Energy Efficiency Changes Standard/Specification Change Description

1993 Federal Standard • 30% more efficient than the 1990 standard

1997 ENERGY STAR • 20% more efficient than the 1993 standard • 44% better than the 1990 standard

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Standard/Specification Change Description

2001 Federal Standard • 30% more efficient than the 1993 standard • 51% better than the 1990 standard



2008 ENERGY STAR • 15% more efficient than the 2001 standard • 61% better than the 1990 standard)

Program Implementation JACO, an appliance recycling company, is the implementer for this program. The following summarizes JACO’s contractual responsibilities toward IPL’s Appliance Recycling program:

• Overall implementation oversight

• Insurance certificate

• Marketing/advertising (including developing and executing marketing strategies complementary to IPL’s marketing efforts)

• Customer incentive check fulfillment

• Scheduling appliance pick up

• Maintaining call center

• Maintaining online scheduling website

• Maintaining customer database

• Picking up appliances (including disabling the unit and safely disposing of appliance’s toxic and ozone-destroying materials)

• Distributing CFL kits

• Recycling appliances

Customers participate in the program by either directly calling JACO’s call center or by enrolling online through the IPL website. Program staff screen customers for eligibility at the time of enrollment. Screening questions ensure that the applicant is an IPL customer (located within IPL’s service territory), the appliance is in operating condition, and the appliance meets program requirements (i.e. appliance cubic footage). As part of the enrollment and screening process, JACO schedules a pick-up appointment with the customer. During this initial scheduling, customers are notified of the date and time (a four-hour window) of their appointment, as well as the requirements for appliance pick-up (e.g., having a clear pathway for removal of the appliance, ensuring that the appliance is plugged in prior to the pick-up, ensuring that the unit is empty prior to pick-up). This information is provided online for those who schedule their appliance pick-up via the Internet.

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Customers receive a 48-hour notification via phone or e-mail as a reminder of their scheduled pick-up appointment and requirements. On the day of the customer’s appointment, the collection crew inspects the unit to confirm it is consistent with the information provided by the customer as part of the screening process.

After verifying the appliance meets all eligibility requirements, the collection crew then loads the unit on the truck, where it is permanently disabled (the power cord to the unit is removed, the gaskets on the refrigerator and freezer door are cut so the doors will not shut properly, and the cold control unit is destroyed). The unit is then transported to JACO’s recycling facility, where it is completely dismantled.

All units have two unique identifiers: a bar code and the appliance turn-in order number. These identifiers enable tracking of the unit through pick-up, transportation to the warehouse, and destruction. Once the unit is destroyed, the customer becomes eligible for the incentive payment. JACO mails the incentive checks directly to the customer, approximately three to four weeks after appliance pick-up.

Program Budget, Goals, and Scorecards In 2014, the program targeted 1% of residential households in the IPL service territory, or approximately 2,800 customers.

The program achieved 85% of its volume goals while spending 77% of the budget by recycling 2,389 appliances (the scorecard excludes distributed CFL bulbs from the reported volume). This represents a 9% increase over volume of recycled appliances in PY2013 (2,200 appliances).

The program reported 2,101,658 kWh in ex ante energy savings and 349 kW in ex ante demand savings, representing 92% and 87% of energy and demand savings goals, respectively. The reported values include savings attributed to the distributed CFL bulbs.

Table 64. Appliance Recycling Goals and Reported Results Metric PY2014 Goal PY2014 Reported (Scorecard) Percent of Goal

Budget $516,208 $398,241 77% Measure 2,820 2,389 85% kWh 2,272,600 2,101,658 92% kW 399 349 87%

Source: IPL DSM Oversight Board PY2014 Scorecard

The program served 2,204 unique participants (Table 65). About 5% recycled multiple appliances.

Table 65. Total Participants by Appliance Type Appliance Type Unique Participants Percent of Total

1 refrigerator 1,683 76.36% 1 freezer 346 15.70%

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Appliance Type Unique Participants Percent of Total 1 refrigerator/1 freezer 61 2.77% 2 refrigerators 52 2.36% 2 freezers 3 0.14% 1 refrigerator / 1 Room AC 45 2.04% 1 freezer / 1 Room AC 5 0.23% 2 refrigerators / 1 Room AC 1 0.05% 1 refrigerator / 2 Room ACs 2 0.09% 1 freezer / 2 Room ACs 3 0.14% 2 refrigerators / 2 Room ACs 1 0.05% 1 refrigerator / 1 Freezer / 1 Room AC 2 0.09% Total 2,204 100.00% Note: Values rounded for reporting purposes

EM&V Methodology For PY2014, evaluation activities focused on establishing program impact results. The Evaluation Team relied on last year’s survey of program participants for verification purposes and to establish the NTG value.

Table 66 presents activities that supported the Appliance Recycling program evaluation.

Table 66. Appliance Recycling EM&V Tasks Task Description

Program Manager and Implementer Interviews

• Interviewed program manager • Interviewed implementer (JACO Environmental)

Program Database Review/Verification

• Reviewed program-tracking database

Impact Analysis

• Calculated evaluated energy and demand savings values based on appliance characteristics and agreed-upon algorithm within the Indiana TRM and previously established part-use factor

• Applied verification rates from PY2013 participant survey for recycled appliances

• Applied installation rates from PY2013 participant survey for CFL measures • Applied NTG estimates from PY2013 participant survey

Impact Analysis This section outlines the evaluation steps to establish ex post gross and net energy and demand savings. To establish impact savings for the Appliance Recycling program, the Evaluation Team took the following steps:

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Step 1. Identified the quantity of recycled appliances in the database. Reviewed contents of the tracking database to identify volume of distributed CFL kits, as well as volume of recycled appliances including refrigerators, freezers and Room ACs.

Step 2. Reviewed characteristics of the recycled appliances and adjusted as needed. Reviewed contents of the tracking database to identify characteristics of recycled appliances including vintage, size, location, and whether the appliance was a primary or secondary appliance. Identified outlying appliance characteristics variables through the tracking database review, and adjusted to render appliance characteristics within probable values (primarily appliance age).

Step 3. Applied verification rates for recycled appliances. Applied a 100% verification rates from the PY2013 participant that confirmed that reported appliances were actually recycled.

Step 4. Applied installation rates for CFLs bulbs. Applied a 40% installation rate from the PY2013 participant survey that established the percentage of program CFL bulbs installed by customers.

Step 5. Applied ex post per-measure savings algorithms. Used adjusted appliance characteristics values (Step 5) to algorithms from the Indiana TRM to calculate ex post savings for recycled refrigerators, freezers, room air conditioners, and distributed CFLs.

Step 6. Selected ex post per-measure savings algorithms. Used adjusted appliance characteristics values (Step 5) to algorithms from the Indiana TRM to calculate ex post savings for recycled refrigerators, freezers, room air conditioners, and distributed CFLs.

Step 7. Calculated carryover CFL savings for CFLS distributed in PY2013, but installed in PY2014. The Uniform Methods Project (UMP)7 indicates that 97% of CFLs (and other light bulbs) not immediately installed and placed in storage become installed within four years of purchase. Thus, the ex post evaluation captures savings for CFL bulbs distributed in (PY2013) but installed in PY2014. This report defines these as “carryover savings.”

Step 8. Calculated ex post gross energy and demand savings. Summarized reported ex ante and ex post energy and demand savings.

Step 9. Applied Part-time Use Adjustment Factor. Given that not all recycled appliances were in continuous operations in the past 12 months, applied a part-time use adjustment factor (established through the PY2011 survey) to ex post savings values to calculate final impact numbers.

7 The UMP is a framework and set of protocols established by the U.S. Department of Energy for determining

energy savings from energy efficiency measures and programs. More details are available online: http://energy.gov/sites/prod/files/2015/02/f19/UMPChapter21-residential-lighting-evaluation-protocol.pdf Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Chapter 21: Residential Lighting Evaluation Protocol. Section 4.12 In-Service Rate. De 2014.

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Step 10. Calculated net energy and demand savings. Applied NTG values from the PY2013 participant survey to establish ex post net energy and demand savings.

Verified Measure Volume In this section, the Evaluation Team tabulates the information from the program tracking database (maintained by JACO) summarizing the number and type of recycled appliances, and the quantity of distributed CFLs.

The program tracking database indicated that 2,204 unique participants who recycled 2,389 appliances, including 1,901 (80% of total) refrigerators, 423 (18% of total) freezers, and 65 (2% of total) room air conditioners. About (60%) of the participants, or 1,313, received the CFLs kit. To establish the verified volume of measures by type, the Evaluation Team adjusted the tracked measure count using the following distinct steps:

Step 1: Determined the audited number of unique participants and measure volume. The Evaluation Team reviewed the program tracking database to determine the volume of recycled appliances and distributed CFLs. In summary, the review confirmed that the program recycled 2,389 appliances and distributed 3,938 CFLs. Ex ante installation rates were applied to the total tracking data measure volume for CFLs (4,425) to determine the audited volume of CFLs, resulting in 3,938 audited CFL measures.

Step 2: Calculated the quantity of PY2014 carryover CFLs. The ex post values for the Appliance Recycling program include additional savings for CFL measures distributed in PY2013, but only installed in the current program year, defined as carryover savings. Appendix I describes the methodology and assumptions used to calculate carryover savings. As per the UMP methods, 41% of distributed CFLs placed in storage the first year, are installed in the second year. As a result, the ex-post value includes savings attributed to 480 CFL bulbs installed in PY2014, but distributed a year earlier (see Evaluation TeamTable 67). Evaluation Team

Table 67. Appliance Recycling Quantity of Carryover CFLs Installed in PY2014

Program Year Measure Percent of Stored CFLs

Installed in PY2014 Total CFL Volume

in Storage Total Volume

Installed in PY2014 PY2013 18W CFL 41.0% 1,171 480 Note: Values rounded for reporting purposes.

Step 3: Apply installation rates to determine the verified measure volume. The Evaluation Team applied a 100% verification rate from the PY2013 participant survey to establish the verified volume of recycled appliances and 40% installation rate for the distributed CFLs (see Table 68).

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Table 68. Appliance Recycling Installation Rates Measure PY2014 Installation Rate (used for ex post analysis)

Refrigerator 100.00%

PY2013 EM&V Report Freezer 100.00% Room Air Conditioner 100.00% CFLs 40.12% Note: Values rounded for reporting purposes.

Step 4: Extract embedded installation rates from ex-ante deemed savings: The ex ante values have embedded installation rates for distributed CFLs. Therefore, to disentangle actual reported, audited, and verified volumes, the Evaluation Team first removed the embedded installation rates to arrive at the number of distributed CFLs, then applied updated installation rates as follows:

1. Confirmed ex ante CFL installation rates with IPL.

2. Applied ex ante installation rates to the ex ante volume to arrive at the audited CFL volume.

3. Applied installation rates from the PY2013 participant survey to the ex ante volume to calculate the verified volume.Evaluation Team

The audited volume of CFLs (applying the installation rate to volume rather than ex ante savings) is about 3,938 CFLs. Using IPL provided installation rates, the verified volume results in 1,775 installed CFLs in PY2014. After application of verification and installation rates, verified measure volume of 2,389 appliances and 1,775 CFLs result. Evaluation TeamTable 69 also includes carryover savings. In summary, the Evaluation Team applied ex post savings to 4,164 verified program measures and 480 carryover CFLs, for a program total of 4,644 verified measures (see Table 69)

Table 69. Appliance Recycling Verified Volume

Measure Ex Ante Volume

Ex Ante Installation

Rate Adjustment

Audited Volume

Verification/ Installation

Rate

Verified Volume

Refrigerator 1,901 N/A 0 1,901 100.00% 1,901 Freezer 423 N/A 0 423 100.00% 423 Room Air Conditioner 65 N/A 0 65 100.00% 65 Total Recycled Appliances

2,389 N/A 0 2,389 100.00% 2,389

Total CFLs 4,425 89.00% (487) 3,938 40.12% 1,775 Subtotal 6,814 N/A (487) 6,327 N/A 4,164 CFL Carryover PY2013a CFL – 18 watt N/A N/A N/A N/A N/A 480 Program Total 6,814 N/A (487) 6,327 N/A 4,644 Notes: Values rounded for reporting purposes. (a) Number of stored CFLs from PY2013 that customers installed in PY2014.

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Appliance Characteristic Factors and Adjustments The Evaluation Team reviewed the database to determine whether appliance characteristics are within reasonable range and meet program requirements. Highlights include:

Size Requirements: All appliances met program requirements of between 10 to 30 cubic feet, and the team did not make adjustments (Table 70).

Table 70. Refrigerator and Freezer Capacity (Cubic Feet) Breakdown Refrigerator

Freezer Cu. Ft. % N Cu. Ft. % N < 10 0% 0 < 10 0% 0 10 to 30 100% 1,901 10 to 30 100% 423 Total 1,901 Total 423

Appliance Age: The tracking database revealed few recycled appliances with ages outside of what is probable. Some appliances were newer than five years old or older than 50 years old. A review of similar databases across various jurisdictions tends to contain these wide ranges as well (the Indiana TRM notes that the program should target appliances 10 years or older). Table 71 shows the number of recycled appliances in the database for different age categories. Nearly 2% of refrigerators and freezers found in the database were older than 50 years old. For appliances that are older than 50 years old, the Evaluation Team capped the age at 50 and assigned a vintage of 50 years, for use in analysis.

Table 71. Refrigerator and Freezer Age Breakdown Refrigerator

Freezer Age (Years) % N Age (Years) % N Less than 5 0.5% 10 Less than 5 0.2% 1 5 to 9 3.2% 60 5 to 9 2.6% 11 10 to 14 14.1% 268 10 to 14 8.5% 36 15 to 19 23.5% 446 15 to 19 8.7% 37 20 to 29 44.6% 848 20 to 29 47.0% 199 30 to 39 11.8% 224 30 to 39 27.0% 114 40 to 50 1.7% 32 40 to 50 4.7% 20 > 50 0.7% 13 > 50 1.2% 5 Total 1,901 Total 423

In summary, capping appliance ages at 50 years affected 0.7% (13 records) of refrigerators and 1.2% (5 records) of freezers. Since this group constitutes a small percentage of participants, the overall impact to the gross savings is small.

Conditioned vs. Unconditioned Space: The Evaluation Team assumes that primary appliances operate in conditioned space. Secondary appliances can potentially operate in unconditioned space, therefore the location variable provided in the tracking database is more appropriate. Table 72 summarizes the number of recycled refrigerators and freezers operating in conditioned and unconditioned space.

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Table 72. Appliance Location Operation

Location Refrigerators (n=1,901) Freezers (n=423)

Conditioned Space

Unconditioned Space

Conditioned Space

Unconditioned Space

Primary Appliances Interior Space 26 - - - Basement - - - - Garage - - - - Kitchen 673 - - - Other 1 - - - Outdoors - - - - Porch - - - - Unknown - - - - Secondary Appliances Interior Space 32 - 15 - Basement 128 - 91 - Garage - 868 - 278 Kitchen - - 6 - Other 2 - 4 - Outdoors - 90 - 16 Porch - 60 - 10 Unknown - 21 - 3 Total 862 1,039 116 307

Table 73 and Table 74 provide the coefficients for both adjusted and unadjusted parameters by appliance type. Implementing the age cap (for appliances 50 years or older) slighting reduces the age coefficient, which marginally reduces the ex post per-measure savings.

Table 73. Appliance Recycling Unadjusted and Adjusted Refrigerator Factors

Refrigerators Ex Ante Values (Unadjusted)

Ex Post Values (Adjusted)

Adjusted Variables Age (Years) 22.10 22.06 Unadjusted Variables Size (Cubic Feet) 19.26 19.26 Dummy: Primary Appliance 0.37 0.37 Dummy: Unit Manufactured Pre-1990 0.40 0.40 Dummy: Single Door 0.03 0.03 Dummy: Side-by-Side 0.27 0.27 CDD(a) 3.32 3.32 HDD(a) 16.43 16.43 Dummy: In Garage 0.55 0.55

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Refrigerators Ex Ante Values (Unadjusted)


Notes: (a) CDDs and HDDs are the average daily values of CDD 65 and HDD 65 from the Indianapolis International Airport weather station for 25 years from 1976 to June 2001, from NOAA

Table 74. Appliance Recycling Unadjusted and Adjusted Freezer Factors

Freezer Ex Ante Values (Unadjusted)


Adjusted Variables Age (Years) 26.52 26.49 Unadjusted Variables Dummy: Unit Manufactured Pre-1990 0.65 0.65 Size (Cubic Feet) 15.55 15.55 CDD(a) 3.32 3.32 HDD(a) 16.43 16.43 Dummy: In Garage 0.73 0.73 Dummy: Chest Freezer 0.29 0.29 Notes: (a) CDDs and HDDs are the average daily values of CDD 65 and HDD 65 from the Indianapolis International Airport weather station for 25 years from 1976 to June 2001, from NOAA

Part-time Use Adjustment Factors Some customers may keep their secondary refrigerators or freezers unplugged at least some of the time, a practice that reduces the gross impact of appliance removal (as it did not consume energy while unplugged some or part of the time). A PY2011 survey with 121 program participants (71 who recycled refrigerators and 50 who recycled freezers) established that while 77% of refrigerators and 74% of freezers run all the time, the remaining refrigerators and freezers operate part of the year (data collected in weeks). Table 75 lists survey results and the associated part-time use factor of 0.852 for refrigerators and 0.838 for freezers. The Evaluation Team thus applies this part-time use adjustment factor to establish ex post gross energy and demand savings estimates.

Table 75. Appliance Recycling Part-time Use Factor by Appliance Type How often was

appliance plugged in?

Percent of Refrigerators

Percent of Freezers

Percent Run Time

Refrigerator Part-Use Factor

Adjustment

Freezer Part-Use Factor

Adjustment All the time 77% 74% 100% 0.771 0.740 Not at All 4% 4% 0% 0.000 0.000 1 Week 0% 2% 2% 0.000 0.000 4 Weeks 1% 0% 8% 0.001 0.000 7 Weeks 1% 0% 14% 0.002 0.000 8 Weeks 3% 2% 15% 0.004 0.003 12 Weeks 0% 2% 23% 0.000 0.005 16 Weeks 3% 0% 31% 0.009 0.000

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How often was appliance

plugged in?

Percent of Refrigerators

Percent of Freezers

Percent Run Time

Refrigerator Part-Use Factor

Adjustment

Freezer Part-Use Factor

Adjustment 24 Weeks 3% 2% 46% 0.013 0.009 26 Weeks 1% 4% 50% 0.007 0.020 28 Weeks 0% 2% 54% 0.000 0.011 30 Weeks 0% 4% 58% 0.000 0.023 35 Weeks 3% 2% 67% 0.019 0.013 36 Weeks 0% 2% 69% 0.000 0.014 40 Weeks 1% 0% 77% 0.011 0.000 50 Weeks 1% 0% 96% 0.014 0.000 Total 100% 100% 0.852 0.838

Notes: Values rounded for reporting purposes.

Ex Post Per-Measure Savings Algorithms This section details the algorithms for all program measures.

Refrigerators and Freezers The Evaluation Team calculated ex post per-measure savings using the data and algorithms from the Indiana TRM that considers the following factors:

• The coefficients are derived from a large set of more than 400 monitored refrigerators and freezers in various climate regions in California and Michigan.

• The algorithms include variables such as location, whether it was a primary or secondary unit, appliance type, age, and size.

• Algorithms are specific to appliance type.

• Results are consistent with other Indiana evaluations.

The Evaluation Team used the following equations from the Indiana TRM to calculate the ex post per-measure energy and demand savings for the recycled refrigerators and freezers:

Equation 4. Energy Savings for Recycled Refrigerators and Freezers

kWh Savings per appliance = Base kWh Savings * PAF

Base kWh SavingsRefrigerator = 365.25*(Intercept + (CAge*Age) + (CBefore1990*FBefore1990) + (CSize*Size) + (CSingleDoor* FSingleDoor) + (CSidebySide*FSidebySide) + (CPrimary*FPrimary) + (COutdoor,CDD*CDD*FOutdoor) + (COutdoor,HDD * HDD * FOutdoor)

Base kWh SavingsFreezer = 365.25*(Intercept + (CAge*Age) + (CBefore1990*FBefore1990) + (CSize*Size) + (CChest* FChest) + (COutdoor,CDD*CDD*FOutdoor) + (COutdoor,HDD * HDD * FOutdoor)

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Equation 5. Demand Savings for Recycled Refrigerators and Freezers

kW Savings per appliance = TAF x LSAF x (kWh Savings per appliance/8,760) Where:

• Part-time Use Factor (PAF) = Factor used to adjust for the amount of time refrigerators and freezers are plugged in annually; PAF for refrigerators is 0.852 and for freezers is 0.838

• Intercept = Intercept from regression model; for refrigerators it is 0.769 and for freezers it is 0.372

• Age = Actual age (or adjusted age for units > 50 years old) of the recycled appliance

• Size = Actual size of the recycled appliance (in cubic feet)

• Cooling Degree Day (CDD) = Average daily value of 3.32 from the Indianapolis International Airport weather station for 25 years from 1976 to June 2001, from NOAA

• Heating Degree Day (HDD) = Average daily value of 16.43 from the Indianapolis International Airport weather station for 25 years from 1976 to June 2001, from NOAA

• Temperature Adjustment Factor (TAF) = 1.21 (from the Indiana TRM)

• Load Shape Adjustment Factor (LSAF) = 1.074 (from the Indiana TRM)

Table 76 and Table 77 show the algorithm coefficients used for calculating ex post gross savings.

Table 76. Appliance Recycling Refrigerator UEC Regression Model Coefficients (Dependent Variable = Average Daily kWh, R2 = 0.29)

Independent Variables Coefficient p-Value VIF Intercept 0.769 <.0001 0.0 Age (Years) 0.008 0.016 2.0 Dummy: Unit Manufactured Pre-1990 0.827 <.0001 1.7 Size (Cubic Feet) 0.083 <.0001 1.9 Dummy: Single Door -1.316 <.0001 1.3 Dummy: Side-by-Side 0.862 <.0001 1.6 Dummy: Primary Appliance 0.642 <.0001 1.5 Interaction: CDD x Dummy: in Garage 0.031 <.0001 1.3 Interaction: HDD x Dummy: in Garage -0.049 <.0001 1.2

Table 77. Appliance Recycling Freezer UEC Regression Model Coefficients (Dependent Variable = Average Daily kWh, R2 = 0.47)

Independent Variables Coefficient p-Value VIF Intercept -0.372 0.043 0.0 Age (Years) 0.036 <.0001 2.0 Dummy: Unit Manufactured Pre-1990 0.632 <.0001 2.1 Size (Cubic Feet) 0.107 <.0001 1.2 Dummy: Chest Freezer -0.293 <.0001 1.2

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Interaction: CDD x Dummy: in Garage 0.047 <.0001 1.1 Interaction: HDD x Dummy: in Garage -0.052 <.0001 1.0

Room Air Conditioners The Evaluation Team used the following equations from the Indiana TRM to calculate the ex post per-measure energy and demand savings for the recycled room ACs:

Equation 6. Energy Savings for Recycled Room ACs kWh savings per appliance = kWhexist - (%replaced * kWhnew)

kWhexist = ((EFLHclg * size * (1/EERexist))/1000

kWhnew = ((EFLHclg * size * (1/EERnew))/1000

Equation 7. Demand Savings for Recycled Room ACs kW savings per appliance = kWexist - (%replaced * kWnew) * CF

kWexist = ((size * (1/EERexist))/1000

kWnew = ((size * (1/EERnew))/1000

Where:

• kWhexist = Recycled room AC annual energy consumption

• % Replaced = Average percentage of recycled room ACs that are replaced (from the Indiana TRM)

• kWhnew = Newly installed room AC annual energy consumption

• Effective Full Load Hours (EFLHclg) = Estimated hours to satisfy the cooling requirement for residents in Indianapolis

• Size = Actual size of the recycled room AC (in BTUh where 12,000 BTUh = 1 ton)

• EERexist = Efficiency rating of the recycled room AC (in units of EER)

• EERnew = Minimum efficiency rating for a newly installed room AC (in units of EER)

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many room ACs are expected to be in use and saving energy during the peak summer demand period

The tracking database did not include details for the individual recycled room ACs, therefore the Evaluation Team applied default values from the Indiana TRM to calculate the ex post per-measure savings (Table 78).

Table 78. Appliance Recycling Variable Assumptions for Recycled Room ACs Variable Unit Ex Post Assumption Source

EFLHclg Hours/yr 332 Indiana TRM Size BTUh 10,000 Indiana TRM

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Variable Unit Ex Post Assumption Source EERexist EER 7.7 Indiana TRM EERnew EER 9.8 Indiana TRM % Replaced % 76% Indiana TRM CF Factor 0.30 Indiana TRM

CFL Kits The Evaluation Team used the following equations to calculate the ex post per-measure energy and demand savings for the distributed CFL:

Equation 8. Energy Savings for CFLs kWh Savings per lamp = ((Wattsbase – WattsCFL) * HOU * WHFe/1000)

Equation 9. Demand Savings for CFLs kW Savings per lamp = (((Wattsbase – WattsCFL) * CF * WHFd/1000)

Where:

• Wattsbase = Incandescent-equivalent wattage of the lamp or bulb being replaced with a CFL

• WattsCFL = Actual wattage of the distributed CFLs

• Hours of Use (HOU) = Average number of hours a day the light is in use

• WHFe = Waste heat factor for energy use, accounts for the effects of more-efficient lighting on cooling energy use

• Coincidence Factor (CF) = A number between zero and one indicating how many CFLs are expected to be in use and saving energy during the peak summer demand period

• WHFd = Waste heat factor for demand, accounts for the effects of more-efficient lighting on cooling energy demand

The tracking database did not include the variable assumptions used to calculate ex ante savings for CFLs. Table 79 summarizes the ex post per-measure assumptions for CFLs.

Table 79. Appliance Recycling Variable Assumptions for CFLs

Variable Unit Ex Post

Assumption Source

Wattsbase Watts 75 Compared lumens with ENERGY STAR values WattsCFL Watts 18 Actual wattage of CFLs included in kit Hours of Use Hours/day 2.45 Indiana Lighting Logger Hours of Use Study (2013) WHFe Factor 0.939 Indiana TRM for Indianapolis for weighted HVAC type WHFd Factor 1.055 Indiana TRM for Indianapolis for weighted HVAC type CF Factor 0.11 Indiana TRM

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Gross Energy and Demand Savings Table 80 summarizes the PY2014 program ex ante and ex post per-measure savings values for all Appliance Recycling program measures.

Table 80. Appliance Recycled Ex Ante Deemed and Ex Post per Measure Savings

Measure Units Ex Ante Deemed Savings Ex Post per-Measure Savings

kWh kW kWh kW Refrigerator Per refrigerator 839.00 0.146 909.07 0.158 Freezer Per freezer 696.00 0.123 632.87 0.112 Window Air Conditioner Per room AC 174.00 0.157 173.70 0.157 CFLs (18W) Per lamp 51.39 0.006 48.25 0.007

Notes: Values rounded for reporting purposes Source: 2014 IPL Ex Ante Estimates spreadsheet Ex ante deemed savings differ from ex post per-measure savings for the following reasons:

• Refrigerators and Freezers: The minor differences between ex ante and ex post savings are normal variations since the ex post value in any given year is a result of specific appliance characteristics recycled in that year (vintage, size, model and related information). Compared to PY2012 (the source of IPL’s ex ante values) the main differences in appliance profiles include appliance age, size, location, and whether recycled refrigerator is primary or secondary.

• CFL: the primary differences stem from application of waste heat factors. Ex ante deemed savings do not account for waste heat factors whereas the ex post does as per guidance from the Indiana TRM. The Evaluation Team applied waste heat factors weighted by HVAC fuel type as the heating fuel type for each participant is unknown.

Table 81 and Table 82 summarize the program’s ex ante and ex post gross savings. The program, before CFL carryover savings,8 achieved approximately 2,092,791 kWh in ex post gross energy savings and 370 kW in ex post gross demand savings, with realization rates of 1.00 and 1.06, respectively.

Carryover savings added 23,173 kWh and 3 kW to the overall program energy and demand savings, respectively, adding 1% to program ex-post savings. The resulting ex post program contributions were 2,115,964 kWh in energy savings with a realization rate of 1.01 and 373 kW in demand savings with a realization rate of 1.07.

8 CFL carryover savings include additional savings for the CFL measures where participants stored CFLs received

in the past program year (PY2013) and installed them during PY2014.

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Table 81. Appliance Recycling Program Ex Ante and Ex Post Gross Energy Savings


(Scorecard)

Audited Volume

Verified Volume


Ex Ante Reported Savings (kWh)

Audited Gross

Savings (kWh)

Verified Gross

Savings (kWh)

Ex Post per-

Measure Savings (kWh)

Ex Post Gross

Savings (kWh)

Refrigerator Scorecard does not report volume per

measure

1,901 1,901 839.00 Scorecard does not

report savings per

measure

1,594,939 1,594,939 909.07 1,728,137 Freezer 423 423 696.00 294,408 294,408 632.87 267,702

Room AC 65 65 174.00 11,310 11,310 173.70 11,290

CFLs None Reported 3,938 1,775 51.39 202,380 91,226 48.25 85,661 Subtotal 2,389 6,327 4,164 N/A 2,101,658 2,103,037 1,991,883 N/A 2,092,791 Realization Rate 1.00 CFL Carryover PY2013a CFL – 18 watt N/A N/A 480 N/A N/A N/A N/A 48.25 23,173 Program Total 2,389 6,327 4,644 N/A 2,101,658 2,103,037 1,991,883 N/A 2,115,964 Realization Rate 1.01 Note: Values rounded for reporting purposes. (a) Number of stored CFLs from PY2013 that customers installed in PY2014.

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Table 82. Appliance Recycling Program Ex Ante and Ex Post Gross Demand Savings


(Scorecard)

Audited Volume

Verified Volume

Ex Ante Deemed Savings

(kW)

Ex Ante Reported Savings

(kW)

Audited Gross

Savings (kW)

Verified Gross

Savings (kW)

Ex Post per-

Measure Savings

(kW)

Ex Post Gross

Savings (kW)

Refrigerator Scorecard does not report volume per

measure

1,901 1,901 0.146 Scorecard does not

report savings per

measure

278 278 0.158 301

Freezer 423 423 0.123 52 52 0.112 47

Room AC 65 65 0.157 10 10 0.157 10

CFLs None Reported 3,938 1,775 0.006 25 11 0.007 12 Subtotal 2,389 6,327 4,164 NA 349 364 351 NA 370 Realization Rate 1.06 CFL Carryover PY2013a CFL – 18 watt N/A N/A 480 N/A N/A N/A N/A 0.007 3 Program Total 2,389 6,327 4,664 N/A 349 364 351 N/A 373 Realization Rate 1.07 Note: Values rounded for reporting purposes. (a) Number of stored CFLs from PY2013 that customers installed in PY2014.

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Ex Post Net Savings The ex post net savings estimates reflect the hypothesis that in the absence of the program some of the customers would have recycled and/or disposed of their appliances anyway. The Evaluation Team applied the NTG values established in the PY2013 participant survey. Table 83 provides the NTG ratio and source for each program measure.

Table 83. Appliance Recycling NTG Values

Measures NTG

Refrigerators 0.78 Freezers 0.76 Room Air Conditioners 0.74 CFLs(a) 0.54 Spillover(a) - Energy 0.02 Spillover(a) – Demand 0.01 Notes: Values rounded for reporting purposes (a) Established at a program level

Table 84 shows the resulting net energy and demand savings. The program contributed 1,656,196 kWh in net energy savings and 291 kW in net demand savings to the IPL DSM programs portfolio.

Table 84. Appliance Recycling Ex Post Net Energy and Demand Savings

Measure Ex Post Gross Savings NTG Ex Post Net Savings

kWh kW kWh kW kWh kW Refrigerators 1,728,137 301 0.78 0.78 1,347,947 235 Freezers 267,702 47 0.76 0.76 203,454 36 Room Air Conditioners 11,290 10 0.74 0.74 8,355 8 CFLs 85,661 12 0.54 0.54 46,257 6 Subtotal 2,092,791 370 0.77 0.77 1,606,013 285 Spillover NA NA 0.02 0.01 37,670 4 Total 2,092,791 370 0.79 0.78 1,643,683 289 CFL Carryover PY2013 CFL – 18 watt 23,173 3 0.54 0.54 12,513 2 Program Total 2,115,964 373 N/A N/A 1,656,196 291 Notes: Values rounded for reporting purposes

Program Insights and Recommendations The Appliance Recycling program is the third largest contributor to IPL’s energy saving portfolio. The program recycled a record number of appliances at 2,389 appliances and distributed 4,721 CFL bulbs. The Appliance Recycling program had a solid performance with energy and demand savings realization rates of 1.0 and 1.06, respectively. Recommendations thus focus on tracking data improvements only.

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Conclusion 1: Program tracking data does not clearly capture sources and variables associated with ex ante deemed savings assumptions. The tracking database did not contain details for the room ACs such as size, efficiency, and whether the room AC was replaced with a new unit. These variables can affect the savings associated with each recycled appliance. If collected in the future, the savings for these measures would more accurately reflect what is occurring within the program.

Recommendation:

• Collect Room AC detailed information such as size, efficiency, and whether the Room AC was a replacement or a new unit.

• Provide a written explanation of assumptions used to derive ex ante deemed savings that are in the program tracking database.

Conclusion 2. Volume of distributed CFL bulbs were not included in the scorecard. Should IPL choose to continue offering CFLs to customers who recycle appliances, they should include both the volume of distributed CFL bulbs based on the wattages of the bulbs (if they differ) and the energy and demand savings associated with these measures in the scorecard. Since the scorecard did include the total expected savings from the distributed CFLs, the realization rate is not a true reflection of how the program performed.

Recommendation: Include volume and savings of CFL bulbs in the scorecard. Additionally, provide the Evaluation Team with information regarding the wattages of the bulbs distributed at each home.

Conclusion 3: Program increased incentive payment to $40 per recycled appliance in PY2015. Incentive values for Room AC did not change. The program had increased goals, without an underlying change in program design. With the incentive change, the following year’s evaluations should focus on testing the effectiveness of the incentive increase in driving higher volume and in increasing customer satisfaction.

Recommendation: Assess effectiveness of incentive increase in both volume and customer satisfaction through the PY2015 evaluation and participant survey.

Conclusion 4. The deemed ex ante savings for CFLs within the tracking database include embedded program installation rates. Note that installation rates reflect first-year savings. Given the UMP, over a four-year period, up to 98% of all CFLs are installed, thus the program can capture carryover savings in future program year. Installations rates in these future years are determined by UMP, rendering it important that ex ante savings do not embed any particular year’s installation rates. Because of carryover savings, it is important that ex-ante savings be provided without the embedded installation rate.

Recommendation: Ex ante deemed savings values should not include embedded installation rates. For reporting program performance in the scorecard, it would be appropriate to apply the installation rates to the distributed CFLs volume. The tracking database should contain values for ex-ante deemed savings

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(without installation rates), total distributed CFLs (without installation rates). The installation rate used should be presented separately.

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Home Energy Inspector Program

Program Description The Home Energy Inspector (HEI) program offers energy efficient measures available to all IPL customers who create both an online account through IPL’s website and complete an online energy audit. The program uses a web-based home energy audit program provided by Apogee. Customers complete an online audit by entering information about their utility account(s) and answering a series of questions regarding their home characteristics. Upon completion, the online assessment tool produces a customized report with a list of recommendations to help increase energy efficiency. Additionally, participants are mailed a kit that includes energy efficient measures that they can install in their homes to help further reduce overall energy consumption. The program implementer (WECC) manages the delivery of the kits (i.e., mailing them to participants who complete the online home assessment).

The tool then produces a report with recommendations on actions customers can take to make their homes more efficient and reduce overall energy consumption.

The program mailed kits containing energy efficient measures from two different vendors. The first 379 kits that were sent out were from Regal Printing, which was the previous supplier of the program’s energy kits. IPL changed suppliers in PY2014. In 2014, IPL used Energy Federation, Inc. (EFI) to fulfill requests. The measures included in the energy kits from both Regal and EFI are the same, except that the EFI kits added one LED night light measure.

IPL delivers the HEI program in partnership with Citizen’s Gas, thus reaching all-electric as well as customers whose heating fuel is provided by the gas company. IPL claims savings for all kit measures for its all-electric customers (we designate them as IPL Electric). For customers who are also served by Citizen’s Gas (we designate them as IPL Joint), IPL only claims savings for the lighting measures.

Table 85 lists all the program measures, and notes whether savings for these measures are included in the current IPL electric evaluation.

Table 85. HEI Program Measures Kit Measures IPL Electric Savings IPL-Joint Savings

Two 1.5 GPM showerhead Yes No Two 1.0 GPM bath aerators Yes No One 1.5 GPM kitchen aerator Yes No Two T-2 13 Watt CFLs Yes Yes Two T-2 19 Watt CFLs Yes Yes One hot water thermometer No No One refrigerator thermometer No No One LED night light (EFI kits only) Yes Yes

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Program Budget, Goals, and Scorecards IPL reported that the program distributed 64% of the planned volume of energy kits, while spending 77% of its budget. The program doubled volume goals (compared to PY2013) to 3,700. IPL mailed a total of 2,370 energy kits, 854 to IPL Electric customers and 1,516 to IPL Joint customers.

IPL claims savings for all mailed measures to IPL Electric customers, and only the lighting measures for IPL Joint customers. The program reportedly met approximately half of energy and demand savings goals.

Table 86. HEI Goals and Reported Results Metric PY2014 Goals PY2014 Reported (Scorecard) Percent of Goals

Budget $195,934 $150,961 77% Kits 3,700 2,370 64% kWh 1,779,099 857,939 48% kW 173 86 50% Source: IPL DSM Oversight Board PY2014 Scorecard.

EM&V Methodology This section summarizes the evaluation efforts for the HEI program. Table 87 presents activities that supported the HEI program evaluation.

Table 87. HEI EM&V Tasks Task Description

Program staff interviews • Interviewed program manager • Interviewed program implementer (CLEAResult)

Program materials review • Reviewed program BRD Program Database Review/ Verification

• Reviewed participant data-tracking database and supporting documentation

Impact Analysis

• Input assumptions based on review of TRMs, industry studies and federal standards

• Established ex post gross energy and demand savings • Applied installation rates and NTG values from a survey with 70 participants

fielded to support the PY2013 evaluation • Established ex post net energy and demand savings

Impact Analysis This section outlines the evaluation steps to establish ex post gross and net energy and demand savings. To establish impact savings for the HEI program, the Evaluation Team took the following steps:

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Step 1: Reviewed data in tracking database. Reviewed contents of the tracking database to identify the volume of distributed energy kits, kit contents, and the total ex ante energy (kWh) and demand (kW) savings. Compared tracking data with reported scorecard energy and demand savings.

Step 2: Verified volume of measure. Given that the energy kit contains measures that may not be installed by recipients, the Evaluation Team applied self-reported installation rates from the PY2013 survey to establish the verified volume of program measures – that is, the number of measures, by measure type, for which the program can claim energy and demand savings.

Step 3: Updated variable assumptions used to calculate ex post savings. Reviewed ex post variable assumptions, updating with data from recent studies as applicable (refer to Appendix J for sources used).

Step 4: Calculated ex post per-measure savings. Used variable assumptions established in Step 3 with algorithms from the Indiana TRM to derive ex post savings by measure.

Step 5: Calculated carryover CFL savings from previous program years. The Uniform Methods Project (UMP)9 indicates that 97% of CFLs (and other light bulbs) not immediately installed and placed in storage, become installed within four years of purchase. Thus, the ex post evaluation captures savings for CFL bulbs distributed in previous program years (PY2011 through PY2013) that were installed in PY2014, and are defined herein as “carryover savings.”

Step 6: Calculated gross program ex post energy and demand savings. Summarized reported ex ante and ex post gross energy and demand savings and calculated a program realization rate.

Step 7: Calculated net energy and demand savings. Applied NTG values established through the PY2013 participant surveys (n=70) to establish the net energy and demand savings.

Verified Volume In PY2014, the program distributed 2,370 energy kits to 2,365 unique participants. To establish the verified volume of measure by type installed, the Evaluation Team checked and performed adjustments to the tracked measure count though several distinct steps:

Step 1: Determine audited number of unique participants, kit volume, and measure quantity. The Evaluation Team reviewed the program tracking database and identified five participants who received two energy kits. The implementer confirmed these five participants received multiple kits. Thus, no adjustments were made to ex ante volumes. In summary, the review confirmed that the program

9 The UMP is a framework and set of protocols established by the U.S. Department of Energy for determining

energy savings from energy efficiency measures and programs. More details are available online: http://energy.gov/sites/prod/files/2015/02/f19/UMPChapter21-residential-lighting-evaluation-protocol.pdf Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Chapter 21: Residential Lighting Evaluation Protocol. Section 4.12 In-Service Rate. De 2014.

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distributed 2,370 kits, containing 15,741 measures. Applying IPL supplied installation rates, we established the audited volume of distributed measures to be 11,679.

Step 2: Calculate Quantity of PY2014 Carryover CFLs. The PY2014 HEI program includes additional savings distributed in earlier program years, but only installed in the current year (carryover savings). Appendix J describes the methodology and assumptions used to calculate carryover savings using the Evaluation TeamUniforms Method Protocol (UMP) methodology.

Table 88 summarizes the number of previously distributed and stored CFLs distributed through the program’s energy kits but installed in PY2014. Using the UMP method, the Evaluation Team calculated carryover savings for 702 CFLs previously stored, but installed in PY2014. .

Table 88. HEI Quantity of Carryover CFLs Installed in PY2014 Program

Year Measure

Percent of Stored CFLs Installed in PY2014

Total CFL Volume in Storage

Total Volume Installed in PY2014

PY2011 13W CFL 0.5% 581 3 19W CFL 0.5% 581 3

PY2012 13W CFL 28.0% 370 104 19W CFL 28.0% 370 104

PY2013 13W CFL 41.0% 596 244 19W CFL 41.0% 596 244

Total N/A N/A 3,094 702 Note: Values rounded for reporting purposes.

Step 3: Apply installation rates to determine verified measure volume. Given that customers received energy kits irrespective of the measure type and quantity they plan to install, the program (and evaluation) assumes that only a percentage of mailed measures were actually installed. Reasons for not installing measures may include the possibility the energy kit did not reach the participant, damaged/malfunctioning measures, the participant’s inability/lack of willingness to install the measures, or measures subsequently removed after installation. The Evaluation Team thus applied installation rates to the distributed energy kit measures that were established in PY2013 through a participant survey.

As the program introduced a new measure (LED Night Lights) during PY2014, this measure installation rate is set at 100%. Participant surveys for future evaluation will collect applicable data to estimate installation rates for LED Night Lights.

Table 89 shows the installation rates for the PY2014 evaluation. Evaluation Team

Table 89. HEI Installation Rates Measure PY2014 Installation Rate (used for ex post analysis)

CFLs 85.73% PY2013 EM&V Report

Bath Aerator 48.58%

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Measure PY2014 Installation Rate (used for ex post analysis) Kitchen Aerator 51.11% Showerhead 39.29% LED Night Light 100.00% New measure Note: Values rounded for reporting purposes.

The ex ante values have embedded installation rates for the distributed program measures. Therefore, to disentangle actual reported, audited and verified volume, the Evaluation Team first removed the embedded installation rates to arrive at the number of distributed measures, then applied updated installation rates as follows:

• Step 1: Confirmed ex ante installation rates with IPL.

• Step 2: Applied ex ante installation rates to the ex ante volume to calculate audited volume of measures.

• Step 4: Applied installation rates from the PY2013 participant survey to the ex ante volume to calculate the verified volume.

Table 90 highlights the resulting values. Using updated installation rates results in a verified volume of 12,055 installed measures during PY2014. The table also includes 702 verified carryover CFLs from previous program years (2011–2013). In summary, the Evaluation Team applied ex post savings to 12,055 verified program measures and 702 carryover CFLs, for a program total of 12,757 verified measures.

Table 90. HEI Verified Volume

Measure Tracking Data

Measure Volume

Tracking Data Installation

Rate

Audited Volume

Adjusted Installation

Rate Verified Volume

Unique Customers 2,365 N/A 2,365 N/A 2,365 IPL Electric CFL - 13 watt 1,708 89.00% 1,520 85.73% 1,464 CFL - 19 watt 1,708 89.00% 1,520 85.73% 1,464 LED Night Light 475 50.00% 238 100.00% 475 Bathroom Aerator 1,708 49.00% 837 48.58% 830 Kitchen Aerator 854 57.00% 487 51.11% 436 Low-Flow Showerhead 1,708 54.00% 922 39.29% 671 IPL Joint CFL – 13 watt 3,032 89.00% 2,698 85.73% 2,599 CFL – 19 watt 3,032 89.00% 2,698 85.73% 2,599 LED Night Light 1,516 50.00% 758 100.00% 1,516 Subtotal 15,741 N/A 11,679 N/A 12,055 CFL Carryover PY2011 – PY2013a

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Measure Tracking Data

Measure Volume

Tracking Data Installation

Rate

Audited Volume

Adjusted Installation

Rate Verified Volume

CFL - 13 watt N/A N/A N/A N/A 351 CFL - 19 watt N/A N/A N/A N/A 351 Program Total 15,741 N/A 11,679 N/A 12,757 Notes: Values rounded for reporting purposes. (a) Number of stored CFLs from PY2011, PY2012, and PY2013 that customers installed in in PY2014.

Ex Ante Deemed Savings Table 91 shows the ex ante deemed savings per kit measure. Through the database review, the confirmed that the appropriate ex ante deemed savings was applied for each kit measure. As discussed previously, the program embedded installation rates in its ex ante savings. To establish audited and verified savings, the Evaluation Team removed the embedded installation rate to arrive at ex ante adjusted deemed savings. Table 91 shows the resulting, adjusted ex ante deemed savings values for PY2014.

Table 91. HEI Ex Ante Deemed Savings

Measure

Within Program Tracking Database

Program Assumed

Installation Rate

Calculated for Direct Comparison to Ex Post Values

Ex Ante Deemed Savings (kWh)a

Ex Ante Deemed

Savings (kW)a

Ex Ante Adjusted Deemed Savings (kWh)b

Ex Ante Adjusted Deemed

Savings (kW)b

IPL Electric CFL - 13 watt 23.93 0.005 89.00% 26.89 0.006 CFL - 19 watt 28.52 0.006 89.00% 32.04 0.007 LED Night Light 6.80 0.000 50.00% 13.60 0.000 Bathroom Aerator 17.10 0.004 49.00% 34.90 0.008 Kitchen Aerator 79.62 0.005 57.00% 139.68 0.009 Low-Flow Showerhead 207.27 0.012 54.00% 383.83 0.022 IPL Joint CFL - 13 watt 45.69 0.005 89.00% 51.34 0.006 CFL - 19 watt 54.44 0.006 89.00% 61.17 0.007 LED Night Light 6.80 0.000 50.00% 13.60 0.000

Note: Values rounded for reporting purposes (a) Includes embedded installation rates. (b) Value calculated by dividing ex ante deemed savings by the program assumed installation rate. These values are directly comparable to the ex post per-measure values.

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The ex ante deemed savings for CFLs apply waste heat factors based on the customer type (i.e., IPL Electric, IPL Joint). The customer type indicates the primary heating fuel type. For those with electric space heating (IPL Electric) the CFL savings are nearly half the savings for those with gas heated homes (IPL Joint). Waste heat factors for electrically heated homes account for increased heating loads as more energy is needed to supplement the heat that was once given off by the incandescent lamps, thus resulting in an energy savings penalty.

Ex Post per-Measure Savings The Evaluation Team referred to the Indiana TRM for variable assumptions for energy and demand savings for energy kit measures distributed through the program. Where data was not available in the IN TRM, the Evaluation Team referenced data from the 2012 Residential Indiana Baseline Study, Michigan Water Study,10 or other sources. The review focused on best-available data from multiple data sources, prioritizing Indiana-specific data where available. Appendix J contains specific algorithms, variable assumptions, and specific references for all program measure ex post variables.

Table 92 compares the adjusted ex ante deemed savings and ex post per-measure savings per energy kit measure.

Table 92. HEI Ex Ante Deemed and Ex Post per Measure Savings

Measure Adjusted Ex Ante Deemed

Savings Ex Post per Measure Savings

kWh kW kWh kW IPL Electric CFL - 13 watt 26.89 0.006 23.31 0.006 CFL - 19 watt 32.04 0.007 27.77 0.007 LED Night Light 13.60 0.000 13.64 0.000 Bathroom Aerator 34.90 0.008 28.23 0.010 Kitchen Aerator 139.68 0.009 333.89 0.013 Low-Flow Showerhead 383.83 0.022 245.33 0.021 IPL Joint CFL - 13 watt 51.34 0.006 44.92 0.006 CFL - 19 watt 61.17 0.007 53.52 0.007 LED Night Light 13.60 0.000 13.64 0.000 Note: Values rounded for reporting purposes

The Evaluation Team has no insight into the input assumptions used to derive the deemed ex ante savings. While IPL program staff indicated that the implementer referenced the Indiana TRM, the ex ante deemed savings are not replicable. However, the Evaluation Team acknowledges that the ex post values are significantly larger than ex ante deemed savings values for low-flow kitchen aerators and

10 Michigan Evaluation Working Group Showerhead and Faucet Aerator Meter Study. June 2013.

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significantly smaller for showerheads. The Evaluation Team updated ex post input assumptions for these measures using data from more recent studies. Table 93 summarizes the updated variables and the source for each variable.

Table 93. HEI Updated PY2014 Variable Assumptions

Measure Variable PY2014 Ex Post Value

PY2014 Ex Post Source

Bathroom Aerator

Number faucets per household

3.15 Statewide average (weighted) from the Residential Indiana Baseline Market Study (2012)

Minutes per day per person 1.6 Michigan Evaluation Work Group Showerhead and Faucet Aerator Meter Study from 2013 Mixed water temperature 86 oF

Kitchen Aerator

Minutes per day per person 4.5 Michigan Evaluation Work Group Showerhead and Faucet Aerator Meter Study from 2013 Mixed water temperature 93 oF

Low-Flow Showerheads

Showerheads per household

1.63 Statewide average (weighted) from the Residential Indiana Baseline Market Study (2012)

Minutes per shower per person per day

7.80 Michigan Evaluation Work Group Showerhead and Faucet Aerator Meter Study from 2013 Showers per person per day 0.60

Mixed water temperature 101 oF

Ex Post Gross Savings Table 94 and Table 95 show the program’s ex ante reported savings, verified savings, and ex post gross savings. The program, before CFL carryover savings,11 achieved approximately 691,550 kWh in ex post gross energy savings and 77 kW in ex post gross demand savings, with realization rates of 0.81 and 0.90, respectively.

Carryover savings add 29,213 kWh and 4 kW to the overall program energy and demand savings, respectively. Carryover CFL savings account for 4% of the program savings. The resulting ex post program contributions are 720,763 kWh in energy savings with a realization rate of 0.84, and 81 kW in demand savings with a realization rate of 0.95.

11 CFL carryover savings includes additional savings for the CFL measures where participants stored CFLs received

in past program years (PY2011 – PY2013) and installed them during PY2014.

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Table 94. HEI Ex Ante and Ex Post Gross Energy Savings


Verified Volume

Updated Ex Ante Deemed Savings (kWh)

Ex Ante Reported Savings (kWh)

Audited Gross

Savings (kWh)

Verified Gross

Savings (kWh)

Ex Post per Measure Savings (kWh)

Ex Post Gross

Savings (kWh)

IPL Electric CFL - 13 watt 1,520 1,464 26.89

Scorecard does not


40,872 39,370 23.31 34,124 CFL - 19 watt 1,520 1,464 32.04 48,712 46,922 27.77 40,659 LED Night Light 238 475 13.60 3,230 6,460 13.64 6,477 Bath Aerator 837 830 34.90 29,207 28,956 28.23 23,420 Kitchen Aerator 487 436 139.68 67,995 60,966 333.89 145,729 Low-Flow Showerhead 922 671 383.83 354,017 257,552 245.33 164,617 IPL Joint CFL - 13 watt 2,698 2,599 51.34 138,532 133,440 44.92 116,748 CFL - 19 watt 2,698 2,599 61.17 165,062 158,995 53.52 139,104 LED Night Light 758 1,516 13.60 10,309 20,618 13.64 20,673 SUBTOTAL 11,679 12,055 N/A 857,939 857,937 753,278 N/A 691,550 Realization Rate 0.81 CFL Carryover PY2011 – PY2013 CFL - 13 watt N/A 351 N/A N/A N/A N/A N/A 13,330 CFL - 19 watt N/A 351 N/A N/A N/A N/A N/A 15,883 PROGRAM TOTAL 11,679 12,757 N/A 857,939 857,937 753,278 N/A 720,763 Realization Rate 0.84 Notes: Values rounded for reporting purposes.

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Table 95. HEI Ex Ante and Ex Post Gross Demand Savings


Verified Volume

Updated Ex Ante Deemed Savings (kW)

Ex Ante Reported Savings

(kW)

Audited Gross

Savings (kW)

Verified Gross

Savings (kW)

Ex Post per

Measure Savings

(kW)

Ex Post Gross

Savings (kW)

IPL Electric CFL - 13 watt 1,520 1,464 0.006

Scorecard does not

report savings per

measure

9 9 0.006 8 CFL - 19 watt 1,520 1,464 0.007 11 10 0.007 10 LED Night Light 238 475 0.000 0 0 0.000 0 Bath Aerator 837 830 0.008 7 7 0.010 8 Kitchen Aerator 487 436 0.008 4 3 0.013 6 Low-Flow Showerhead 922 671 0.023 21 15 0.021 14 IPL Joint CFL - 13 watt 2,698 2,599 0.006 16 16 0.006 14 CFL - 19 watt 2,698 2,599 0.007 19 18 0.007 17 LED Night Light 758 1,516 0.000 0 0 0.000 0 SUBTOTAL 11,679 12,055 N/A 86 87 78 N/A 77 Realization Rate 0.90 CFL Carryover PY2011 – PY2013 CFL - 13 watt N/A 351 N/A N/A N/A N/A N/A 2 CFL - 19 watt N/A 351 N/A N/A N/A N/A N/A 2 PROGRAM TOTAL 11,679 12,757 N/A 86 87 78 N/A 81 Realization Rate 0.95 Notes: Values rounded for reporting purposes.

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Ex Post Net Estimates To calculate net energy and demand savings, the Evaluation Team applied NTG values calculated through a participant survey with 70 customers that supported the PY2013 evaluation.

The participant survey NTG estimates are based on self-reported answers to questions on what customers would have done in the absence of the program as well as the influence the program had on their decision to install the energy efficient measures.

For CFLs, the questions identified whether participants had purchased CFLs in the past, and if so how many CFLs were purchased. The underlying assumption is that customers who purchased a relatively lower number of CFLs in the past would be less likely to purchase more CFLs on their own compared to those who had previously purchased a larger volume of CFLs. Questions also addressed the likelihood that participants would have changed their lights to CFLs in the absence of the program, and the timing associated with this change, if applicable. Further, the Evaluation Team asked questions about the influence of the program in their decision to install the CFLs.

For the water conservation measures, the free ridership question asks participants whether they already had the measure installed in their home prior to the program.

The Evaluation Team calculated a spillover score by asking participants whether they had taken additional energy saving actions after participating in the program. The team calculated a spillover factor for those customers who attributed a high degree of influence (8 or higher on a 10-point scale) to the program in their decision to undertake the additional energy saving action(s).

Table 96 shows the NTG (including free ridership and spillover) values for each of the program measures.

Table 96. HEI NTG Values

Measures NTG (1-

Freeridership) Spillovera

kWh and kW kWh kW CFLs 0.78

0.10 0.16 Bathroom Aerator 0.83 Kitchen Aerator 0.81 Low-Flow Showerhead 0.75 Notes: (a) established at a program level (rather than measure level). Values rounded for reporting purposes.

Table 97 shows the resulting net energy and demand savings. The program contributed 638,631 kWh and 76 kW in energy and demand savings, respectively.

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Table 97. HEI Ex Post Net Savings

Measure Ex Post Gross Savings NTG Ex Post Net Savings kWh kW kWh kW kWh kW

IPL Electric CFL -13 watt 34,124 8 0.78 0.78 26,617 6 CFL-19 watt 40,659 10 0.78 0.78 31,714 8 LED Night Light 6,477 0 1.00 1.00 6,477 0 Bath Aerator 23,420 8 0.83 0.83 19,438 7 Kitchen Aerator 145,729 6 0.81 0.81 118,041 5 Low-Flow Showerhead 164,617 14 0.75 0.75 123,462 11 IPL Joint CFL – 13 watt 116,748 14 0.78 0.78 91,063 11 CFL – 19 watt 139,104 13 0.78 0.78 108,501 13 LED Night Light 20,673 0 1.00 1.00 20,673 0 Subtotal 691,550 77 0.79 0.78 545,986 60 Spillover N/A N/A 0.10 0.16 71,921 13 Total 691,550 77 0.89 0.95 617,908 73 CFL Carryover PY2011 – PY2013 CFL – 13 watt 13,330 2 0.71 0.71 9,424 1 CFL – 19 watt 15,883 2 0.71 0.71 11,229 2 Subtotal 29,213 4 N/A N/A 20,723 3 Program Total 720,763 81 N/A N/A 638,631 76 Notes: Values rounded for reporting purposes.

Program Insights and Recommendations The HEI Program distributed slightly more energy kits than in the previous years. The information provided in the tracking database was complete. As the Evaluation Team did not field participating surveys this year, there are no process recommendations. Thus, the recommendations focus on better documenting the input variables for ex ante values so that differences between ex ante deemed savings and ex post per measure savings can be better explained.

Conclusion 1: There was a slight increase in the number of energy kits distributed by the program in PY2014 compared to more aggressive savings targets. The program distributed slightly more energy kits in PY2014 at 2,370 distributed kits compared to 2,355 in PY2013. However, while the number of distributed energy kits increased, compared to goals the program underperformed. The program goals doubled, primarily because the linkage with Citizen’s Gas was reestablished.

Recommendation: IPL should review historic program performance to establish program targets that can be realistically achieved given underlying marketing and outreach efforts.

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Conclusion 2. Online audit savings are currently captured through spillover calculations from participant surveys. IPL may consider understanding behavioral savings through a billing analysis.

Recommendation: Consider a billing analysis to quantify online assessment savings.

Conclusion 3. The IPL Peer Comparison Report reaches the majority of the residential customer population within the IPL service territory. The HEI program could establish marketing links with the Peer Comparison program where the HERs points report recipients to the online assessment. Recommendation: Consider cross marketing the program by adding a weblink reference in the HERs directing households to complete an online assessment through the HEI program.

Conclusion 4. The deemed ex ante savings for CFLs within the tracking database include embedded program installation rates. Note that installation rates reflect first-year savings. Given the UMP, over a four-year period, up to 98% of all CFLs are installed, thus the program can capture carryover savings in future program year. Installations rates in these future years are determined by UMP, rendering it important that ex ante savings do not embed any particular year’s installation rates. Because of carryover savings, it is important that ex-ante savings be provided without the embedded installation rate.

Recommendation: Ex ante deemed savings values should not include embedded installation rates. For reporting program performance in the scorecard, it would be appropriate to apply the installation rates to the distributed CFLs volume. The tracking database should contain values for ex-ante deemed savings (without installation rates), total distributed CFLs (without installation rates). The installation rate used should be presented separately.

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CoolCents Program—Residential and C&I

The objective of the Air Conditioner Cycling Program, or Air Conditioning Load Management (ACLM) program, is to reduce peak load by curtailing air conditioning during peak usage periods (summer months between June and September). This program focuses both on C&I and Residential customers, but reports savings and performance separately.

The objective of the Air Conditioning Load Management (ACLM) program is to reduce peak load by curtailing air conditioning (AC) use during peak usage periods between May 1 and September 30. This program focuses on residential customers. IPL also manages a C&I ACLM program with similar offerings to nonresidential customers. This evaluation focuses on residential premises only.

Peak load reduction is enabled through an adaptive load control receiver (LCR) installed outside the home near the central air conditioning unit. The LCR receives signals from IPL via a paging network to initiate and terminate operation. When the LCR is on, it cycles the air conditioner on and off at set time periods or duty cycle.

Customers receive a $5/month/unit credit on their electric bill during the summer months from June to September, or $20 per program season. By the end of 2014, the AC Cycling program had over 37,000 active load switches installed in residential premises and over 3,500 load switches installed in commercial premises.

Program Budgets, Goals, and Scorecards In PY2014, the ACLM program achieved 99% of its residential and 77% of its C&I enrollment targets, contributing 91% and 77% of demand savings goals for the residential and C&I programs, respectively.

Table 98. Residential CoolCents Program Goals and Reported Results Metric Goal Reported (Scorecard) Percent of Goal

Budget $2,046,241 $1,646,615 80% Measure 37,511 37,061 99% kWh 429,524 392,974 91% kW 34,936 31,646 91% Source: IPL DSM Oversight Board Monthly DSM Scorecard, December 2014

Table 99. Residential CoolCents Program Goals and Reported Results Metric Goal Reported (Scorecard) Percent of Goal

Budget $287,071 $31,389 11% Measure 4,595 3,526 77% kWh 24,259 18,617 77% kW 1,893 1,453 77% Source: IPL DSM Oversight Board Monthly DSM Scorecard, December 2014

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Due to the delay in collecting the load switch data for analysis, the Evaluation Team will provide the CoolCents programs impact results in a separate memo with an expected delivery date around August 2015.

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Appendix A: Self-Report Net-to-Gross Evaluation Methodology

Net-to-gross (NTG) estimates serve a critical role in demand-side management (DSM) program impact evaluations, allowing utilities to determine portions of gross energy savings influenced by and attributable to their DSM programs, free from other influences. Two components constitute NTG: freeridership and spillover. Freeriders are customers who would have purchased a measure without a program’s influence. Spillover is the amount of additional savings obtained by customers investing in additional, energy-efficient measures or activities due to their program participation.

Various methods can be used to estimate program freeridership and spillover. This evaluation’s baseline approach used self-reports, procured through participant surveys.

Survey Design Direct questions (such as: “Would you have installed measure e X without the program incentive?”) tend to result in exaggerated “yes” responses. Participants often provide answers they believe surveyors seek; so a question becomes the equivalent of asking: “Would you have done the right thing on your own?”

Effectively avoiding such bias involves asking a question in several different ways, then checking for consistent responses. Programs can affect more than a customer’s decision to purchase an energy-efficient measure: they can influence a customer’s purchase of an energy-efficient measure sooner than planned, purchases of a higher-efficiency measure than planned, or purchases of more units than planned (in the program’s absence).

Determining freeridership estimates from a series of questions rather than using a single question helps to form a picture of the program’s influence on the participant. (For example: Did the program affect the timing of their decision and, if so, by how many months/years? Did the program affect the efficiency of equipment installed and, if so, by how much? Did the program affect the quantity of technology installed and, if so, by how much?) Use of multiple questions also provides a consistency check.

Not all questions are weighted equally. For example, if the respondent would not have installed measures at the same efficiency level without the program, they automatically become a 0% freerider. If they would not have installed the measures within two years without the program, they automatically become a 0% freerider. Other questions included in the freeridership analysis are assigned partial weights for responses indicative of a non-freerider.

Using this method does not allow a respondent to be estimated as a 100% freerider based on a single answer to a single question; a customer would have to provide consistent responses across relevant questions included in the freeridership analysis.

The Evaluation Team designed survey questions to determine why customers installed a given measure and what influence the program had on their decisions. The survey sought to establish what decision

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makers might have done in the program’s absence, using seven core freeridership questions to address the issue:

1. Would participants have installed the measures without the program?

2. Were participants planning on ordering or installing the measures before learning about the program?

3. Would participants have installed the measures at the same efficiency levels without the program incentive?

4. Would participants have installed the same quantity of measures without the program?

5. In the program’s absence, when would respondents have installed the measures?

6. Was the purchase of measures in the organization’s most recent capital budget?

7. Did the incentive increase the company’s return on investment (ROI) for the project, thereby allowing the project to receive implementation funding?

The spillover survey sought to answer three primary questions:

1. Since participating in the evaluated program, did participants install additional energy-efficient equipment or services?

2. How influential was the evaluated program on the participants’ decisions to install additional energy-efficient equipment in their facilities?

3. Did customers receive incentives for additional measures installed?

Freeridership Survey Questions The participant survey freeridership portion included 13 questions, addressing the seven core freeridership questions. The Evaluation Team included several skip patterns in the survey design, which allowed interviewers to confirm respondents’ previous answers by asking the same question in a different format. Freeridership questions asked in the survey included the following:

1. Without the [REWARD AMOUNT] rebate from IPL, would your business have paid the full cost to complete the exact same project at the same time?

2. [Ask if question 1 is Yes] Had your organization already ordered or purchased the equipment that was rebated before your organization heard about the program?

3. [Ask if question 1 is Yes] Did your organization have specific plans to install the same equipment BEFORE learning about the IPL program rebate?

4. [Ask if question 1 is Yes and if question 3 is Yes] Prior to hearing about the IPL rebate program, was the equipment you ultimately installed included in the organization’s capital budget?

5. [Ask if question 1 is Yes and if measure quantity > 1] And would you have installed the same quantity of equipment without the incentive?

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6. [Ask if question 1 is Yes] Without the incentive from IPL would your organization have still purchased and installed this project that was just as efficient, more efficient, or less efficient than the one you purchased?

7. [Ask if question 1 is Yes] When would you have installed this equipment without the program? Would you have installed it…. At the same time? Within one to two years? Within three to five years?

8. [Ask if question 1 is Yes] Did the IPL rebates for this project allow you to increase the project’s ROI so that it met the company’s internal ROI requirements for capital allocation, thereby allowing the project to receive implementation approval?

9. [Ask if question 1 is No] So, just to confirm, you would not have completed this project if the IPL rebates were not available. Is that correct?

10. [Ask if question 9 is No and if measure quantity > 1] Would you have installed similar but less energy-efficient equipment?

11. [Ask if question 9 is No] Would you have installed less equipment without the rebate?

12. [Ask if question 9 is No] In terms of project timing, when would you have completed the project?… At the same time? Within one to two years? Within three to five years?

13. [Ask if question 9 is No] Did the IPL rebates for this project allow you to increase the project’s ROI so that it met the company’s internal ROI requirements for capital allocation, thereby allowing the project to receive implementation approval?

Spillover Survey Questions As noted, spillover questions sought to determine whether program participants installed other energy-saving measures after participating in the program. Savings participants received from additional measures would be considered spillover savings if they met the following conditions:

• The program significantly influenced their decisions to purchase additional measures; and

• They did not receive additional incentives for those measures.

If the participant installed one or more measures, additional questions addressed the quantity they installed and the program’s influence (e.g., highly influential, somewhat influential, or not at all influential) on their purchasing decisions.

The Evaluation Team combined freeridership and spillover questions in the same survey, simultaneously asking the questions via telephone interviews with randomly selected program participants. Prior to undertaking the live participant phone calls, the team worked with the survey company to pretest the survey, ensuring all appropriate prompts and skip patterns would be followed. The team also monitored initial phone calls to verify the following:

• Survey respondents understood the questions; and

• Adjustments were not required.

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Freeridership Methodology The Evaluation Team developed a transparent, straightforward matrix for assigning scores to participants, based on their objective responses to targeted survey questions. Question response patterns received freeridership scores, with confidence and precision estimates calculated depending on the distribution of these scores (a specific approach for this technique can be found in the NAPEE Handbook on DSM Evaluation, 2007 edition, page 5-1).

Response patterns and scoring weights remained explicit; they could be discussed and changed, with results shown in real time. The approach provided the following, other important features:

• Derivation of a partial freeridership score, based on the likelihood of a respondent taking similar actions in the incentive’s absence;

• Use of a rules-based approach for consistency among multiple respondents;

• Use of open-ended questions to ensure quantitative scores matched respondents’ more detailed explanations regarding program attribution; and

• The ability to change weightings in a “what if” exercise (for testing the response set’s stability).

The Evaluation Team’s method offered a key advantage in introducing the concept of partial freeridership. Experience has shown program participants do not fall neatly into freerider and non-freerider categories. Consequently, the study assigned partial freeridership scores to participants with plans to install a measure; though the program exerted some influence over their decisions, other market characteristics outside the program also proved influential. Further, with partial freeridership, “don’t know” and “refused” responses could be used to assign partial credits, rather than entirely removing respondents from the analysis.

The study assessed freeridership through three steps:

1. It converted each participant survey response into a freeridership matrix terminology.

2. Each participant’s combination of responses received a score from the matrix.

3. All participants were aggregated into an average freeridership score for the entire program category.

Convert Responses to Matrix Terminology The study independently evaluated each survey question’s response, assessing participants’ freeridership levels per question, with each survey response option converted into the following values:

• “Yes” (100%freerider)

• “No” (0%freerider)

• “Partial” (50%freerider)

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Participant Freeridership Scoring Following conversion of survey responses into matrix terminology, a freeridership matrix could be created, allowing each participant’s combined responses to be assigned a freeridership score. In creating the matrix, the Evaluation Team considered all combinations of survey question responses, with each combination receiving a freeridership score of 0% to 100%. This technique allowed every participant’s combination of responses to be assigned a score of 0% to 100%.

Program Category Freeridership Scoring After assigning a freeridership score to every survey respondent, the Evaluation Team calculated a savings-weighted average freerider score for the program category. The following equation weighted respondents’ freerider scores by the estimated savings of equipment installed:

𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝑘𝑘𝐷𝐷𝐸𝐸𝑆𝑆ℎ𝑀𝑀𝐷𝐷𝑡𝑡 𝐹𝐹𝑃𝑃𝐷𝐷𝐷𝐷𝑃𝑃𝐸𝐸𝑡𝑡𝐷𝐷𝑃𝑃𝑃𝑃ℎ𝐸𝐸𝐷𝐷

=∑[𝑅𝑅𝐷𝐷𝑃𝑃𝐷𝐷𝑀𝑀𝑀𝑀𝑡𝑡𝐷𝐷𝑀𝑀𝑀𝑀 𝐹𝐹𝑅𝑅 𝑆𝑆𝑆𝑆𝑀𝑀𝑃𝑃𝐷𝐷] 𝑥𝑥 [𝑅𝑅𝐷𝐷𝑅𝑅𝑤𝑤𝑀𝑀𝐷𝐷𝑡𝑡 𝑀𝑀𝐷𝐷𝑤𝑤𝑃𝑃𝑀𝑀𝑃𝑃𝐷𝐷 𝑘𝑘𝑘𝑘ℎ 𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃]∑[𝑅𝑅𝐷𝐷𝑅𝑅𝑤𝑤𝑀𝑀𝐷𝐷𝑡𝑡 𝑀𝑀𝐷𝐷𝑤𝑤𝑃𝑃𝑀𝑀𝑃𝑃𝐷𝐷 𝑘𝑘𝑘𝑘ℎ 𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝑓𝑓𝑀𝑀𝑃𝑃 𝐴𝐴𝐷𝐷𝐷𝐷 𝑅𝑅𝐷𝐷𝑃𝑃𝐷𝐷𝑀𝑀𝑀𝑀𝑡𝑡𝐷𝐷𝑀𝑀𝑀𝑀𝑃𝑃]

The Freeridership Scoring Model The Evaluation Team developed an Excel-based model to assist with freeridership calculations and to improve the results’ consistency and quality. The model translated raw survey responses into matrix terminology, and then assigned each participant’s response pattern a score from the matrix. Program participants in the sample then could be aggregated to calculate average freerider scores.

The model incorporated the following inputs:

• Raw survey responses for each participant, along with their incented measures, and energy savings from those measures, if applicable;

• Figures converting raw survey responses into matrix terminologies for each program category; and

• Custom freeridership scoring matrices for each unique survey type.

The model used a simple interface, allowing users to quickly reproduce a scoring analysis for any program category. It displayed each participant’s combination of responses and corresponding freeridership scores, producing a summary table that provided the average score.

Spillover Methodology Spillover refers to additional savings generated by program participants through program participation, but not captured by program records. Spillover occurs when participants choose to purchase energy-efficient measures or adopt energy-efficient practices due a program’s influence, but do not receive any financial incentive for the additional measures. As these customers have not received a financial incentive, they typically do not appear in program records of savings generated by spillover impacts.

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Spillover examples include:

• Program participants adopting additional measures without an incentive.

• Consumers acting on program-induced changes in available energy-using equipment within the marketplace.

• Changes brought about by more efficient practices employed by architects and engineers, ultimately guiding consumer behaviors into desired patterns.

• Changes in nonparticipants’ behaviors resulting from direct marketing or changes in stocking practices.

The energy-efficiency program’s spillover effect serves as an additional impact, which can be added to the program’s direct results.

The Evaluation Team measured spillover by asking a sample of participants that purchased a particular measure and received an incentive whether, due to the program, they installed another efficient measure or undertook another energy-efficiency activity. Surveys asked respondents to rate the program’s (and the incentive’s) relative influence (e.g., highly, somewhat, or not at all influential) on their decisions to pursue additional savings.

Participant Spillover Analysis Spillover savings calculations used a top-down approach. Analysis began with a subset only containing survey respondents, indicating they installed additional energy-savings measures after participating in the program. Participants were removed from this subset if they indicated the program had little influence on their decisions to purchase additional measures; thus, the subset only retained participants rating the incentive as highly influential.

For remaining participants with spillover savings, the Evaluation Team estimated energy savings for additional measures installed. The team’s engineers calculated savings values based on average savings per measure installed in the program, then matching them to additional measures installed by survey participants. The Indiana Technical Resource Manual (TRM) was used as a reference when information from the evaluation could not be used.

The following equation produced the spillover percentage per program category by dividing the sum of additional spillover savings reported by respondents for a given program category by total incentivized gross savings achieved by all respondents in the program category:

𝑆𝑆𝐷𝐷𝐸𝐸𝐷𝐷𝐷𝐷𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 % =∑𝑆𝑆𝐷𝐷𝐸𝐸𝐷𝐷𝐷𝐷𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 𝑀𝑀𝐷𝐷𝑤𝑤𝑃𝑃𝑀𝑀𝑃𝑃𝐷𝐷 𝑘𝑘𝑘𝑘ℎ 𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝑓𝑓𝑀𝑀𝑃𝑃 𝐴𝐴𝐷𝐷𝐷𝐷 𝑆𝑆𝑀𝑀𝑃𝑃𝑤𝑤𝐷𝐷𝐷𝐷 𝑅𝑅𝐷𝐷𝑃𝑃𝐷𝐷𝑀𝑀𝑀𝑀𝑡𝑡𝐷𝐷𝑀𝑀𝑀𝑀𝑃𝑃∑𝑃𝑃𝑃𝑃𝑀𝑀𝑆𝑆𝑃𝑃𝑤𝑤𝐷𝐷 𝑀𝑀𝐷𝐷𝑤𝑤𝑃𝑃𝑀𝑀𝑃𝑃𝐷𝐷 𝑘𝑘𝑘𝑘ℎ 𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝑓𝑓𝑀𝑀𝑃𝑃 𝐴𝐴𝐷𝐷𝐷𝐷 𝑆𝑆𝑀𝑀𝑃𝑃𝑤𝑤𝐷𝐷𝐷𝐷 𝑅𝑅𝐷𝐷𝑃𝑃𝐷𝐷𝑀𝑀𝑀𝑀𝑡𝑡𝐷𝐷𝑀𝑀𝑀𝑀𝑃𝑃

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Appendix B: Peer Comparison Report—Modeled Estimates

The tables below show the Fixed Effects (FE) modeled estimates for the program by wave. The FE models use monthly kWh12 as the dependent variables and the pre-participation kWh was used as the denominator.

Table 100. Fixed Effects Model Estimate of Program Impacts for Wave 1: Dependent Variable is kWh/day (February2012 to January 2015)

Variable Coefficient (Negative Values =

Saving) Per Participant t-value

Equivalent to Percentage

Impact: Post*Part -31.69 -13.98 -1.58% Sample Size 1,865,293 obs (42,730 homes) R-Square 51.6%

Table 101. Fixed Effects Model Estimate of Overall Program Impacts for Wave 2: Dependent Variable is kWh/day (April 2013 to January 2015)




Impact: Post*Part -10.42 -8.05 -1.15% Sample Size 2,292,343 obs (81,299 homes) R-Square 63%

Table 102. Fixed Effects Model Estimate of Overall Program Impacts for Wave 3: Dependent Variable is kWh/day (January 2014 to January 2015)




Impact: Post*Part -9.23 -6.87 -0.93% Sample Size 4,784,586 obs (20,4449 homes) R-Square 64%

The tables below present the estimated FE regression models for the households that only received quarterly paper reports vs. households that received both quarterly paper and monthly e-mail reports, respectively.

12 The monthly kWh is the “calendarized” monthly kWh, therefore the varying billing cycle has been adjusted.

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Table 103. Model Estimate of Report Channel Impacts for Wave 1: Dependent Variable is kWh/day (January 2011 to March 2014)




Impact for Paper Reports Only: Post* Part*E-mail0

-34.65 -12.69 -1.73%

Impact for Paper/e-mail: Post* Part*E-mail1

-25.10 -6.18 -1.25%

Sample Size 1,865,293 obs (42,730 homes) R-Square 51.6%

Table 104. Model Estimate of Report Channel Impacts for Wave 2: Dependent Variable is kWh/day (March 2012 to March 2014)

Variable Coefficient (Negative Values = Saving) Per

Participant t-value



-8.76 -6.08 -0.97%

Impact for Paper/e-mail: Post* Part*E-mail1

-17.17 -5.84 -1.89%

Sample Size 2,292,343 obs (81,299 homes) R-Square 63%

Table 105. Model Estimate of Report Channel Impacts for Wave 3: Dependent Variable is kWh/day (March 2012 to March 2014)

Variable

Coefficient (Negative

Values = Saving) Per Participant

t-value Equivalent to Percentage


-9.09 -5.99 -0.92%

Impact for Paper/e-mail: Post* Part*E-mail1 -9.74 -2.89 -0.98% Sample Size 2,784,586 obs (20,4449 homes) R-Square 63.9%

108

Appendix C: Peer Comparison Report Fixed Effects Model Output for Wave 1

Below is a summary of the detailed model results. As described in the analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover for a comparison report program like this, a treatment – control group approach is commonly used. The model specification is:

Equation 10. Fixed Effect Model – Direct Overall Savings 𝑘𝑘𝑘𝑘ℎ𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛼𝛼1𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛼𝛼2𝐶𝐶𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐻𝐻𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽1𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖 + 𝛽𝛽2𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where:

• 𝛼𝛼𝑖𝑖: The OPower customer ID, where the fixed effect comes from (denoted by Opower_customer_ID in the model output below) It serves as a by-account intercept which captures the differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.)

• MonthIDt: A dummy variable which equals to 1 for the corresponding month and 0 otherwise. This variable is used to control for the macro economic conditions.

• CDDi: Cooling degree days which capture the weather effect (base 65º F)

• HDDi: Heating degree days which capture the weather effect (base 65º F)

• Postt: A dummy variable which equals to one if the month is in the post-program period; 0 otherwise.

• 𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖: An interaction of the variable ‘Post’ and the variable ‘Participation’. Participation is also a dummy variable which equals to one if a customer received reports, 0 otherwise.

109

Number of Observations Read 1865293 Number of Observations Used 1865293 Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 42781 898545882886 21003386.618 45.42 <.0001 Error 1.82E6 842791710233 462434.36129 Corrected Total 1.87E6 1.7413376E12 R-Square Coeff Var Root MSE kwh Mean 0.516009 33.97345 680.0253 2001.637 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 42729 368088624592 8614491.9046 18.63 <.0001 monthID 48 481061752831 10022119851 21672.5 <.0001 cdd 1 1941342401.9 1941342401.9 4198.09 <.0001 hdd 1 47361841167 47361841167 102419 <.0001 post 1 1880493.0195 1880493.0195 4.07 0.0437 Post_part 1 90441399.378 90441399.378 195.58 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 48 29287149527 610148948 1319.43 <.0001 cdd 1 2250264443 2250264443 4866.13 <.0001 hdd 1 47329631572 47329631572 102349 <.0001 post 1 9003909 9003909 19.47 <.0001 Post_part 1 90441399 90441399 195.58 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits monthID 20110101 -114.498055 B 124.0825945 -0.92 0.3561 -357.695633 128.699523 monthID 20110201 195.111812 B 9.7114709 20.09 <.0001 176.077666 214.145957 monthID 20110301 586.966435 B 9.4779313 61.93 <.0001 568.390019 605.542852 monthID 20110401 700.116100 B 9.7520915 71.79 <.0001 681.002339 719.229860 monthID 20110501 982.313369 B 10.7956254 90.99 <.0001 961.154318 1003.472420 monthID 20110601 1242.024455 B 12.9601213 95.83 <.0001 1216.623067 1267.425843 monthID 20110701 1402.127344 B 16.3168928 85.93 <.0001 1370.146800 1434.107887 monthID 20110801 1308.957882 B 14.7032130 89.03 <.0001 1280.140095 1337.775669 monthID 20110901 997.332263 B 10.9586650 91.01 <.0001 975.853660 1018.810867 monthID 20111001 702.856136 B 9.8264111 71.53 <.0001 683.596711 722.115560

110

Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits monthID 20111101 492.160740 B 9.4962069 51.83 <.0001 473.548504 510.772976 monthID 20111201 451.829831 B 9.5752283 47.19 <.0001 433.062716 470.596946 monthID 20120101 444.731810 B 9.7285399 45.71 <.0001 425.664209 463.799410 monthID 20120201 433.164523 B 9.5171186 45.51 <.0001 414.511301 451.817745 monthID 20120301 692.548664 B 8.3508459 82.93 <.0001 676.181295 708.916032 monthID 20120401 627.479714 B 5.9684520 105.13 <.0001 615.781755 639.177673 monthID 20120501 1065.342031 B 7.4356146 143.28 <.0001 1050.768484 1079.915577 monthID 20120601 1190.617645 B 11.5198928 103.35 <.0001 1168.039055 1213.196235 monthID 20120701 1257.802944 B 15.8934854 79.14 <.0001 1226.652264 1288.953624 monthID 20120801 1220.363228 B 11.1566135 109.38 <.0001 1198.496653 1242.229803 monthID 20120901 885.673021 B 7.1258392 124.29 <.0001 871.706623 899.639418 monthID 20121001 475.590802 B 5.4370275 87.47 <.0001 464.934417 486.247187 monthID 20121101 281.369213 B 5.0632840 55.57 <.0001 271.445352 291.293074 monthID 20121201 304.031599 B 5.2966528 57.40 <.0001 293.650343 314.412854 monthID 20130101 227.296274 B 5.9803757 38.01 <.0001 215.574945 239.017603 monthID 20130201 216.455456 B 5.5525630 38.98 <.0001 205.572625 227.338287 monthID 20130301 247.248577 B 5.2950253 46.69 <.0001 236.870511 257.626643 monthID 20130401 499.991742 B 5.4559606 91.64 <.0001 489.298249 510.685235 monthID 20130501 870.857014 B 7.0199116 124.06 <.0001 857.098231 884.615797 monthID 20130601 1096.574416 B 9.3236777 117.61 <.0001 1078.300331 1114.848500 monthID 20130701 1198.763988 B 10.5540375 113.58 <.0001 1178.078441 1219.449535 monthID 20130801 1161.158585 B 10.3587448 112.09 <.0001 1140.855805 1181.461365 monthID 20130901 981.789155 B 8.5770009 114.47 <.0001 964.978531 998.599779 monthID 20131001 516.756878 B 5.7405709 90.02 <.0001 505.505558 528.008198 monthID 20131101 133.771577 B 5.2134601 25.66 <.0001 123.553376 143.989778 monthID 20131201 139.167879 B 6.2320034 22.33 <.0001 126.953369 151.382389 monthID 20140101 164.740999 B 7.1905891 22.91 <.0001 150.647694 178.834304 monthID 20140201 62.531042 B 6.4481742 9.70 <.0001 49.892845 75.169240 monthID 20140301 167.891409 B 5.4345870 30.89 <.0001 157.239807 178.543010 monthID 20140401 411.336335 B 5.5360046 74.30 <.0001 400.485958 422.186712 monthID 20140501 831.510224 B 6.9448656 119.73 <.0001 817.898528 845.121919 monthID 20140601 1126.070535 B 9.1125347 123.57 <.0001 1108.210283 1143.930787 monthID 20140701 1179.323787 B 9.1211117 129.30 <.0001 1161.446725 1197.200849 monthID 20140801 1118.521609 B 9.4183607 118.76 <.0001 1100.061949 1136.981269 monthID 20140901 834.289913 B 7.4591609 111.85 <.0001 819.670216 848.909609 monthID 20141001 442.954848 B 5.7230853 77.40 <.0001 431.737800 454.171897 monthID 20141101 84.097772 B 5.3597013 15.69 <.0001 73.592943 94.602600 monthID 20141201 105.759038 B 5.9589322 17.75 <.0001 94.079738 117.438338 monthID 20150101 0.000000 B . . . . . cdd 1.897579 0.0272025 69.76 <.0001 1.844263 1.950895 hdd 2.429184 0.0075931 319.92 <.0001 2.414302 2.444066 post 36.075534 8.1756496 4.41 <.0001 20.051545 52.099524 Post_part -31.689535 2.2659863 -13.98 <.0001 -36.130790 -27.248281

111

Appendix D: Peer Comparison Report Fixed Effects Model Output for Wave 1 (Paper vs. Paper/E-mail)

Below is a summary of the detailed model results. As described in the Analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover, for a comparison report program like this, a treatment vs. control group approach is commonly used. A proc glm procedure in SAS was utilized to construct the FE model.

This model can be expressed as:

Equation 11. Fixed Effect Model – E-mail vs. Paper Savings 𝑘𝑘𝑘𝑘ℎ𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛼𝛼1𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀ℎ𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛼𝛼2𝐶𝐶𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐻𝐻𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽1𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷𝑖𝑖 + 𝛽𝛽2𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝐼𝐼𝑀𝑀𝑖𝑖

+ 𝛽𝛽3𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷𝑖𝑖 + 𝛽𝛽4𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑤𝑤𝑃𝑃𝑀𝑀𝑖𝑖𝐸𝐸𝐷𝐷𝑤𝑤𝐸𝐸𝐷𝐷𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

Where:

• 𝛼𝛼𝑖𝑖 − Opower_customer_ID: this is where the fixed effect comes. It serves as a by-account intercept which captures the differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.)

• MonthID: A dummy variable which equals to 1 for the corresponding month and 0 otherwise. This variable is used to control for the macroeconomic conditions.

• CDD: Cooling degree days which capture the weather effect (base 65º F)

• HDD: Heating degree days which capture the weather effect (base 65º F)

• Post*E-mail0: A multiplication of the variable ‘Post’ and the variable ‘E-mail0’. Post is a dummy variable which equals to one if the month is in the post-program period; 0 otherwise. DM is a dummy variable which equals to one where paper reports (DM) is the only treatment approach available for both treatment and control group members, 0 otherwise. ‘Post*E-mail0’ measures during the post-program period how customers eligible for paper reports only changed their consumption (given e-mail = 0);

• Post*E-mail1: A multiplication of the variable ‘Post’ and the variable ‘E-mail’. E-mail is a dummy variable which equals to one where e-mail is available for both treatment and control group members, 0 otherwise. Note if e-mail =1 it essentially means this customer received both paper and e-mail reports. ‘Post*E-mail1’ measures during the post-program period how customers eligible for e-mail (in addition to paper reports) changed their consumption (given E-mail = 1).

112

Note these consumption changes are NOT caused by the program because it is estimated based on treatment and control group members;

• Post*Part*E-mail0: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘E-mail0’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. ‘Post*Part*E-mail0’ measures during the post-program period how the report had changed the consumption of recipients who were eligible for paper reports only. It is mutually exclusive to the variable “Post*Part*E-mail”, i.e. if Post*Part*E-mail0 = 1 then Post*Part*E-mail1 = 0. This way the coefficient 𝛽𝛽3 measures total impact of paper reports;

• Post*Part*E-mail1: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘E-mail1’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. Post*Part tracks the treatment group membership during the post-program period; ‘Post*Part*E-mail’ measures during the post-program period how program treatment (paper report and e-mail) changed the consumption of recipients who were eligible for e-mail and paper reports. It is mutually exclusive to the variable “Post*Part*E-mail0”, i.e. if Post*Part*E-mail1 = 1 then Post*Part*E-mail0 = 0. This way the coefficient 𝛽𝛽4 measures total impact of paper + e-mail reports.

113

Number of Observations Read 1865293 Number of Observations Used 1865293 Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 42783 898548780158 21002472.481 45.42 <.0001 Error 1.82E6 842788812961 462433.27905 Corrected Total 1.87E6 1.7413376E12 R-Square Coeff Var Root MSE kwh Mean 0.516011 33.97341 680.0245 2001.637 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 42729 368088624592 8614491.9046 18.63 <.0001 monthID 48 481061752831 10022119851 21672.6 <.0001 cdd 1 1941342401.9 1941342401.9 4198.10 <.0001 hdd 1 47361841167 47361841167 102419 <.0001 post*e-mail 2 3112337.3134 1556168.6567 3.37 0.0346 Post_part*e-mail 2 92106827.325 46053413.662 99.59 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 48 29285309372 610110612 1319.35 <.0001 cdd 1 2250747664 2250747664 4867.18 <.0001 hdd 1 47328707032 47328707032 102347 <.0001 post*e-mail 2 9152923 4576461 9.90 <.0001 Post_part*e-mail 2 92106827 46053414 99.59 <.0001 Standard Parameter Estimate Error t Value Pr > |t| monthID 20110101 -114.845422 B 124.0826423 -0.93 0.3547 monthID 20110201 195.100396 B 9.7114639 20.09 <.0001 monthID 20110301 586.947670 B 9.4779298 61.93 <.0001 monthID 20110401 700.088880 B 9.7521002 71.79 <.0001 monthID 20110501 982.254838 B 10.7956847 90.99 <.0001 monthID 20110601 1241.931603 B 12.9602521 95.83 <.0001 monthID 20110701 1401.993059 B 16.3171051 85.92 <.0001 monthID 20110801 1308.841936 B 14.7033890 89.02 <.0001 monthID 20110901 997.273244 B 10.9587257 91.00 <.0001 monthID 20111001 702.828532 B 9.8264198 71.52 <.0001

114

Standard Parameter Estimate Error t Value Pr > |t| monthID 20111101 492.141073 B 9.4962059 51.83 <.0001 monthID 20111201 451.815447 B 9.5752215 47.19 <.0001 monthID 20120101 444.719350 B 9.7285314 45.71 <.0001 monthID 20120201 433.148304 B 9.5171134 45.51 <.0001 monthID 20120301 692.497490 B 8.3508942 82.92 <.0001 monthID 20120401 627.446931 B 5.9684931 105.13 <.0001 monthID 20120501 1065.287383 B 7.4357065 143.27 <.0001 monthID 20120601 1190.512035 B 11.5200872 103.34 <.0001 monthID 20120701 1257.656330 B 15.8937458 79.13 <.0001 monthID 20120801 1220.264488 B 11.1567863 109.37 <.0001 monthID 20120901 885.624276 B 7.1259087 124.28 <.0001 monthID 20121001 475.572725 B 5.4370372 87.47 <.0001 monthID 20121101 281.356702 B 5.0632849 55.57 <.0001 monthID 20121201 304.023111 B 5.2966487 57.40 <.0001 monthID 20130101 227.291703 B 5.9803690 38.01 <.0001 monthID 20130201 216.448544 B 5.5525573 38.98 <.0001 monthID 20130301 247.239356 B 5.2950211 46.69 <.0001 monthID 20130401 499.971951 B 5.4559703 91.64 <.0001 monthID 20130501 870.814078 B 7.0199627 124.05 <.0001 monthID 20130601 1096.501610 B 9.3237887 117.60 <.0001 monthID 20130701 1198.677632 B 10.5541747 113.57 <.0001 monthID 20130801 1161.074445 B 10.3588777 112.08 <.0001 monthID 20130901 981.727624 B 8.5770884 114.46 <.0001 monthID 20131001 516.737875 B 5.7405803 90.01 <.0001 monthID 20131101 133.769108 B 5.2134544 25.66 <.0001 monthID 20131201 139.171960 B 6.2319974 22.33 <.0001 monthID 20140101 164.748219 B 7.1905846 22.91 <.0001 monthID 20140201 62.536505 B 6.4481688 9.70 <.0001 monthID 20140301 167.891370 B 5.4345806 30.89 <.0001 monthID 20140401 411.325916 B 5.5360042 74.30 <.0001 monthID 20140501 831.476692 B 6.9448972 119.72 <.0001 monthID 20140601 1126.007373 B 9.1126204 123.57 <.0001 monthID 20140701 1179.261498 B 9.1211946 129.29 <.0001 monthID 20140801 1118.455825 B 9.4184503 118.75 <.0001 monthID 20140901 834.250797 B 7.4592005 111.84 <.0001 monthID 20141001 442.944331 B 5.7230837 77.40 <.0001 monthID 20141101 84.098712 B 5.3596953 15.69 <.0001 monthID 20141201 105.765694 B 5.9589282 17.75 <.0001 monthID 20150101 0.000000 B . . . cdd 1.897808 0.0272028 69.77 <.0001 hdd 2.429166 0.0075931 319.92 <.0001 post*e-mail 0 36.766603 8.2653452 4.45 <.0001 post*e-mail 1 34.529583 8.5947143 4.02 <.0001 Post_part*e-mail 0 -34.646019 2.7308727 -12.69 <.0001 Post_part*e-mail 1 -25.102509 4.0602126 -6.18 <.0001

115

Appendix E: Peer Comparison Report Fixed Effects Model Output for Wave 2

Below is a summary of the detailed model results. As described in the Analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover for a comparison report program like this, a treatment – control group approach is commonly used. The model specification is:


Where:







116

Number of Observations Read 2292343 Number of Observations Used 2292343 Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 81160 594562965483 7325812.7832 46.50 <.0001 Error 2.21E6 348387733334 157557.24012 Corrected Total 2.29E6 942950698817 R-Square Coeff Var Root MSE kwh Mean 0.630535 39.82215 396.9348 996.7690 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 81122 498586315767 6146129.4811 39.01 <.0001 monthID 34 89250764471 2625022484.4 16660.8 <.0001 cdd 1 1588031959.4 1588031959.4 10079.1 <.0001 hdd 1 5118036354.4 5118036354.4 32483.7 <.0001 post 1 9617471.7405 9617471.7405 61.04 <.0001 part2 1 10199464.639 10199464.639 64.73 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 34 7317112861 215209202 1365.91 <.0001 cdd 1 1761010349 1761010349 11177.0 <.0001 hdd 1 5121341485 5121341485 32504.6 <.0001 post 1 3729335 3729335 23.67 <.0001 part2 1 10199465 10199465 64.73 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits monthID 20120301 185.0938878 B 5.67169131 32.63 <.0001 173.9775710 196.2102046 monthID 20120401 384.0383163 B 5.03417519 76.29 <.0001 374.1715088 393.9051238 monthID 20120501 577.0372414 B 5.95012019 96.98 <.0001 565.3752137 588.6992690 monthID 20120601 575.4002168 B 8.04913170 71.49 <.0001 559.6242000 591.1762337 monthID 20120701 550.2596759 B 10.45163888 52.65 <.0001 529.7748289 570.7445229 monthID 20120801 595.2716029 B 7.85837472 75.75 <.0001 579.8694630 610.6737427 monthID 20120901 514.1512426 B 5.71251122 90.00 <.0001 502.9549202 525.3475650 monthID 20121001 341.9842568 B 4.51597212 75.73 <.0001 333.1331093 350.8354044 monthID 20121101 223.8471070 B 4.00328070 55.92 <.0001 216.0008167 231.6933972 monthID 20121201 180.6191108 B 3.92962031 45.96 <.0001 172.9171923 188.3210293

117

Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits monthID 20130101 89.1367343 B 4.25603664 20.94 <.0001 80.7950512 97.4784174 monthID 20130201 98.3189439 B 4.02986148 24.40 <.0001 90.4205562 106.2173316 monthID 20130301 147.1049713 B 3.92807341 37.45 <.0001 139.4060846 154.8038579 monthID 20130401 319.8098776 B 4.47633324 71.44 <.0001 311.0364209 328.5833344 monthID 20130501 493.0168342 B 5.07936815 97.06 <.0001 483.0614502 502.9722183 monthID 20130601 580.6580922 B 6.23981268 93.06 <.0001 568.4282774 592.8879071 monthID 20130701 619.3590245 B 6.95458690 89.06 <.0001 605.7282772 632.9897718 monthID 20130801 603.1521000 B 6.87765170 87.70 <.0001 589.6721430 616.6320570 monthID 20130901 558.2946195 B 5.80613486 96.16 <.0001 546.9147981 569.6744410 monthID 20131001 378.9528981 B 3.65070765 103.80 <.0001 371.7976387 386.1081576 monthID 20131101 158.8107031 B 2.58158417 61.52 <.0001 153.7508883 163.8705178 monthID 20131201 64.4235227 B 3.21172994 20.06 <.0001 58.1286442 70.7184011 monthID 20140101 -3.2092641 B 4.03644879 -0.80 0.4266 -11.1205627 4.7020345 monthID 20140201 -18.2558095 B 3.39387075 -5.38 <.0001 -24.9076776 -11.6039414 monthID 20140301 124.1470078 B 2.64659092 46.91 <.0001 118.9597820 129.3342335 monthID 20140401 309.7141194 B 3.42143839 90.52 <.0001 303.0082197 316.4200191 monthID 20140501 509.4162404 B 4.73008757 107.70 <.0001 500.1454340 518.6870468 monthID 20140601 630.4376017 B 6.14303327 102.63 <.0001 618.3974712 642.4777323 monthID 20140701 663.6561858 B 6.15756816 107.78 <.0001 651.5875673 675.7248042 monthID 20140801 620.7401428 B 6.35153297 97.73 <.0001 608.2913601 633.1889255 monthID 20140901 525.3049345 B 5.10403198 102.92 <.0001 515.3012102 535.3086588 monthID 20141001 356.2807653 B 3.62153192 98.38 <.0001 349.1826893 363.3788414 monthID 20141101 128.2794737 B 2.64956034 48.42 <.0001 123.0864280 133.4725194 monthID 20141201 80.9897938 B 2.99442772 27.05 <.0001 75.1208201 86.8587675 monthID 20150101 0.0000000 B . . . . . cdd 1.7397573 0.01645610 105.72 <.0001 1.7075039 1.7720107 hdd 1.0431995 0.00578622 180.29 <.0001 1.0318588 1.0545403 post -15.3459140 3.15425112 -4.87 <.0001 -21.5281359 -9.1636920 post_part -10.4158464 1.29456858 -8.05 <.0001 -12.9531556 -7.8785372

118

Appendix F: Peer Comparison Report Fixed Effects Model Output for Wave 2 (Paper vs. Paper/E-mail)

Below is a summary of the detailed model results. As described in the analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover, for a comparison report program like this, a treatment vs. control group approach is commonly used. A proc glm procedure in SAS was utilized to construct the FE model.




Where:





• Post*E-mail0: A multiplication of the variable ‘Post’ and the variable ‘E-mail0’. Post13 is a dummy variable which equals to one if the month is in the post-program period; 0 otherwise. DM is a dummy variable which equals to one where paper reports is the only treatment approach available for both treatment and control group members, 0 otherwise. ‘Post*E-mail0’ measures during the post-program period how customers eligible for paper reports only changed their consumption (given e-mail = 0);

• Post*E-mail1: A multiplication of the variable ‘Post’ and the variable ‘E-mail1’. E-mail1 is a dummy variable which equals to one where e-mail is available for both treatment and control

13 The very first month during which the first report was sent was deleted from the analysis. For example if the

first report went out on March 9th within a billing period of March 1st – March 31st, March is considered “dead-band” and is excluded from the analysis. This is a consistent approach across the other Indiana utilities.

119

group members, 0 otherwise. Note if e-mail1 =1 it essentially means this customer received both paper and e-mail reports. ‘Post*E-mail1’ measures during the post-program period how customers eligible for e-mail (in addition to paper reports) changed their consumption (given E-mail = 1). Note these consumption changes are NOT caused by the program because it is estimated based on treatment and control group members;

• Post*Part*E-mail0: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘E-mail0’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. ‘Post*Part*DM’ measures during the post-program period how the report had changed the consumption of recipients who were eligible for paper reports only. It is mutually exclusive to the variable “Post*Part*E-mail”, i.e. if Post*Part*DM = 1 then Post*Part*E-mail = 0. This way the coefficient 𝛽𝛽3 measures total impact of paper reports;

• Post*Part*E-mail1: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘E-mail1’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. Post*Part tracks the treatment group membership during the post-program period; ‘Post*Part*E-mail1’ measures during the post-program period how program treatment (paper report and e-mail) changed the consumption of recipients who were eligible for e-mail and paper reports. It is mutually exclusive to the variable “Post*Part*DM”, i.e. if Post*Part*E-mail1 = 1 then Post*Part*E-mail0 = 0. This way the coefficient 𝛽𝛽4 measures total impact of paper + e-mail reports;

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Number of Observations Read 2292343 Number of Observations Used 2292343 Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 81162 594565834585 7325667.6103 46.50 <.0001 Error 2.21E6 348384864232 157556.08509 Corrected Total 2.29E6 942950698817 R-Square Coeff Var Root MSE kwh Mean 0.630538 39.82200 396.9334 996.7690 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 81122 498586315767 6146129.4811 39.01 <.0001 monthID 34 89250764471 2625022484.4 16660.9 <.0001 cdd 1 1588031959.4 1588031959.4 10079.2 <.0001 hdd 1 5118036354.4 5118036354.4 32483.9 <.0001 post*e-mail 2 11493647.465 5746823.7324 36.47 <.0001 part2*e-mail 2 11192390.9 5596195.4502 35.52 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 34 7317573049 215222737 1366.01 <.0001 cdd 1 1761102184 1761102184 11177.6 <.0001 hdd 1 5121631943 5121631943 32506.7 <.0001 post*e-mail 2 6126529 3063265 19.44 <.0001 part2*e-mail 2 11192391 5596195 35.52 <.0001

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Standard Parameter Estimate Error t Value Pr > |t| monthID 20120301 185.0541198 B 5.67168797 32.63 <.0001 monthID 20120401 383.9905354 B 5.03418054 76.28 <.0001 monthID 20120501 576.9907608 B 5.95011622 96.97 <.0001 monthID 20120601 575.3471419 B 8.04911582 71.48 <.0001 monthID 20120701 550.2005822 B 10.45161154 52.64 <.0001 monthID 20120801 595.2277275 B 7.85835522 75.74 <.0001 monthID 20120901 514.1205963 B 5.71249868 90.00 <.0001 monthID 20121001 341.9551026 B 4.51596770 75.72 <.0001 monthID 20121101 223.8136863 B 4.00328285 55.91 <.0001 monthID 20121201 180.5767269 B 3.92963062 45.95 <.0001 monthID 20130101 89.0880744 B 4.25604969 20.93 <.0001 monthID 20130201 98.2755407 B 4.02987206 24.39 <.0001 monthID 20130301 147.0645902 B 3.92808189 37.44 <.0001 monthID 20130401 319.7864525 B 4.47632570 71.44 <.0001 monthID 20130501 492.9857408 B 5.07935966 97.06 <.0001 monthID 20130601 580.6440300 B 6.23979127 93.06 <.0001 monthID 20130701 619.3490739 B 6.95456184 89.06 <.0001 monthID 20130801 603.1470258 B 6.87762660 87.70 <.0001 monthID 20130901 558.2958303 B 5.80611361 96.16 <.0001 monthID 20131001 378.9525661 B 3.65069462 103.80 <.0001 monthID 20131101 158.8043361 B 2.58157587 61.51 <.0001 monthID 20131201 64.4093541 B 3.21172103 20.05 <.0001 monthID 20140101 -3.2272412 B 4.03643726 -0.80 0.4240 monthID 20140201 -18.2692780 B 3.39386087 -5.38 <.0001 monthID 20140301 124.1423456 B 2.64658186 46.91 <.0001 monthID 20140401 309.7235007 B 3.42142657 90.52 <.0001 monthID 20140501 509.4310560 B 4.73007171 107.70 <.0001 monthID 20140601 630.4562568 B 6.14301318 102.63 <.0001 monthID 20140701 663.6782030 B 6.15754881 107.78 <.0001 monthID 20140801 620.7628881 B 6.35151347 97.73 <.0001 monthID 20140901 525.3299659 B 5.10401780 102.92 <.0001 monthID 20141001 356.3041462 B 3.62152341 98.39 <.0001 monthID 20141101 128.2940952 B 2.64955315 48.42 <.0001 monthID 20141201 80.9986394 B 2.99441768 27.05 <.0001 monthID 20150101 0.0000000 B . . . cdd 1.7398032 0.01645604 105.72 <.0001 hdd 1.0432301 0.00578620 180.30 <.0001 post*e-mail 0 -17.5688133 3.20459723 -5.48 <.0001 post*e-mail 1 -6.4021127 3.90556414 -1.64 0.1012 part2*e-mail 0 -8.7606338 1.44203239 -6.08 <.0001 part2*e-mail 1 -17.1688194 2.93884010 -5.84 <.0001

122

Appendix G: Peer Comparison Report Fixed Effects Model Output for Wave 3

Below is a summary of the detailed model results. As described in the Analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover for a comparison report program like this, a treatment – control group approach is commonly used. The model specification is:


Where:







123

Number of Observations Read 4784586 Number of Observations Used 4784586

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 204478 1.5123204E12 7396005.3608 39.66 <.0001 Error 4.58E6 854166303154 186494.83585 Corrected Total 4.78E6 2.3664867E12 R-Square Coeff Var Root MSE kwh Mean 0.639057 44.97503 431.8505 960.2005 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 204448 1.2717186E12 6220254.4554 33.35 <.0001 monthID 26 183274375436 7049014439.9 37797.4 <.0001 cdd 1 13599964389 13599964389 72924.1 <.0001 hdd 1 43443329042 43443329042 232947 <.0001 post 1 275328268.71 275328268.71 1476.33 <.0001 part2 1 8804132.5811 8804132.5811 47.21 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 26 19182960212 737806162 3956.17 <.0001 cdd 1 13080192872 13080192872 70137.0 <.0001 hdd 1 43448210378 43448210378 232973 <.0001 post 1 204088402 204088402 1094.34 <.0001 part2 1 8804133 8804133 47.21 <.0001

124

Standard Parameter Estimate Error t Value Pr > |t| monthID 20121101 -60.5205218 B 54.32463149 -1.11 0.2653 monthID 20121201 -81.2700530 B 3.37896211 -24.05 <.0001 monthID 20130101 -68.9415948 B 3.50679951 -19.66 <.0001 monthID 20130201 -59.1826840 B 3.43766763 -17.22 <.0001 monthID 20130301 -6.8358385 B 3.39264736 -2.01 0.0439 monthID 20130401 164.5789568 B 3.39022758 48.55 <.0001 monthID 20130501 219.7901786 B 3.69905636 59.42 <.0001 monthID 20130601 135.5406790 B 4.38943194 30.88 <.0001 monthID 20130701 94.9721726 B 4.82425320 19.69 <.0001 monthID 20130801 81.4358044 B 4.78002546 17.04 <.0001 monthID 20130901 161.4338938 B 4.14872088 38.91 <.0001 monthID 20131001 194.7699504 B 3.39740455 57.33 <.0001 monthID 20131101 23.2764003 B 3.33788287 6.97 <.0001 monthID 20131201 -59.4388376 B 3.50649442 -16.95 <.0001 monthID 20140101 -117.4403218 B 3.67855841 -31.93 <.0001 monthID 20140201 -51.1850091 B 1.91519102 -26.73 <.0001 monthID 20140301 85.3677800 B 1.55405204 54.93 <.0001 monthID 20140401 258.0858028 B 1.54153286 167.42 <.0001 monthID 20140501 340.4764335 B 2.10100454 162.05 <.0001 monthID 20140601 304.0287410 B 3.10769334 97.83 <.0001 monthID 20140701 343.6852578 B 3.08332756 111.47 <.0001 monthID 20140801 268.1315534 B 3.30006648 81.25 <.0001 monthID 20140901 322.9446193 B 2.33369935 138.38 <.0001 monthID 20141001 295.3277370 B 1.62464841 181.78 <.0001 monthID 20141101 85.8033905 B 1.56128429 54.96 <.0001 monthID 20141201 42.1133020 B 1.75898242 23.94 <.0001 monthID 20150101 0.0000000 B . . . cdd 2.9741508 0.01123025 264.83 <.0001 hdd 1.0793843 0.00223627 482.67 <.0001 post -106.8164269 3.22895721 -33.08 <.0001 post_part -9.2345749 1.34402400 -6.87 <.0001

125

Appendix H: Peer Comparison Report Fixed Effects Model Output for Wave 3 (Paper vs. Paper/E-mail)

Below is a summary of the detailed model results. As described in the analysis section, data are available both across households (i.e., cross-sectional) and over time (i.e., time-series). With this type of data, known as ‘panel’ data, it becomes possible to control, simultaneously, for differences across households as well as differences across periods in time through the use of a ‘fixed-effects’ panel model specification. The fixed-effect refers to the model specification aspect that differences across homes that do not vary over the estimation period (such as square footage, heating system, etc.) can be explained, in large part, by customer-specific intercept terms that capture the net change in consumption due to the program, controlling for other factors that do change with time (e.g., the weather). Moreover, for a comparison report program like this, a treatment vs. control group approach is commonly used. A proc glm procedure in SAS was utilized to construct the FE model.




Where:





• Post*E-mail0: A multiplication of the variable ‘Post’ and the variable ‘E-mail0’. Post14 is a dummy variable which equals to one if the month is in the post-program period; 0 otherwise. E-mail0 is a dummy variable which equals to one where paper reports is the only treatment approach available for both treatment and control group members, 0 otherwise. ‘Post*E-mail0 measures during the post-program period how customers eligible for paper reports only changed their consumption (given e-mail = 0);

• Post*E-mail1: A multiplication of the variable ‘Post’ and the variable ‘E-mail1’. E-mail1 is a dummy variable which equals to one where e-mail is available for both treatment and control

14 The very first month during which the first report was sent was deleted from the analysis. For example if the

first report went out on March 9th within a billing period of March 1st – March 31st, March is considered “dead-band” and is excluded from the analysis. This is a consistent approach across the other Indiana utilities.

126

group members, 0 otherwise. Note if e-mail =1 it essentially means this customer received both paper and e-mail reports. ‘Post*E-mail’ measures during the post-program period how customers eligible for e-mail (in addition to paper reports) changed their consumption (given E-mail = 1). Note these consumption changes are NOT caused by the program because it is estimated based on treatment and control group members;

• Post*Part*E-mail0: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘DM’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. ‘Post*Part*DM’ measures during the post-program period how the report had changed the consumption of recipients who were eligible for paper reports only. It is mutually exclusive to the variable “Post*Part*E-mail”, i.e. if Post*Part*DM = 1 then Post*Part*E-mail = 0. This way the coefficient 𝛽𝛽3 measures total impact of paper reports;

• Post*Part*E-mail1: A multiplication of the variable ‘Post’, ‘Part’ and the variable ‘E-mail’. Post as previously discussed tracks the post-program period. ‘Part’ is a dummy that tracks treatment group membership: i.e. Part equals to one if a customer is included in the treatment group, 0 otherwise. Post*Part tracks the treatment group membership during the post-program period; ‘Post*Part*E-mail’ measures during the post-program period how program treatment (paper report and e-mail) changed the consumption of recipients who were eligible for e-mail and paper reports. It is mutually exclusive to the variable “Post*Part*E-mail0”, i.e. if Post*Part*E-mail1 = 1 then Post*Part*E-mail0 = 0. This way the coefficient 𝛽𝛽4 measures total impact of paper + e-mail reports.

127

Number of Observations Read 4784586 Number of Observations Used 4784586 Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 204480 1.5123307E12 7395983.5746 39.66 <.0001 Error 4.58E6 854155965991 186492.66032 Corrected Total 4.78E6 2.3664867E12 R-Square Coeff Var Root MSE kwh Mean 0.639062 44.97477 431.8480 960.2005 Source DF Type I SS Mean Square F Value Pr > F opower_customer_id 204448 1.2717186E12 6220254.4554 33.35 <.0001 monthID 26 183274375436 7049014439.9 37797.8 <.0001 cdd 1 13599964389 13599964389 72924.9 <.0001 hdd 1 43443329042 43443329042 232949 <.0001 post*e-mail 2 285673510.33 142836755.16 765.91 <.0001 part2*e-mail 2 8796054.264 4398027.132 23.58 <.0001 Source DF Type III SS Mean Square F Value Pr > F monthID 26 19182569466 737791133 3956.14 <.0001 cdd 1 13082327956 13082327956 70149.3 <.0001 hdd 1 43451258578 43451258578 232992 <.0001 post*e-mail 2 205266049 102633025 550.33 <.0001 part2*e-mail 2 8796054 4398027 23.58 <.0001

128

Standard Parameter Estimate Error t Value Pr > |t| monthID 20121101 -60.7948654 B 54.32432728 -1.12 0.2631 monthID 20121201 -81.2332404 B 3.37896575 -24.04 <.0001 monthID 20130101 -68.9286825 B 3.50679853 -19.66 <.0001 monthID 20130201 -59.1705155 B 3.43766740 -17.21 <.0001 monthID 20130301 -6.8269816 B 3.39264749 -2.01 0.0442 monthID 20130401 164.5924282 B 3.39022793 48.55 <.0001 monthID 20130501 219.7822476 B 3.69905280 59.42 <.0001 monthID 20130601 135.4955985 B 4.38942568 30.87 <.0001 monthID 20130701 94.8977739 B 4.82424903 19.67 <.0001 monthID 20130801 81.3438077 B 4.78002704 17.02 <.0001 Standard Parameter Estimate Error t Value Pr > |t| monthID 20130901 161.3569661 B 4.14872529 38.89 <.0001 monthID 20131001 194.7205839 B 3.39741051 57.31 <.0001 monthID 20131101 23.2182936 B 3.33789217 6.96 <.0001 monthID 20131201 -59.5116843 B 3.50650630 -16.97 <.0001 monthID 20140101 -117.5215608 B 3.67857094 -31.95 <.0001 monthID 20140201 -51.0898692 B 1.91522325 -26.68 <.0001 monthID 20140301 85.4327194 B 1.55406765 54.97 <.0001 monthID 20140401 258.1617098 B 1.54155764 167.47 <.0001 monthID 20140501 340.5338968 B 2.10100648 162.08 <.0001 monthID 20140601 304.0527267 B 3.10767689 97.84 <.0001 monthID 20140701 343.7003099 B 3.08331024 111.47 <.0001 monthID 20140801 268.1280375 B 3.30004726 81.25 <.0001 monthID 20140901 322.9556747 B 2.33368621 138.39 <.0001 monthID 20141001 295.3471755 B 1.62464104 181.79 <.0001 monthID 20141101 85.8028847 B 1.56127518 54.96 <.0001 monthID 20141201 42.1000277 B 1.75897306 23.93 <.0001 monthID 20150101 0.0000000 B . . . cdd 2.9744076 0.01123023 264.86 <.0001 hdd 1.0794255 0.00223626 482.69 <.0001 post*e-mail 0 -105.4328665 3.29907513 -31.96 <.0001 post*e-mail 1 -112.1432217 4.03801273 -27.77 <.0001 part2*e-mail 0 -9.0870710 1.51745854 -5.99 <.0001 part2*e-mail 1 -9.7340118 2.89502166 -3.36 0.0008

129

Appendix I: Multifamily Direct Install Program Assumptions and Algorithms

Below is a discussion of the analysis and the assumptions behind the following Multifamily Direct Install measures:

• Compact Fluorescent Lamps - Standard

• Compact Fluorescent Lamps – Specialty Globe

• Compact Fluorescent Lamps – Specialty Candelabra

• LED Night Light

• Kitchen Faucet Aerators

• Bathroom Faucet Aerators

• Low-flow Showerheads

• Programmable Thermostats

Standard and Specialty Compact Fluorescent Lamps (CFLs) The program provides incentives for the following CFLs:

1. 13 Watt and 19 Watt standard CFLs installed in apartment units

2. 13 Watt and 19 Watt standard CFLs installed in common areas

3. 9 Watt to 14 Watt specialty globe CFLs installed in apartment units

4. 9 Watt to 14 Watt specialty globe CFLs installed in common areas

5. 7 Watt to 9 Watt specialty candelabra CFLs installed in apartment units

Table 106 lists the baseline incandescent equivalent wattages for the various CFL measures installed within the program.

Table 106. Baseline Incandescent Wattages Measure Evaluated Incandescent Baseline Wattage(a)

9 watt CFL (globe) 40 7 watt CFL (candelabra) 40 9 watt CFL (candelabra) 40 13 watt CFL (standard) 60 14 watt CFL (globe) 60 19 watt CFL (standard) 75 Notes: (a) Incandescent-equivalent wattage based on ENERGY STAR Lighting Calculator www.ENERGYSTAR.gov/ia/business/bulk.../light_bulb_calculator.xlsx

130

The Evaluation Team used the following equations to calculate energy and demand savings for CFLs:

Equation 16. CFL Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 =(𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐶𝐶𝐶𝐶)𝑥𝑥 (𝐼𝐼𝑤𝑤𝐸𝐸𝐷𝐷𝐷𝐷 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷 ∗ 365) 𝑥𝑥 (1 + 𝑘𝑘𝐻𝐻𝐹𝐹𝐷𝐷)

1,000

Equation 17. CFL Demand Savings

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 = (𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐶𝐶𝐶𝐶)𝑥𝑥 𝐶𝐶𝑀𝑀𝐸𝐸𝑀𝑀𝑆𝑆𝐸𝐸𝑡𝑡𝐷𝐷𝑀𝑀𝑆𝑆𝐷𝐷 𝐹𝐹𝑤𝑤𝑆𝑆𝑀𝑀𝑀𝑀𝑃𝑃 𝑥𝑥 (1 + 𝑘𝑘𝐻𝐻𝐹𝐹𝑡𝑡)

1,000

Where:

• Baseline Wattage (Wbaseline) = Assigned incandescent-equivalent wattage of the lamp or bulb being replaced with a CFL

• CFL Wattage (WCFL) = Actual installed wattage of the CFL (varies by CFL type)

• Daily hours of use = Average number of hours a day the light is in use

• WHFe = Waste Heat Factor for energy use, accounts for the effects of more-efficient lighting on cooling energy use

• WHFd = Waste Heat Factor for demand, accounts for the effects of more-efficient lighting on cooling energy demand

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many CFLs are expected to be in use and saving energy during the peak summer demand period

The MFDI program installs both standard and specialty CFL lamps in apartment units and in common areas. The tables below list the ex post variable assumptions and the source of each assumption for all CFL program measures. The Evaluation Team calculated separate ex post savings for those with electric heating (IPL Electric) and for those with gas heating (IPL Joint). Additionally, the program installed varying wattages for CFLs of the same type (i.e. standard, specialty globe, specialty candelabra, etc.). The program tracking database did not include the quantity of installed lamps by wattage. Because of this, the Evaluation Team calculated an average ex post per-measure savings by CFL lamp type.

Table 107 lists the assumptions and the source of each assumption for standard CFLs.

Table 107. MFDI Ex Post Variable Assumptions for Standard CFLs

Variable Space

Heating Fuel

Standard CFL (In-Unit) Value

Standard CFL (Common Area)

Value Source

Daily Hours of Use Electric & Gas

2.47 16.30 In-Unit: Indiana Lighting Logger Hours of Use Study (PY2014) Common Area: Illinois TRM v.4.0

Baseline Wattage (Wbase)

Electric & Gas

60 (13 watt CFL) 75 (19 watt CFL)


ENERGY STAR Lighting Calculator

CFL Wattage (WCFL)

Electric & Gas

13 watt CFLs 19 watt CFLs


Wattages installed within the program

131

Variable Space

Heating Fuel

Standard CFL (In-Unit) Value

Standard CFL (Common Area)

Value Source

Energy Waste Heat Factor (WHFe)

Electric 0.55 0.55 Indiana TRM for Indianapolis for those with AC and electric heating


Gas 1.06 1.06 Indiana TRM for Indianapolis for those with AC and gas heating

Demand Waste Heat Factor (WHFd)

Electric & Gas

1.07 1.07 Indiana TRM for Indianapolis for those with cooling present

Coincidence Factor

Electric & Gas

0.11 0.11 Indiana TRM

Table 108 lists the assumptions and the source of each assumption for specialty globe CFLs.

Table 108. MFDI Ex Post Variable Assumptions for Specialty Globe CFLs

Variable Space Heating Fuel

Specialty Globe CFL (In-Unit)

Value

Specialty Globe CFL (Common Area)

Value Source

Daily Hours of Use Electric & Gas 2.47 16.30

In-Unit: Indiana Lighting Logger Hours of Use Study (PY2014) Common Area: Illinois TRM v.4.0

Baseline Wattage (Wbase)

Electric & Gas 40 (9 watt CFL)


60 (14 watt CFL) ENERGY STAR Lighting Calculator

CFL Wattage (WCFL) Electric & Gas 9 watt CFLs


14 watt CFLs Wattages installed within the program


Electric 0.55 0.55

Indiana TRM for Indianapolis for those with AC and electric heating


Gas 1.06 1.06

Indiana TRM for Indianapolis for those with AC and gas heating


Electric & Gas 1.07 1.07 Indiana TRM for Indianapolis for those with cooling present

Coincidence Factor Electric & Gas 0.11 0.11 Indiana TRM

Table 109 lists the assumptions and the source of each assumption for specialty candelabra CFLs.

132

Table 109. MFDI Ex Post Variable Assumptions for Specialty Candelabra CFLs

Variable Space Heating Fuel

Specialty Candelabra CFL (In-Unit) Value

Source

Daily Hours of Use Electric & Gas 3.26 Illinois TRM v.4.0 Baseline Wattage (Wbase) Electric & Gas 40 ENERGY STAR Lighting Calculator

CFL Wattage (WCFL) Electric & Gas 7 watt CFLs 9 watt CFLs

Wattages installed within the program


Electric n/aa Indiana TRM for Indianapolis for those with AC and electric heating


Gas 1.06 Indiana TRM for Indianapolis for those with AC and gas heating


Electric & Gas 1.07 Indiana TRM for Indianapolis for those with cooling present

Coincidence Factor Electric & Gas 0.11 Indiana TRM Note: (a) Specialty candelabra CFLs only installed in homes with gas heating (IPL Joint participants) as indicated in the program tracking database

Table 110 shows the ex ante deemed savings as well as the resulting ex post per measure savings for all program CFL measures.

Table 110. MFDI CFL Ex Ante Deemed and Ex Post per-Measure Savings

Measure Location


Ex Ante Deemed Savings

(kW)

Ex Post per-Measures

Savings (kWh)

Ex Post per-Measure Savings

(kW) Standard CFL – IPL Electric In-Unit 26.89 0.006 25.54 0.006 Standard CFL – IPL Joint In-Unit 51.80 0.006 49.22 0.006 Standard CFL – IPL Electric Common Area 51.00 0.010 168.53 0.006 Standard CFL – IPL Joint Common Area 51.00 0.010 324.81 0.006 Specialty Globe CFL – IPL Electric In-Unit 26.89 0.006 19.09 0.005 Specialty Globe CFL – IPL Joint In-Unit 51.80 0.006 36.79 0.005 Specialty Globe CFL – IPL Electric Common Area 51.00 0.010 125.99 0.005 Specialty Globe CFL – IPL Joint Common Area 51.00 0.010 242.82 0.005 Specialty Candelabra CFL – IPL Joint In-Unit 16.51 0.010 40.36 0.004

LED Night Lights The Evaluation Team used the following equations to calculate energy savings for LED night lights:

Equation 18. LED Night Light Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 =(𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐿𝐿𝐿𝐿 𝑁𝑁𝐶𝐶)𝑥𝑥 (𝐼𝐼𝑤𝑤𝐸𝐸𝐷𝐷𝐷𝐷 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷 ∗ 365)

1,000

133

Where:

• Baseline Wattage (Wbase) = Assigned incandescent-equivalent wattage of the night light being replaced with a LED night light

• CFL Wattage (WLED NL) = Wattage of the LED night light

• Daily hours of use = Average number of hours per day the night light is in use

Table 111 lists the assumptions and the source of each assumption for LED night lights.

Table 111. Ex Post Variable Assumptions for LED Night Lights Variable LED Night Light Value Source

Baseline Wattage (Wbase) 5 Indiana TRM CFL Wattage (WCFL) 0.5 Wattage installed within the program Daily hours of use 8 Indiana TRM

Table 112 shows the ex ante deemed savings and resulting ex post per-measure savings per for LED night lights.

Table 112. LED Night Light Ex Ante Deemed and Ex Post per-Measure Savings

Measure Ex Ante Deemed

Savings (kWh) Ex Ante Deemed

Savings (kW) Ex Post per-Measure

Savings (kWh) Ex Post per-Measure

Savings (kW) LED Night Light 13.60 0.000 13.14 0.000

There are no LED night light demand savings attributed to the program. The night lights typically operate outside of peak summer demand hours (at night) and therefore do not accrue demand savings.

Low-Flow Bathroom and Kitchen Faucet Aerators The Evaluation Team used the following equations to calculate energy and demand savings of low-flow bathroom and kitchen faucet aerators in homes with electric water heaters.

Equation 19. Faucet Aerator Energy Savings (Electric Water Heater)

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎(𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =𝑆𝑆 𝑥𝑥 𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠 ∗ 365 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊 ∗ 3412

Equation 20.Faucet Aerator Demand Savings (Electric Water Heater)

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎 (𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =�𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙� ∗ 60 ∗ 𝑆𝑆 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊 ∗ 3412∗ 𝐶𝐶𝐹𝐹

Where:

• S = Constant used to convert the weight of water from gallons to pounds (8.3 lbs/gallon)

• Gallons Saved per Day (GPDsaved) = Amount of water saved in gallons per day (see equations below)

134

• Tmix = Temperature of the water leaving the aerator

• Tinlet = Temperature of the water that enters the water heater

• EF = Efficiency factor of the electric water heater that is in operation

• gpmbase = The baseline flow rate in gallons per minute

• gpmLowflow = The low-flow rate in gallons per minute

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many aerators are expected to be in use and saving energy during the peak summer demand period

The following equations determine the expected gallons of hot water saved per day, when installing a low-flow aerator:

Equation 21. Gallons Saved per Day 𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎

=𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷/𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀/𝑡𝑡𝑤𝑤𝐷𝐷)

(𝑀𝑀𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑓𝑓𝑤𝑤𝑀𝑀𝑆𝑆𝐷𝐷𝑀𝑀𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡)

𝐿𝐿𝑀𝑀𝑤𝑤 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎

= 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷/𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀/𝑡𝑡𝑤𝑤𝐷𝐷)


Where:

• gpmbase = Baseline flow rate in gallons per minute

• gpmLowflow = Low-flow rate in gallons per minute

Finally, the team used the following equation to calculate hot water savings, in gallons saved per day, when installing a low flow aerator:

Equation 22. Gallons Saved Per Day 𝐺𝐺𝑤𝑤𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀𝑃𝑃 𝑆𝑆𝑤𝑤𝑤𝑤𝐷𝐷𝑡𝑡 𝐷𝐷𝐷𝐷𝑃𝑃 𝐼𝐼𝑤𝑤𝐷𝐷 (𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠)

= (𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎 − 𝐿𝐿𝑀𝑀𝑤𝑤 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎)

𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎=𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷/𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀/𝑡𝑡𝑤𝑤𝐷𝐷)


𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎



Where: • gpmbase = The flow rate in gallons per minute of the existing faucet

• gpmLowflow = The flow rate in gallons per minute of the low-flow aerator

Table 113 summarizes the energy savings assumptions for kitchen and faucet aerators, and identifies the source of each assumption.

135

Table 113. Ex Post Variable Assumptions for Aerators

Variable Kitchen Faucet

Aerators (1.5 GPM)

Bathroom Faucet Aerators

(1.0 GPM) Source

Minutes of Use/Person/Day

4.50 1.60 Michigan Showerhead and Faucet Aerator Meter Study; 2013a

People per Household 1.83 1.83 Data from Ferret Software for Indiana for MF units onlyb Faucets per Household

1.00 1.24 Cadmus' 2012 on-site data for IPL; 80 measurements across 97 units

Gpmbase 2.05 1.98 Average gpm from database; implementer gathered on-site data for 10% of installs

GpmLowflow 1.50 1.00 Gpm of installed aerators

Temperatureinlet 58.1˚F 58.1˚F DHW_Event Schedule Generator developed by NRELc; Inlet water temperature generated for Indianapolis, Indiana

Temperaturemix 93˚F 86˚F Michigan Showerhead and Faucet Aerator Meter Study; 2013a

(Electric WH) 0.98 0.98 Indiana TRM Coincidence Factor (CF)

0.00262 0.00262 Indiana TRM

Conversion Factor (S) 8.33 8.33 Engineering constant in units of BTU/ (gal˚F) Notes: (a) Michigan Evaluation Working Group Showerhead and Faucet Aerator Meter Study. June 2013. (b) ACS 3-year public use micro-data from 2008 to 2010 for multifamily units from Ferret Software for Indiana (c) http://www1.eere.energy.gov/buildings/residential/ba_analysis_spreadsheets.html

Table 114 shows the ex ante deemed savings and the ex post per-measure savings for kitchen and bathroom aerators.

136

Table 114. Low-flow Aerator Ex Ante Deemed and Ex Post per measure Savings


Savings (kWh)

Ex Ante Deemed

Savings (kW)


Savings (kWh)

Ex Post per-Measure

Savings (kW) Bathroom Faucet Aerator 52.07 0.008 58.33 0.011 Kitchen Faucet Aerator 103.40 0.008 141.96 0.007

Low-Flow Showerheads The following equations determine the energy and demand savings for low-flow showerheads in homes with electric water heaters.

Equation 23. Low-flow Showerhead Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏 (𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =𝑆𝑆 𝑥𝑥 𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠 ∗ 365 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

3412 ∗ 𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊

Equation 24. Low-flow Showerhead Demand Savings

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏(𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =(𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙) ∗ 60 ∗ 𝑆𝑆 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

3412 ∗ 𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊∗ 𝐶𝐶𝐹𝐹

Where:



• Tmix = Temperature of the water leaving the showerhead



• gpmbase = The baseline flow rate in gallons per minute of the existing showerhead

• gpmLowflow = The low flow rate in gallons per minute of the installed low-flow showerhead

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many showerheads are expected to be in use and saving energy during the peak summer demand period

The following equations determine the expected gallons of hot water saved per day, when installing a low-flow showerhead:

Equation 25. Gallons Saved per Day 𝐺𝐺𝑤𝑤𝐷𝐷𝐷𝐷𝑀𝑀𝑀𝑀𝑃𝑃 𝑆𝑆𝑤𝑤𝑤𝑤𝐷𝐷𝑡𝑡 𝐷𝐷𝐷𝐷𝑃𝑃 𝐼𝐼𝑤𝑤𝐷𝐷 (𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠)

= (𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠 − 𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠)

𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠= 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀)∗ (𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝑃𝑃 𝑡𝑡𝑤𝑤𝐷𝐷) (𝑀𝑀𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃ℎ𝐷𝐷𝑤𝑤𝑡𝑡𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡)⁄

137

𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠= 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀)∗ (𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝑃𝑃 𝑡𝑡𝑤𝑤𝐷𝐷) (𝑀𝑀𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃ℎ𝐷𝐷𝑤𝑤𝑡𝑡𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡)⁄

Where:



Table 115 summarizes the savings assumptions for low-flow showerheads, and identifies the source of each assumption.

Table 115. Ex Post Variable Assumptions for Low-flow Showerheads Variable Value Sources

Minutes per shower/person 7.8 Michigan Showerhead and Faucet Aerator Meter Study; 2013a Showers per day/person 0.60 Michigan Showerhead and Faucet Aerator Meter Study; 2013a Showerheads per household 1.18 Cadmus’ 2012 on-site data for IPL; 80 measurements across 97 units People per household 1.83 Data from Ferret Software for Indiana for MF units onlyb

Gpmbase 2.21 Average gpm from database; implementer gathered on-site data for 10% of installs

GpmLowflow 1.50 Gpm of installed low-flow showerheads

Temperatureinlet 58.1˚F DHW_Event Schedule Generator developed by NRELc; Inlet water temperature generated for Indianapolis, Indiana

Temperaturemix 101˚F Michigan Showerhead and Faucet Aerator Meter Study; 2013a

Water Heater Efficiency Factor (Electric WH)

0.98 Indiana TRM

Coincidence Factor (CF) 0.00371 Indiana TRM Conversion Factor (S) 8.33 Engineering constant in units of BTU/ (gal˚F) Notes: (a) Michigan Evaluation Working Group Showerhead and Faucet Aerator Meter Study. June 2013. (b) ACS 3-year public use micro-data from 2008 to 2010 for multifamily units from Ferret Software for Indiana (c ) http://www1.eere.energy.gov/buildings/residential/ba_analysis_spreadsheets.html

Table 116 shows the ex ante deemed and the ex post per-measure energy and demand savings for low-flow showerheads.

Table 116. Low-flow Showerhead Ex Ante Deemed and Ex Post per Measure Savings


Savings (kWh)

Ex Ante Deemed Savings (kW)


Savings (kWh)

Ex Post per-Measure

Savings (kW) Low-Flow Showerhead 343.30 0.023 201.49 0.017

138

Programmable Thermostats Absent any specific information about the type of HVAC unit, the Evaluation Team assumed that if the primary heating fuel is electric in multifamily apartment units, the electric heating equipment is baseboard heating. Since a thermostat does not typically control baseboard heating, team based ex post savings on cooling savings only.

The following equations determine the energy and demand savings for programmable thermostats.

Equation 26. Programmable Thermostat Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃(𝑔𝑔𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏𝑖𝑖,𝑙𝑙𝑖𝑖𝑖𝑖ℎ 𝐴𝐴𝐶𝐶) = 𝐹𝐹𝐿𝐿𝐻𝐻𝑒𝑒𝑙𝑙𝑙𝑙𝑏𝑏 ∗ 𝐵𝐵𝑇𝑇𝑈𝑈ℎ𝑆𝑆𝐸𝐸𝐸𝐸𝑅𝑅 ∗ 1000

∗ 𝐸𝐸𝑆𝑆𝐹𝐹𝑒𝑒𝑙𝑙𝑙𝑙𝑏𝑏

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃(𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 ℎ𝑏𝑏𝑏𝑏𝑖𝑖,𝑙𝑙𝑖𝑖𝑖𝑖ℎ 𝐴𝐴𝐶𝐶) = � 𝐹𝐹𝐿𝐿𝐻𝐻𝑒𝑒𝑙𝑙𝑙𝑙𝑏𝑏 ∗ 𝐵𝐵𝑇𝑇𝑈𝑈ℎ𝑆𝑆𝐸𝐸𝐸𝐸𝑅𝑅 ∗ 1000

∗ 𝐸𝐸𝑆𝑆𝐹𝐹𝑒𝑒𝑙𝑙𝑙𝑙𝑏𝑏�

The Indiana TRM does not include algorithms to calculate demand savings for programmable thermostats. To calculate demand savings, the team obtained the hourly simulated data from the same DOE2 files used to determine the savings within the Indiana TRM. The Indiana TRM identifies peak from 3:00 p.m. to 6:00 p.m. for summer weekdays. The team obtained the hourly simulated demand usage data for the four prototype homes used within the TRM. The output data was both with and without programmable thermostats included. The team normalized the output data by the square foot of conditioned floor area, and applied the average size home using data from the 2011 American Housing Survey to obtain a demand savings value for multifamily and mobile home units.

The following equation establishes demand savings for programmable thermostats:

Equation 27. Programmable Thermostat Demand Savings

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃(𝑙𝑙𝑖𝑖𝑖𝑖ℎ 𝐴𝐴𝐶𝐶) = 𝐴𝐴𝑤𝑤𝐷𝐷𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷 𝑘𝑘𝑘𝑘 𝑆𝑆𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑝𝑝𝑏𝑏𝑏𝑏𝑝𝑝 ∗ 𝐴𝐴𝑤𝑤𝐷𝐷𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷 𝑃𝑃𝑠𝑠 𝑓𝑓𝑀𝑀𝑀𝑀𝐶𝐶 𝑈𝑈𝑏𝑏𝑖𝑖𝑖𝑖

𝑆𝑆𝑠𝑠 𝑓𝑓𝑀𝑀𝐼𝐼𝑁𝑁 𝑇𝑇𝑅𝑅𝑀𝑀 𝑚𝑚𝑙𝑙𝑠𝑠𝑏𝑏𝑏𝑏

Where:

• FLHcool = Full load cooling hours for Indianapolis, IN

• FLHheat = Full load heating hours for Indianapolis, IN

• BTUh = Capacity of heating and cooling system (Btu/hours)

• SEER = Efficiency of existing cooling system controlled by programmable thermostat

• COP = Electric heating efficiency of existing heating system controlled by programmable thermostat

• ESFcool = Energy Savings Factor for cooling

• ESFheat = Energy Savings Factor for heating

• 3,412 = Converting Btu to kWh

• 1,000 = Converting watts to kilowatts

• Average kW Savingspeak = Average demand savings from the hourly simulated IN TRM data

139

• Average sq ftMF Unit = Average square footage of multifamily unit

• Sq ftIN TRM Model = Square footage of single-family model home used to generate hourly simulated kW savings (IN TRM data)

Table 117 summarizes the savings assumptions for programmable thermostats, and identifies the source of each assumption.

Table 117. Ex Post Variable Assumptions for Programmable Thermostats Variable Value Sources

FLHcool 487 Indiana TRM for Indianapolis, IN

Capacity (BTUh) 24,000

• Based on the 2011 American Housing Survey, the average size of a manufactured/mobile home is 1,100 SF (could not find Indiana-specific data for mobile homes in the Indiana census, but this size agrees with the average sizes for sale in Indiana found here): http://www.mhvillage.com/SearchState.php?State=IN

• Census data taken from: http://www.census.gov/programs-surveys/ahs/data/2011/h150-11.html

• Using 1,100 SF, assume an average heating/cooling capacity of 2.0 ton (12,000 btuh/ton) for Northern Indiana based on: http://www.energystar.gov/ia/partners/bldrs_lenders_raters/downloads/SizingGuidelines.pdf

SEER 10 Indiana TRM – minimum standard efficiency ESFcooling 0.09 Indiana TRM Btu/kWh 3,412 Conversion Watts/kWh 1,000 Conversion Average kW Savingspeak

0.229 Average kW savings using IN TRM hourly simulated data for M-F June-August 3PM to 6PM (peak time)

Average sq ftMF

Unit 1,100 Average square footage based on 2011 American Housing Survey

Sq ftIN TRM Model 1,603 Square footage used in IN TRM hourly simulated data

Table 118 shows the ex ante deemed and the ex post per-measure energy and demand savings for programmable thermostats.

Table 118. Programmable Thermostat Ex Ante Deemed and Ex Post per Measure Savings

Measure Ex Ante

Deemed Savings (kWh)

Ex Ante Deemed

Savings (kW)


Savings (kWh)

Ex Post per-Measure

Savings (kW) Programmable Thermostat 120.82 0.000 105.19 0.157

140

Appendix J: Appliance Recycling Program Measures, Assumptions, and Algorithms

Carryover CFLs Savings The Evaluation Team calculated carryover CFL savings for the expected CFLs placed in storage from the past program year (PY2013). The Uniform Methods Project (UMP)15 indicates that most bulbs placed in storage (97%) become installed within four years (including the initial program year) and recommends calculating the installation rate when stored bulbs are installed as follows:

Equation 28. Installation Rates for CFLs in Storage 𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 1 = 𝐼𝐼𝑆𝑆𝑅𝑅𝑆𝑆𝑆𝑆𝑎𝑎𝑠𝑠𝑏𝑏𝑆𝑆𝑏𝑏𝑠𝑠

𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 2 = (𝑆𝑆𝑀𝑀𝑀𝑀𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷 %𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 1 ∗ 41%) + 𝐼𝐼𝑆𝑆𝑅𝑅𝑆𝑆𝑆𝑆𝑎𝑎𝑠𝑠𝑏𝑏𝑆𝑆𝑏𝑏𝑠𝑠


𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 4 = 97% − 𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 3

Where:

• ISRsurveyed = Installation rate from self-reported survey results for the year the measure was distributed

• ISRYear 2 = Percentage of stored CFLs installed in Year 2 (one year after program participation)

• ISRYear 3 = Percentage of stored CFLs installed in Year 3 (two years after program participation)

• ISRYear 4 = Percentage of stored CFLs installed in Year 4 (three years after program participation)

• Storage%Year 1 = Percentage of CFLs placed in storage for the year the measure was distributed

• 41% = Total percentage of CFLs installed (of original CFLs in storage) within two years, including the program year

• 69% = Total percentage of CFLs installed (of original CFLs in storage) within three years, including the program year

• 97% = Total percentage of CFLs installed (of original CFLs in storage) within four years, including the program year

In the year customers received bulbs, they stored 60% of all bulbs received through the program. Of those 60% of bulbs, the UMP assumes that 41% are installed in the next year, 28% of the remaining stored bulbs are installed the following year, and 97% of all stored bulbs are installed ibythe fourth year (included the program year). PY2013 was the first year in which the Appliance Recycling program began leaving CFLs with participants. The PY2014 evaluation includes carryover savings for the second year,

15 Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Chapter

21: Residential Lighting Evaluation Protocol. Section 4.12 In-Service Rate. December 2014.

141

assuming, 41% of stored bulbs were installed in PY2014 (as specified in the UMP). The evaluation team will include carryover savings for the 28% of the remaining bulbs in storage for CFLs distributed in PY2013, and 41% for those distributed in PY2014 as part of the PY2015 evaluation. Table 119 summarizes the percent of stored bulbs expected to be installed in PY2014.

Table 119. Percentage of Stored CFLs Installed by Year

Program Year % Stored CFLs Installed in PY2014

% Stored CFLs Installed in PY2015

PY2013 41.0% 28.0% PY2014 n/a 41.0%

To calculate the carryover CFL savings, the Evaluation Team used self-reported installation rates and applied the equations above to estimate the number of stored CFLs installed in PY2014. Table 120 summarizes the number of stored CFLs installed in PY2014. The evaluation includes carryover savings for a total of 480 CFLs.

Table 120. Quantity of CFLs Installed in PY2014

Program Year Measure Percent of Stored CFLs Installed in

PY2014


CFL Volume Installed in PY2014

PY2013 18W CFL 41.0% 1,171 480 Total 1,171 480

Table 121 summarizes the additional carryover gross savings from the stored CFL measures installed in PY2014.

Table 121. Carryover Gross Savings for CFL Measures (Savings Added to PY2014)

Program Year Measure

Total Volume Installed

in PY2014

Ex Post per-bulb Savings (kWh)

Ex Post per-bulb Savings

(kW)

Total Gross Carryover

kWh Savings


kW Savings

PY2013 18W CFL 480 48.25 0.007 23,173 3.18 Total 480 23,173 3.18 Notes: Values rounded for reporting purposes.

Table 122 summarizes the additional carryover net savings from the storage CFL measures installed in PY2014.

142

Table 122. Carryover Net Savings for CFL Measures (Savings Added to PY2014)

Program Year Measure


in PY2014

Total Gross

Carryover kWh

Savings


kW Savings NTG

Total Net Carryover

kWh Savings

Total Net Carryover

kW Savings

PY2013 18W CFL 480 23,173 3.18 0.54 12,513 1.72 Total 480 23,173 3.18 0.54 12,513 1.72 Notes: Values rounded for reporting purposes.

143

Appendix K: Home Energy Inspector Program Measures, Assumptions, and Algorithms

This appendix presents detailed information on the analysis and supporting assumptions for the following Home Energy Inspector program energy kit measures:

• 13-watt CFLs

• 19-watt CFLs

• LED Night Lights

• Bathroom Faucet Aerators

• Kitchen Faucet Aerators

• Low-Flow Showerheads

Compact Fluorescent Lamps The Evaluation Team used the following equations to calculate energy and demand savings for CFLs:

Equation 29. CFL Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 =(𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐶𝐶𝐶𝐶)𝑥𝑥 (𝐼𝐼𝑤𝑤𝐸𝐸𝐷𝐷𝐷𝐷 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷 ∗ 365) 𝑥𝑥 (1 + 𝑘𝑘𝐻𝐻𝐹𝐹𝐷𝐷)

1,000

Equation 30. CFL Demand Savings

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 = (𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐶𝐶𝐶𝐶)𝑥𝑥 𝐶𝐶𝑀𝑀𝐸𝐸𝑀𝑀𝑆𝑆𝐸𝐸𝑡𝑡𝐷𝐷𝑀𝑀𝑆𝑆𝐷𝐷 𝐹𝐹𝑤𝑤𝑆𝑆𝑀𝑀𝑀𝑀𝑃𝑃 𝑥𝑥 (1 + 𝑘𝑘𝐻𝐻𝐹𝐹𝑡𝑡)

1,000

Where:

• Baseline Wattage (Wbase) = Assigned incandescent-equivalent wattage of the lamp or bulb being replaced with a CFL

• CFL Wattage (WCFL) = Actual installed wattage of the CFL (13 watts or 19 watts)

• Daily hours of use = Average number of hours per day the light is in use

• WHFe = Waste heat factor for energy use, accounts for the effects of more-efficient lighting on cooling energy use

• WHFd = Waste heat factor for demand, accounts for the effects of more-efficient lighting on cooling energy demand

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many CFLs are expected to be in use and saving energy during the peak summer demand period

Table 123 lists the assumptions and the source of each assumption for the distributed CFLs.

144

Table 123. Ex Post Variable Assumptions for CFLs

Variable 13-Watt CFL Value

19-Watt CFL Value Source

Baseline Wattage (Wbase) 60 75 Compared lumens with ENERGY STAR values; ENERGY STAR Lighting Calculator

CFL Wattage (WCFL) 13 19 Actual wattages of distributed CFLs

Daily hours of use 2.47 2.47 2012 Energizing Indiana Programs. Indiana Core Lighting Logger Hours of Use Study. July 29, 2014.

Energy Waste Heat Factor (WHFe) – Electric Heat (IPL Electric)

0.55 0.55 Indiana TRM for Indianapolis for those with AC and electric heating

Energy Waste Heat Factor (WHFe) – Gas Heat (IPL Joint)

1.06 1.06 Indiana TRM for Indianapolis for those with AC and gas heating

Demand Waste Heat Factor (WHFd) 1.07 1.07 Indiana TRM for Indianapolis for those with AC and electric heating and those with AC and gas heating

Coincidence Factor 0.11 0.11 Indiana TRM

Table 124 shows the ex ante deemed savings and the resulting ex post per-measure savings per for CFL measures.

Table 124. CFL Ex Ante and Ex Post per-Measure Savings

Program Measure Updated Ex Ante

Deemed Savings (kWh)

Updated Ex Ante Deemed Savings

(kW)

Ex Post per-measure

Savings (kWh)

Ex Post per-measure

Savings (kW)

IPL Electric CFL – 13-Watt 26.89 0.006 23.3 0.006 CFL – 19-Watt 32.04 0.007 27.8 0.007

IPL Joint CFL – 13-Watt 51.34 0.006 44.9 0.006 CFL – 19-Watt 61.17 0.007 53.5 0.007

On average, the ex post per-measure savings are comparable to the ex ante savings. The ex ante and ex post savings apply waste heat factors based on the primary heating fuel type. For those with electric space heating (IPL Electric) the CFL savings are nearly half than the savings for those with gas heated homes (IPL Joint). Waste heat factors for electrically heated homes account for increased heating loads, as more energy is needed to supplement the heat that was once given off by the incandescent lamps, thus resulting in an energy savings penalty. Measures installed in homes with gas heating (IPL Joint) also experience a heating penalty, which is captured in therms. Therm savings are outside of the scope of this evaluation.

145

Carryover CFLs Savings The Evaluation Team calculated carryover CFL savings for the expected CFLs placed in storage in past program years. The Uniform Methods Project (UMP)16 indicates that most bulbs placed in storage (97%) become installed within four years of purchase (including the initial program year) and recommends calculating the installation rate when stored bulbs are installed as follows:

Equation 31. Installation Rates for CFLs in Storage 𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 1 = 𝐼𝐼𝑆𝑆𝑅𝑅𝑆𝑆𝑆𝑆𝑎𝑎𝑠𝑠𝑏𝑏𝑆𝑆𝑏𝑏𝑠𝑠



𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 4 = 97% − 𝐼𝐼𝑆𝑆𝑅𝑅𝑌𝑌𝑏𝑏𝑏𝑏𝑎𝑎 3

Where:

• ISRsurveyed = Installation rate from self-reported survey results for the year the measure was distributed

• ISRYear 2 = Percentage of stored CFLs installed in Year 2 (1 year after program participation)

• ISRYear 3 = Percentage of stored CFLs installed in Year 3 (2 years after program participation)

• ISRYear 4 = Percentage of stored CFLs installed in Year 4 (3 years after program participation)

• Storage%Year 1 = Percentage of CFLs placed in storage for the year the measure was distributed

• 41% = Total percentage of CFLs installed (of original CFLs in storage) within two years, including the program year

• 69% = Total percentage of CFLs installed (of original CFLs in storage) within three years, including the program year

• 97% = Total percentage of CFLs installed (of original CFLs in storage) within four years, including the program year

In the year customers received bulbs, they stored on average 12.5% of all bulbs received through the program. Of those 12.5% of bulbs, the UMP assumes that 41% are installed in the next year, 28% of the remaining stored bulbs are installed the following year, and 97% of all stored bulbs are installed in the fourth year (including the program year). Table 125 summarizes the percent of stored bulbs expected to be installed in PY2014.

16 Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Chapter

21: Residential Lighting Evaluation Protocol. Section 4.12 In-Service Rate. December 2014.

146

Table 125. Percentage of Stored CFLs Installed by Year

Program Year % Stored CFLs Installed in PY2012



PY2011 41.0% 28.0% 0.53% PY2012 n/a 41.0% 28.0% PY2013 n/a n/a 41.0%

To calculate the carryover CFL savings, the Evaluation Team used self-reported installation rates and applied the equations above to estimate the number of stored CFLs installed in PY2014 from past program years (PY2011 through PY2013). Table 126 summarizes the number of stored CFLs installed in PY2014. The evaluation includes savings for a total of 702 CFLs

Table 126. Quantity of CFLs Installed in PY2014

Program Year Measure Percent of Stored CFLs Installed in

PY2014


CFL Volume Installed in PY2014

PY2011 13W CFL 0.5% 581 3 19W CFL 0.5% 581 3

PY2012 13W CFL 28.0% 370 104 19W CFL 28.0% 370 104

PY2013 13W CFL 41.0% 596 244 19W CFL 41.0% 596 244

Total 3,094 702

Table 127 summarizes the additional carryover gross savings from the stored CFL measures installed in PY2014.

Table 127. Carryover Gross Savings for CFL Measures (Savings Added to PY2014)

Program Year Program Measure


in PY2014



(kW)


kWh Savings


kW Savings

PY2011 IPL Electric 13W CFL 3 39.79 0.005 124 0.02 19W CFL 3 47.41 0.006 147 0.02

PY2012 IPL Electric

13W CFL 40 23.31 0.006 922 0.22 19W CFL 40 27.77 0.007 1,099 0.26

IPL Joint 13W CFL 64 44.92 0.006 2,876 0.35 19W CFL 64 53.52 0.007 3,427 0.42

PY2013 IPL Electric

13W CFL 72 23.31 0.006 1,681 0.40 19W CFL 72 27.77 0.007 2,003 0.48

IPL Joint 13W CFL 172 44.92 0.006 7,727 0.95 19W CFL 172 53.52 0.007 9,206 1.13

147



in PY2014



(kW)


kWh Savings


kW Savings

Total 702 29,213 4.25

Table 128 summarizes the additional carryover net savings from the CFL measures installed in PY2014.

Table 128. Carryover Net Savings for CFL Measures (Savings Added to PY2014)



in PY2014

Total Gross

Carryover kWh

Savings


kW Savings NTG

Total Net Carryover

kWh Savings

Total Net Carryover

kW Savings

PY2011 IPL Electric 13W CFL 3 124 0.02 0.54 67 0.01 19W CFL 3 147 0.02 0.54 79 0.01

PY2012 IPL Electric

13W CFL 40 922 0.22 0.54 498 0.12 19W CFL 40 1,099 0.26 0.54 593 0.14

IPL Joint 13W CFL 64 2,876 0.35 0.54 1,553 0.19 19W CFL 64 3,427 0.42 0.54 1,850 0.23

PY2013 IPL Electric

13W CFL 72 1,681 0.40 0.78 1,311 0.31 19W CFL 72 2,003 0.48 0.78 1,563 0.37

IPL Joint 13W CFL 172 7,727 0.95 0.78 6,027 0.74 19W CFL 172 9,206 1.13 0.78 7,181 0.88

Total 702 29,213 4.25 0.70 20,723 3.01

LED Night Lights The Evaluation Team used the following equations to calculate energy savings for LED night lights:

Equation 32. LED Night Light Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃 =(𝑘𝑘𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 −𝑘𝑘𝐶𝐶𝐿𝐿𝐿𝐿 𝑁𝑁𝐶𝐶)𝑥𝑥 (𝐼𝐼𝑤𝑤𝐸𝐸𝐷𝐷𝐷𝐷 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷 ∗ 365)

1,000

Where:

• Baseline Wattage (Wbase) = Assigned incandescent-equivalent wattage of the night light being replaced with a LED night light

• CFL Wattage (WLED NL) = Wattage of the LED night light

• Daily hours of use = Average number of hours per day the night light is in use

Table 129 lists the assumptions and the source of each assumption for distributed LED night lights.

148

Table 129. Ex Post Variable Assumptions for LED Night Lights Variable LED Night Light Value Source

Baseline Wattage (Wbase) 5 Indiana TRM CFL Wattage (WCFL) 0.33

Daily hours of use 8

Table 130 shows the ex ante deemed savings and resulting ex post per-measure savings per for LED night lights.

Table 130. LED Night Light Ex Ante and Ex Post per-Measure Savings

Measure Updated Ex Ante

Savings (kWh) Updated Ex Ante

Savings (kW) Ex Post Savings

(kWh) Ex Post Savings

(kW) LED Night Light 13.60 0.000 13.64 0.000

There are no LED night lights demand savings attributed to the program. The night lights typically operate outside of peak summer demand hours (at night) and therefore do not accrue demand savings.

Low-Flow Bathroom and Kitchen Faucet Aerators The Evaluation Team used the following equations to calculate the energy and demand savings of low-flow kitchen and bathroom faucet aerators in IPL electric homes with electric water heaters.

Equation 33. Faucet Aerator Energy Savings (Electric Water Heater)

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎(𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =𝑆𝑆 𝑥𝑥 𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠 ∗ 365 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊 ∗ 3412

Equation 34. Faucet Aerator Demand Savings (Electric Water Heater)

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎 (𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =�𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙� ∗ 60 ∗ 𝑆𝑆 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊 ∗ 3412∗ 𝐶𝐶𝐹𝐹

Where:



• Tmix = Temperature of the water leaving the aerator



• gpmbase = Baseline flow rate in gallons per minute

• gpmLowflow = Low-flow rate in gallons per minute

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many aerators are expected to be in use and saving energy during the peak summer demand period

149

The following equations determine the expected gallons of hot water saved per day, when installing a low-flow aerator:


= (𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎 − 𝐿𝐿𝑀𝑀𝑤𝑤 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎)

𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎

=𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑀𝑀𝑃𝑃𝐷𝐷/𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀/𝑡𝑡𝑤𝑤𝐷𝐷)


𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝐴𝐴𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑙𝑙𝑎𝑎



Where:

• gpmbase = Baseline flow rate in gallons per minute of the existing faucet

• gpmLowflow = Low-flow rate in gallons per minute of the low-flow aerator

Table 131 summarizes the energy savings assumptions for kitchen and bathroom faucet aerators, and identifies the source of each assumption.

Table 131. Ex Post Variable Assumptions for Aerators

Variable

Kitchen Faucet

Aerators Value

Bathroom Faucet

Aerators Value

Source

Minutes of use/person/ day

4.50 1.60 Michigan Showerhead and Faucet Aerator Meter Study; 2013a

People per household 2.47 2.47 Data from Ferret Software for Indiana Uses ACS three-year public use microdata (2008-2010) for all units

Faucets per household 1 3.15 Indiana Residential Baseline Study (2012) – statewide value Gpmbase 2.44 1.90 2012 on-site data for IPL; 80 measurements across 97 units GpmLowflow 1.49 1.01 2012 on-site data for IPL; 80 measurements across 97 units

Temperatureinlet 58.1˚F 58.1˚F DHW_Event Schedule Generator developed by NRELb; Inlet water temperature generated for Indianapolis, Indiana

Temperaturemix 93˚F 86˚F Michigan Showerhead and Faucet Aerator Meter Study; 2013a

Efficiency Factor (Electric WH)

0.98 0.98 Indiana TRM

Coincidence Factor (CF) 0.00262 0.00262 Indiana TRM Conversion Factor (S) 8.33 8.33 Engineering constant in units of BTU/ (gal˚F) Notes: (a) Michigan Evaluation Working Group Showerhead and Faucet Aerator Meter Study. June 2013. (b) http://www1.eere.energy.gov/buildings/residential/ba_analysis_spreadsheets.html

150

Table 132 shows the ex ante deemed savings and the ex post per-measure savings for kitchen and bathroom aerators.

Table 132. Low-flow Aerator Ex Ante and Ex Post per-Measure Savings




(kWh) Ex Post

Savings (kW) Bathroom Faucet Aerator 34.90 0.008 28.23 0.0097 Kitchen Faucet Aerator 139.68 0.009 333.89 0.0129

Low-Flow Showerheads The following equations determine the energy and demand savings of low-flow showerheads in IPL electric homes with electric water heaters.

Equation 36. Low-Flow Showerheads Energy Savings

𝑘𝑘𝑘𝑘ℎ 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏 (𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =𝑆𝑆 𝑥𝑥 𝐺𝐺𝑃𝑃𝐼𝐼𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠 ∗ 365 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

3412 ∗ 𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊

Equation 37. Low-Flow Showerhead Demand Savings

𝑘𝑘𝑘𝑘 𝑃𝑃𝑤𝑤𝑤𝑤𝐸𝐸𝑀𝑀𝑆𝑆𝑃𝑃𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏(𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝑀𝑀𝑃𝑃𝐸𝐸𝑆𝑆 𝑤𝑤𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 ℎ𝐷𝐷𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃) =(𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙) ∗ 60 ∗ 𝑆𝑆 ∗ (𝑇𝑇𝑚𝑚𝑖𝑖𝑚𝑚 − 𝑇𝑇𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑖𝑖)

3412 ∗ 𝐸𝐸𝐹𝐹𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑖𝑖𝑎𝑎𝑖𝑖𝑒𝑒 𝑊𝑊𝑊𝑊∗ 𝐶𝐶𝐹𝐹

Where:


• Gallons Saved per Day (GPDsaved) = The amount of water saved in gallons per day (see equations below)

• Tmix = Temperature of the water leaving the showerhead



• gpmbase = The baseline flow rate in gallons per minute of the existing showerhead

• gpmLowflow = Low-flow rate in gallons per minute of the low-flow showerhead

• Coincidence Factor (CF) = A number between 0 and 1 indicating how many showerheads are expected to be in use and saving energy during the peak summer demand period

The following equations determine the expected gallons of hot water saved per day, when installing a low-flow showerhead:


= (𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠 − 𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠)

𝐵𝐵𝑤𝑤𝑃𝑃𝐷𝐷 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠= 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀)∗ (𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝑃𝑃 𝑡𝑡𝑤𝑤𝐷𝐷) (𝑀𝑀𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃ℎ𝐷𝐷𝑤𝑤𝑡𝑡𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡)⁄

151

𝐿𝐿𝑀𝑀𝑤𝑤 − 𝐹𝐹𝐷𝐷𝑀𝑀𝑤𝑤 𝐴𝐴𝑀𝑀𝑀𝑀𝑀𝑀𝑤𝑤𝐷𝐷 𝑘𝑘𝑤𝑤𝑀𝑀𝐷𝐷𝑃𝑃 𝑈𝑈𝑃𝑃𝑤𝑤𝑆𝑆𝐷𝐷𝑆𝑆ℎ𝑙𝑙𝑙𝑙𝑏𝑏𝑎𝑎ℎ𝑏𝑏𝑏𝑏𝑠𝑠= 𝑆𝑆𝐷𝐷𝐷𝐷𝑏𝑏𝑙𝑙𝑙𝑙 𝑓𝑓𝑏𝑏𝑙𝑙𝑙𝑙 ∗ (𝑁𝑁𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝐷𝐷𝐷𝐷𝑀𝑀𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝑀𝑀 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡) ∗ (𝐷𝐷𝐸𝐸𝑀𝑀𝑀𝑀𝑀𝑀𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀)∗ (𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃𝑃𝑃𝑀𝑀𝑀𝑀 𝐷𝐷𝐷𝐷𝑃𝑃 𝑡𝑡𝑤𝑤𝐷𝐷) (𝑀𝑀𝑀𝑀𝐷𝐷𝑅𝑅𝐷𝐷𝑃𝑃 𝑀𝑀𝑓𝑓 𝑃𝑃ℎ𝑀𝑀𝑤𝑤𝐷𝐷𝑃𝑃ℎ𝐷𝐷𝑤𝑤𝑡𝑡𝑃𝑃 𝐷𝐷𝐷𝐷𝑃𝑃 ℎ𝑀𝑀𝑀𝑀𝑃𝑃𝐷𝐷ℎ𝑀𝑀𝐷𝐷𝑡𝑡)⁄

Where:



Table 133 summarizes the savings assumptions for low-flow showerheads, and identifies the source of each assumption.

Table 133. Showerhead Energy and Demand Variable Values

Variable Low-Flow

Showerhead Source

Minutes per shower/person

7.80 Michigan Showerhead and Faucet Aerator Meter Study; 2013a

Showers per day/person 0.60 Michigan Showerhead and Faucet Aerator Meter Study; 2013a Showerheads per household

1.63 Indiana Residential Baseline Study (2012) – statewide value

People per household 2.47 Data from Ferret Software for Indiana Uses ACS three-year public use microdata (2008-2010) for all units

Gpmbase 2.63 2012 on-site data for IPL; 80 measurements across 97 units GpmLowflow 1.74 2012 on-site data for IPL; 80 measurements across 97 units

Temperatureinlet 58.1˚F DHW_Event Schedule Generator developed by NRELb; Inlet water temperature generated for Indianapolis, Indiana

Temperaturemix 101˚F Michigan Showerhead and Faucet Aerator Meter Study; 2013a

Efficiency Factor (Electric WH)

0.98 Indiana TRM

Coincidence Factor (CF) 0.00371 Indiana TRM Conversion Factor (S) 8.33 Engineering constant in units of BTU/ (gal˚F) Notes: (a) Michigan Evaluation Working Group Showerhead and Faucet Aerator Meter Study. June 2013. (b) http://www1.eere.energy.gov/buildings/residential/ba_analysis_spreadsheets.html

Table 134 shows the ex ante deemed savings and the ex post savings for low-flow showerheads.

Table 134. Low-flow Showerhead Ex Ante and Ex Post per-Measure Savings




(kWh) Ex Post

Savings (kW) Low-Flow Showerhead 383.83 0.022 245.33 0.211

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