Nova Scotia 2015‐2040  

Demand Side Management (DSM) Potential Study

Presented to

January 7, 2014

Presented by

Todd Williams, Managing Director

One Adelaide Street East, Suite 3000

Toronto, ON M5C 2V9

647.288.5204, Todd.Williams@Navigant.com

Stu Slote, Associate Director (Project Manager)

802.526.5113, Stu.Slote@Navigant.com

Gary Cullen, Director (Potential Study Lead)

360.828.4008, Gary.Cullen@Navigant.com

Navigant

www.navigant.com
i 

Table of Contents

1. Executive Summary ..........................................................................................................1

1.1 Study Objectives.................................................................................................................................. 1

1.2 Approach to Estimating Energy Efficiency Potential..................................................................... 1

1.3 Energy Efficiency Technical Potential Results ................................................................................ 5

1.4 Energy Efficiency Economic Potential Results................................................................................ 7

1.5 Energy Efficiency Achievable Potential Results ............................................................................. 7

2. Energy Efficiency Potential Methodology .................................................................12

2.1 Overview of the Energy Efficiency Resource Assessment Model (EERAM)............................ 12

2.2 EERAM Model Features................................................................................................................... 12

2.3 Calculating Energy Efficiency Potential......................................................................................... 13

2.4 Measure Types Addressed............................................................................................................... 14

2.5 Financial Tests Calculated................................................................................................................ 15

2.6 Approach to Multi‐Life Benefits ..................................................................................................... 16

2.7 Codes and Standards Modified Baseline Effects on Cumulative Potential .............................. 17

2.8 Dual Baseline Measures ................................................................................................................... 18

2.9 Transitioning to Market Transformation....................................................................................... 18

2.10 Appliance Recycling ......................................................................................................................... 19

2.11 Behaviour Based Energy Savings Potential................................................................................... 19

3. Efficiency Measure Characterization..........................................................................21

3.1 Measure Selection and Inclusion .................................................................................................... 21

3.2 Base and Efficient Consumption, Effective Useful Life, and Incremental Measure Cost ....... 22

3.3 Loadshapes ........................................................................................................................................ 24

3.4 Net‐to‐Gross....................................................................................................................................... 25

3.5 Measure Densities ............................................................................................................................. 26

4. Conclusion........................................................................................................................27

Appendix A. Technical Potential ....................................................................................28

Appendix B. Economic Potential ....................................................................................29

Appendix C. Achievable Potential .................................................................................30

Appendix D. Glossary of Terms......................................................................................46

Appendix E. EERAM Economic Tests ...........................................................................49

Appendix F. 2012 Baseline Study ...................................................................................51
ii 

List of Figures

Figure 1. Diagram of Types of Energy Efficiency Potential................................................................................ 1

Figure 2. Total All Sectors Energy Efficiency Technical Potential – 2015 to 2040 (Gross at Generator) ....... 6

Figure 3. Total All Sectors Achievable Potential Incremental Energy Savings and Costs – 2015 to 2040

(Net‐at‐Generator).................................................................................................................................................... 8

Figure 4. Total All Sectors Achievable Potential Cumulative Energy Savings and Costs – 2015 to 2040

(Net‐at‐Generator).................................................................................................................................................. 10

Figure 5. Total All Sectors Achievable Potential – 2015 to 2040 (Net‐at‐Generator) – Cost per kWh

Savings – Base Scenario ......................................................................................................................................... 11

Figure 6. Total All Sectors Energy Efficiency Technical Potential – 2015 to 2040 (Gross at Generator) ..... 28

Figure 7. Total All Sectors Energy Efficiency Economic Potential – 2015 to 2040 (Gross at Generator)..... 29

Figure 8. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario........................................................................................................................................................... 30

Figure 9. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario........................................................................................................................................................... 31

Figure 10. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario............................................................................................................................................................ 32

Figure 11. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario.......................................................................................................................................................... 33

Figure 12. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario........................................................................................................................................................... 34

Figure 13. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario........................................................................................................................................................... 35

Figure 14. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario............................................................................................................................................................ 36

Figure 15. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario.......................................................................................................................................................... 37

Figure 16. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario........................................................................................................................................................... 38

Figure 17. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario........................................................................................................................................................... 39

Figure 18. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario............................................................................................................................................................ 40

Figure 19. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario.......................................................................................................................................................... 41

Figure 20. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario........................................................................................................................................................... 42

Figure 21. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario........................................................................................................................................................... 43

Figure 22. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario............................................................................................................................................................ 44

Figure 23. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario.......................................................................................................................................................... 45

Figure 24. Benefit/Cost Test Formulas................................................................................................................. 50
1 

1. Executive Summary

1.1 Study Objectives

The purpose of this study is to conduct a Demand Side Management (DSM) potential analysis of long‐

term electrical energy and system peak demand savings in Nova Scotia. On behalf of Efficiency Nova

Scotia Corporation (ENSC), Navigant conducted a potential analysis of electrical energy efficiency over

twenty‐six years (2015 to 2040). The study addresses the Technical, Economic and Achievable Potential

for Nova Scotia using Navigant’s Energy Efficiency Resource Assessment Model (EERAM).  

This report is structured as follows:       

 Section 1    Executive Summary  

 Section 2    Energy Efficiency Potential Methodology  

 Section 3    Efficiency Measure Characterization

 Section 4 Conclusion    

 Appendix A   Technical Potential

 Appendix B Economic Potential

 Appendix C Achievable Potential

 Appendix D Glossary of Terms

 Appendix E    EERAM Economic Tests

 Appendix F 2012 Nova Scotia Baseline Study

1. Approach to Estimating Energy Efficiency Potential

Figure 1. Diagram of Types of Energy Efficiency Potential

As shown in Figure 1, there are three categories of energy efficiency potential:

2. Technical Potential – The theoretical maximum amount of energy use that could be displaced

by the technology being evaluated, disregarding all non–engineering constraints.

Technical Potential

Economic Potential

Market

Potential

Technical Potential

Economic Potential

Achievable

Potential
2 

3. Economic Potential – The subset of technical potential that is economically cost–effective.

Estimates of economic potential do not address market barriers to implementation.

4. Achievable Potential – The potential that can realistically be achieved through implementation

of cost‐effective demand‐side management programs, taking into account real‐world barriers

and assuming the most aggressive program and policy scenario possible.  

Navigant undertook the energy efficiency potential study with the following key tasks (each of these

tasks is summarized below):

• Develop baseline consumption profiles and initial building simulation model

specifications.

• Characterize the energy efficiency measures.

• Conduct benefit‐cost analysis of energy efficiency measures.

• Estimate energy efficiency potentials.

1.1.1 Develop Baseline Consumption Profiles & Initial Building Simulation Model

Specifications

Navigant conducted this task by characterizing the Nova Scotia residential sector in terms of customer

numbers, as well as age and size of the household/housing stock, and the business, nonprofit and

institutional (BNI) sector in terms of BNI square footage. Residential, and Commercial and Industrial

sector electricity sales data was used as a starting point to determine customer energy use in broad end‐

use categories, such as lighting, space heating and domestic hot water. This data was supplemented by

data collected during an online panel survey of the Nova Scotia Residential sector and a phone survey of

the BNI sector, as well as onsite assessments of both sectors conducted in the last quarter of 2012 and the

first quarter of 2013. This information is used to estimate savings from energy efficiency investments.

Navigant used representative data for each sector to develop characteristic whole building energy

simulations, and inform inputs to the resource assessment model.

1.1.2 Characterize the Energy Efficiency Measures

Navigant developed representative DSM measures to be used as inputs to the ENSC DSM potential

analysis. Navigant first reviewed the measure level details used as inputs to the approved 2013‐2014

DSM Plan. As appropriate, Navigant revised measure assumptions, and received feedback from ENSC

staff on measure relevance. Navigant characterized the energy efficiency measures as follows:  

1. Estimate the baseline and new efficient measure energy consumption for each end‐use (heating,

lighting, domestic hot water, etc.) or unit energy consumption (UEC).

2. Estimate the incremental savings from each measure – improving from the baseline to the new

technology.

3. Determine the incremental costs and lifetimes for each of the new technologies.

4. Determine cost effectiveness of measures.

In addition, the baselines consider that different classes of buildings have different penetrations of

technologies, such as existing homes compared to new construction.  

Navigant characterized each resource by estimating the following parameters for each measure:
3 

 Measure Replacement Methodology: Each measure was defined as Retrofit/Early Retirement,

Replace on Burnout (ROB), Dual Baseline, or New Construction, to guide baseline definitions

and incremental cost calculations.  

 Measure Description: Technical definitions of the baseline (code or existing) and energy‐

efficient technology were provided, detailing characteristics such as capacity, efficiency, size,

and fuel type. Definitions of baseline technologies account for any anticipated changes to federal

or provincial codes.

 Savings Estimations: The annual energy consumption (kWh) and demand (kW) for the baseline,

code, and energy‐efficient condition of each technology was defined and used to calculate

annual savings. Effective useful life (EUL) for each measure was used to calculate lifetime

energy and demand savings as well. For certain BNI early replacement measures, remaining

useful life (RUL) was used to calculate the portion of savings before the measure would have

reached the end of its EUL. Additionally, the effects of future changes to codes and standards

were considered to develop a savings profile over time.

 Seasonal Distribution of Savings: Winter peak ratios were used to calculate avoided cost

benefits for each measure.

 Measure Costs: Incremental and total measure costs were calculated based on the measure

applicability and account for all material, labour, and operations and maintenance (O&M) costs

associated with the baseline, code, and/or energy efficient technology.

 Measure Densities: The density of each measure was determined for the baseline and energy

efficient conditions. Densities were defined as the number of each technology per home for

residential, and per 1000 sq. ft. for the business, nonprofit and institutional (BNI) sector.

Navigant characterized each measure using a variety of sources and methodologies. Existing measure

data for all technologies currently offered through ENSC’s DSM programs were used as appropriate.

Navigant reviewed all assumptions and supporting documentation to ensure measure parameters were

up‐to‐date and reflected the current state of the technology.

Climate‐Dependent Measures

For climate‐dependent measures, Navigant used a combination of building simulation modeling using

the eQuest model and engineering estimates to derive energy efficiency measure per unit savings.

Building prototypes were developed based on the information gathered during the survey of Nova

Scotia customer surveys and onsite assessments.  

Climate‐Independent Measures

For the climate‐independent measures, Navigant utilized various resources, including data from Natural

Resources Canada, the ENERGY STAR® Program, and manufacturer and national retailer data. Other

measures were analyzed using engineering principles, such as steady‐state heat loss, rated power, and

hours of operation. Spreadsheet models were the primary tool used to develop the energy savings

estimates for the climate‐independent measures.
4 

Measure Costs

Measure costs were based on the incremental equipment cost between the baseline and technologies for

replacement on burnout and new applications. Retrofit measure costs included the full material cost of

the energy‐efficient measure and associated labour for removal of existing equipment and installation of

the energy‐efficient technology. Cost sources included existing ENSC data, online market research, area

distributors, RS Means CostWorks©, etc.

In EERAM, measure technology costs are allowed to change over time and have technology cost vectors

similar to the measure efficiency time vectors. Based on a US Department of Energy paper1, technology

costs come down over time at a rate that varies by technology. Within EERAM, the measures are

mapped to a code that best matches the curves of technology cost reduction as identified in the DOE

paper. This code matches the appropriate learning curve to the technology, with several technologies

coded as having no change. Another variable estimates where the technology is on the learning curve in

terms of maturity.

Measure Lifetimes

For measure lifetimes, a combination of resources was used, including manufacturer data, standard

economic depreciation assumptions, NRCan, the ENERGY STAR® Website, industry trade organizations,

various Technical Reference Manual (TRM) reports, U.S. EPA, U.S. DOE, CBEEDAC, ACEEE, and

various studies reviewed for this project. As with measure costs, where possible measure lifetimes were

obtained from multiple sources and reconciled based on engineering judgment.

1.1.3 Conduct Benefit‐Cost Analysis of Energy Efficiency Measures

The energy efficiency measures were evaluated with respect to each of the four main standard cost tests,

with the total resource benefit‐cost tests used to determine cost‐effectiveness.  

1. Participant test: measures are cost effective from this perspective if the reduced electric costs to

the participating customer from the measure exceed the after‐incentive cost of the measure to the

customer.

2. Program administrator cost (PAC) test: measures are cost effective from this perspective if the

sum of their avoided costs (costs avoided by the measure’s energy and demand savings) and

other benefits for the utility are greater than the program administrator’s costs to support the

measure, including administrative costs and customer incentives.  

3. Ratepayer impact measure (RIM) test: measures are cost effective from this perspective if their

avoided costs are greater than the sum of the program administrators’ costs and the “lost

revenues” caused by the measure.

4. Total resource cost (TRC) test: measures are cost effective from this perspective if the sum of

their avoided costs and other benefits for participants or the utility are greater than the sum of

the measure costs and the program administrator’s administrative costs.

                                                          

1 U.S. Department of Energy (2011). Using the Experience Curve Approach for Appliance Price Forecasting.

Supplemental draft paper to the DOE proposed rule in Docket No. EE‐2008‐BT‐STD‐0012.
5 

1.1.4 Estimate Energy Efficiency Potentials

Navigant developed estimates of energy efficiency measure potentials in terms of Technical, Economic,

and “Achievable” Potential. Note that the Energy Efficiency Achievable Potentials presented in this

report are those that are realistic for Efficiency Nova Scotia Corporation to achieve through its

implementation of cost‐effective demand‐side management programs and do not include savings

associated with free‐riders or savings from activities outside of DSM programs (such as energy savings

resulting from codes and standards or efficiency initiatives of non‐participants or electric utilities).

Economic Potential was estimated using the TRC test as the economic screen applied to Technical

Potential to determine cost‐effectiveness. Alternatively, the EERAM tool allows for the PAC test to be

used as the economic screen.       

To estimate Achievable Potential, the EERAM model was used to estimate conversion rates from

inefficient products to more efficient products for retrofit, replacement and dual baseline measures, as

well as installation rates in new buildings for new construction markets. Navigant used a dual baseline

approach to calculate technical potential where appropriate for early replacement measures. ENSC has

adopted the dual baseline approach for certain measures. A dual baseline approach to calculating energy

savings involves the following: in the year an energy‐efficient measure is installed, if the replaced

measure is still in working order, its remaining useful life (RUL) must be calculated based on its effective

useful life (EUL) and the length of time it has been in use prior to replacement. Once calculated, energy

savings are impacted in the following ways:

• “Enhanced” or full savings (the difference between existing energy consumption and the

energy‐efficient measure consumption) are calculated for the RUL period.

• Regular savings (the difference between the standard new baseline consumption and the energy‐

efficient measure consumption) are calculated for the rest of the measure life period.

A dual baseline approach for calculating costs involves the following:

• The full cost for the energy efficient equipment is calculated.

• The full cost for the standard practice baseline equipment that would have been purchased at

the end of the RUL period is calculated.

• Final costs are the difference between the present value of the baseline costs and the energy‐

efficient costs at the time of early replacement of the existing technology.

1.2 Energy Efficiency Technical Potential Results

Figure 2 summarizes the Total Technical Potential results of the analysis conducted, 2015 through 2040

for the Residential, Commercial and Industrial sectors. The total (Gross at Generator) energy efficiency

Technical Potential savings, including energy savings resulting from codes and standards, 2015 through

2040, is estimated to be approximately 7,820 GWh, about 56% of forecast sales without DSM, and

approximately 1,556 MW, about 61% of peak winter demand. Appendix A presents the 2015 – 2040

Technical Potential with the breakdown of savings from DSM programs and savings from Codes and

Standards.  
6 

Figure 2. Total All Sectors Energy Efficiency Technical Potential – 2015 to 2040 (Gross at Generator)

Total All Sectors

Technical Potential

(Gross at Generator)

Year Peak

Demand

Savings

(MW)

Energy

Savings

(GWh)

2015 1,150 5,332

2016 1,168 5,434

2017 1,190 5,554

2018 1,208 5,654

2019 1,227 5,757

2020 1,246 5,859

2021 1,265 5,961

2022 1,284 6,047

2023 1,300 6,120

2024 1,391 6,719

2025 1,406 6,797

2026 1,418 6,866

2027 1,430 6,935

2028 1,440 6,999

2029 1,452 7,068

2030 1,463 7,133

2031 1,472 7,195

2032 1,481 7,257

2033 1,491 7,320

2034 1,500 7,388

2035 1,510 7,460

2036 1,520 7,533

2037 1,529 7,608

2038 1,538 7,676

2039 1,547 7,747

2040 1,556 7,820
7 

1.3 Energy Efficiency Economic Potential Results

Appendix B presents the total Economic Potential results of the analysis, 2015 through 2040, across all

sectors; Residential, Commercial and Industrial. The total (Gross at Generator) energy efficiency

economic potential savings, including energy savings resulting from codes and standards, 2015 through

2040, is estimated to be approximately 6,354 GWh, about 46% of forecast sales without DSM, and

approximately 1,334 MW, about 52% of peak winter demand.  

The economic potential is a high percentage of the technical potential due to two factors. First, the

analysis includes energy efficiency measures that a program administrator would consider including in

a DSM portfolio, which tends to create a measure dataset that is already nearly economically feasible.

Second, while the program portfolio overall passes the TRC test, the economic screen for individual

measures is set to 0.75 to allow for a mix of measures to be included in programs that are cost effective.          

1.4 Energy Efficiency Achievable Potential Results

The total Achievable Potential savings (Net at Generator) from energy efficiency programs, 2015 through

2040, are presented in Appendix C. Four scenarios of Achievable Potential have been developed and are

labeled as Low, Base, Mid, and High and each are presented in terms “Total All Sectors” followed by a

breakdown for each of the three sectors – Residential, Commercial and Industrial.  

1.4.1 Four Scenarios of Achievable Potential

Four achievable potential scenarios were created. Each scenario is based on modifying the incentive

level. Each measure has an estimated consumer payback elasticity based on calibration year estimates of

measure installations and the incentive provided. Changes to the incentive level affects the consumer

payback ratio and the resulting estimates of program participation based on measure consumer payback

elasticity. Additionally, other factors affecting program participation as the incentive level changes

include:

• Administrative costs are higher for lower incentives and conversely lower for higher incentives.

Changes in administrative costs affect the consumer payback.

• Consumer awareness is higher when incentives are higher and conversely lower when

incentives are lower than the base scenario. Consumer awareness affects the population of

consumers who could participate in the program.

• Net‐to‐Gross ratio. This ratio becomes closer to 1.0 when there is a higher incentive and

conversely lower when the incentive is lower than the base scenario.

The four scenarios are:

• Base Scenario: incentive in the calibration year is set to what was offered in that calibration year.

• Low Scenario: incentive is set to 1⁄2 of the calibration year incentive.

• Mid Scenario: incentive is set to 1.5 times the calibration year incentive (up to a maximum of

100% of incremental cost)

• High Scenario: incentive is set to twice the calibration year incentive (up to a maximum of 100%

of incremental cost)
8 

1.4.2 Incremental Energy Savings and Costs of Achievable Potential Scenarios

Figure 3 presents a summary of the four Achievable Potential Scenarios in terms of “Incremental Energy

Savings (GWh)” (Net‐at‐Generator) from energy efficiency programs, 2015 through 2040, along with

“Incremental Program Administrator Cost ($ millions)”.  

Figure 3. Total All Sectors Achievable Potential Incremental Energy Savings and Costs – 2015 to 2040

(Net‐at‐Generator)

Total All Sectors Incremental Achievable Potential (Net‐at‐Generator)

Energy Savings (GWh) Program Administrator Costs ($ millions)

Scenario   Base     Low Mid High     Base Low Mid High

2015 137.8 107.7 164.0 174.9 $50.7 $46.5 $56.1 $76.3

2016 139.9 106.0 171.0 191.3 $50.5 $44.8 $57.5 $92.0

2017 141.7 104.0 177.7 205.5 $50.0 $42.9 $59.0 $104.6

2018 136.2 102.3 170.0 193.9 $52.4 $39.2 $66.1 $107.4

2019 134.8 100.0 167.4 186.9 $57.0 $37.6 $76.4 $112.8

2020 133.9 98.7 166.2 182.9 $61.5 $37.8 $86.7 $119.3

2021 130.1 98.1 162.2 177.7 $56.9 $38.5 $81.7 $106.9

2022 127.6 98.7 159.6 175.6 $54.1 $39.8 $79.1 $102.2

2023 126.6 99.5 158.8 176.9 $51.5 $39.7 $76.3 $99.0

2024 127.4 101.1 161.6 184.7 $50.8 $40.4 $76.8 $104.0

2025 130.1 103.2 167.4 196.0 $50.6 $40.8 $78.5 $110.9

2026 135.6 106.8 176.6 209.2 $52.1 $42.0 $81.9 $118.6

2027 143.5 111.8 187.5 217.7 $54.8 $43.7 $85.7 $121.1

2028 153.4 118.5 199.1 220.6 $58.5 $46.1 $90.1 $120.4

2029 163.3 126.5 207.8 216.5 $60.7 $47.0 $92.0 $114.5

2030 170.7 134.2 210.6 207.1 $63.1 $49.5 $92.2 $106.5

2031 173.5 139.5 206.8 195.4 $62.6 $50.8 $87.4 $94.6

2032 171.4 141.1 198.5 183.8 $61.4 $51.1 $82.3 $85.9

2033 166.0 139.2 188.4 173.7 $59.3 $50.4 $76.8 $65.8

2034 159.3 135.2 178.4 164.7 $56.7 $48.9 $71.2 $61.1

2035 153.0 130.5 169.5 157.0 $47.7 $47.2 $57.9 $56.6

2036 147.0 125.9 161.4 150.4 $46.5 $45.6 $54.8 $52.4

2037 141.5 121.6 154.2 145.1 $45.4 $44.0 $52.0 $49.6

2038 136.1 117.8 147.8 141.3 $44.4 $42.5 $49.0 $44.2

2039 131.6 114.3 142.6 137.5 $43.5 $41.3 $46.5 $43.4

2040 127.9 111.2 139.0 134.0 $42.3 $40.2 $43.6 $42.6
9 

1.4.3 Cumulative Savings and Costs of Achievable Potential Scenarios

Figure 4 presents the “Cumulative Energy Savings (GWh)” (Net‐at‐Generator) from energy efficiency

programs, 2015 through 2040, along with the “Cumulative Program Administrator Cost ($ millions)” for

each of the four Achievable Potential Scenarios. Cumulative savings take into account the overall savings

occurring in a given year from both new participants and savings continuing to result from past

participation with measures that are still in place. Cumulative savings do not always equal the sum of all

prior year incremental values as some measures have relatively short measure lives and, as a result, their

savings drop off over time.
10 

Figure 4. Total All Sectors Achievable Potential Cumulative Energy Savings and Costs – 2015 to 2040

(Net‐at‐Generator)

Total All Sectors Cumulative Achievable Potential (Net‐at‐Generator)

Energy Savings (GWh) Program Administrator Costs ($ millions)

Scenario   Base     Low Mid High     Base Low Mid High

2015 137.8 107.7 164.0 174.9 $50.7 $46.5 $56.1 $76.3

2016 226.1 162.2 283.4 314.6 $101.2 $91.3 $113.6 $168.3

2017 360.8 259.1 454.2 513.1 $151.2 $134.3 $172.6 $272.9

2018 480.3 345.9 606.0 687.3 $203.6 $173.5 $238.6 $380.3

2019 597.4 431.1 752.5 849.1 $260.6 $211.1 $315.0 $493.1

2020 708.9 508.5 897.5 1,012.2 $322.1 $248.8 $401.7 $612.4

2021 817.1 582.0 1,038.8 1,170.2 $379.0 $287.3 $483.4 $719.3

2022 932.3 670.4 1,186.0 1,334.1 $433.2 $327.1 $562.5 $821.5

2023 1,040.9 754.2 1,325.9 1,492.3 $484.7 $366.8 $638.7 $920.5

2024 1,155.4 841.0 1,474.8 1,664.0 $535.5 $407.1 $715.6 $1,024.5

2025 1,257.1 922.0 1,603.8 1,820.5 $586.1 $447.9 $794.1 $1,135.4

2026 1,371.0 1,005.3 1,758.8 2,006.7 $638.3 $489.9 $876.0 $1,254.0

2027 1,509.5 1,111.5 1,940.7 2,217.3 $693.0 $533.5 $961.7 $1,375.1

2028 1,644.6 1,211.0 2,121.0 2,418.5 $751.5 $579.6 $1,051.8 $1,495.5

2029 1,797.6 1,326.9 2,318.3 2,624.4 $812.2 $626.6 $1,143.9 $1,609.9

2030 1,956.3 1,455.8 2,511.5 2,813.4 $875.3 $676.2 $1,236.1 $1,716.4

2031 2,112.9 1,579.0 2,700.6 2,991.8 $937.9 $726.9 $1,323.5 $1,811.0

2032 2,266.2 1,702.6 2,880.9 3,158.6 $999.4 $778.0 $1,405.8 $1,896.9

2033 2,415.4 1,822.7 3,055.0 3,320.5 $1,058.7 $828.4 $1,482.6 $1,962.8

2034 2,560.6 1,941.9 3,220.7 3,473.0 $1,115.4 $877.3 $1,553.8 $2,023.8

2035 2,699.4 2,057.0 3,376.3 3,614.3 $1,163.1 $924.5 $1,611.7 $2,080.4

2036 2,833.1 2,169.1 3,523.9 3,749.5 $1,209.6 $970.1 $1,666.5 $2,132.9

2037 2,955.0 2,271.4 3,657.9 3,874.4 $1,254.9 $1,014.1 $1,718.5 $2,182.5

2038 3,080.8 2,379.5 3,794.4 4,005.0 $1,299.3 $1,056.7 $1,767.5 $2,226.7

2039 3,197.5 2,479.8 3,921.4 4,128.6 $1,342.8 $1,098.0 $1,814.1 $2,270.1

2040 3,308.7 2,575.6 4,039.3 4,241.0 $1,385.0 $1,138.1 $1,857.7 $2,312.6

1.4.4 Unit Costs of Achievable Potential Scenarios

Figure 5 presents the Unit Costs for each of the Achievable Potential Scenarios expressed as the

Cumulative Program Administrator Cost ($ millions) divided by the Cumulative Energy Savings (GWh).

Over the 2015– 2040 analysis period, the Base Scenario results in the lowest unit cost ($0.419/kWh),
11 

followed by the Low Scenario ($0.442.kWh) and then the Mid Scenario ($0.460/kWh). The High Scenario

has the highest unit cost ($0.545/kWh).     

Figure 5. Total All Sectors Achievable Potential – 2015 to 2040 (Net‐at‐Generator) – Cost per kWh

Savings – Base Scenario

Total All Sectors Achievable Potential (Net‐at‐Generator) – Cost per kWh Savings – Base Scenario

Base Scenario Low Scenario Mid Scenario High Scenario

Program

Administrator

Costs  

($ million)  

[a]

Cumulative

Energy

Savings

(GWh)  

[b]  

Program

Administrator

Costs  

($ million)

[a]

Cumulative

Energy

Savings

(GWh)

[b]  

Program

Administrator

Costs  

($ million)

[a]

Cumulative

Energy

Savings

(GWh)  

[b]

Program

Administra

tor Costs  

($ million)

[a]

Cumulative

Energy

Savings

(GWh)  

[b]

2015

to

2040

$1,385.0 3,308.7 $1,138.1 2,575.6 $1,857.7 4,039.3 $2,312.6 4,241.0

Cost per

kWh

Savings

= [a] / [b]  

$0.419 $0.442 $0.460 $0.545
12 

2. Energy Efficiency Potential Methodology

2.1 Overview of the Energy Efficiency Resource Assessment Model (EERAM)

The Energy Efficiency Resource Assessment Model (EERAM) is an energy efficiency potential model

designed to estimate technical, economic, and achievable energy efficiency potential for a program

administrator’s service area. Developed by Navigant, the model forecasts energy savings and demand

reduction potential within the residential, commercial, and industrial sectors over a forecast period of

typically 20 – 25 years.  

EERAM is an Excel spreadsheet model based on the integration of energy efficiency measure impacts

and costs, customer characteristics, utility load forecasts, and utility avoided costs and rate schedules.

Excel is used as the modeling platform to provide transparency to the estimation process. Using Excel

also allows the model to be customized and to accommodate either detailed or more general model

input data.

The model utilizes a bottom‐up approach, using the starting points of study area building stocks and

equipment saturation estimates, forecasts of building stock decay and new construction, energy

efficiency technology data, past energy efficiency program accomplishments, and decision‐maker

variables that help drive the market scenarios.

For established energy efficiency measures, EERAM calculates achievable potential based on a decision

maker adoption rate algorithm. This algorithm is primarily a measure by measure elasticity response to

measure payback. However, a diffusion curve methodology is used for emerging technologies.

Emerging technologies are considered to follow a Bass diffusion curve methodology rather than a

measure payback methodology. The Bass diffusion model was developed by Frank Bass and describes

the process of how new products are adopted as an interaction between users and potential users.

2.2 EERAM Model Features

The EERAM model incorporates a number of innovative features, including:  

• Utilization and wherever possible, direct linkage to province‐wide or program administrator

specific deemed, per unit measure energy savings and cost databases.  

• Utilization of program administrator‐specific or, if not available, regionally‐specific building

characteristics information. These data often come from on‐site or telephone‐based building

surveys of building and appliance stocks. The EERAM model is able to include as many

different building segments and industrial classifications as there is data available.  

• Utilization of decision‐maker awareness of measures and if aware, the willingness to purchase

the energy efficiency measure variables. These variables are utilized in the consumer choice

algorithm. This data is most often collected at the same time as the building and appliance stock

characteristics are gathered through a special decision maker survey.  

• Utilization of historical program administrator‐specific energy efficiency program achievements

to calibrate model results.  

• The ability to create forecasts utilizing either the historical achievement data for calibration or

utilizing percent of sales by sector as the first year calibration method.  
13 

• The ability to quickly create scenarios based on different measure incentive levels. Other

scenarios can also be developed using alternative input data, such as avoided costs, energy

forecasts, building stock forecasts, and others that may be of interest to the program

administrators.  

• The ability to include a number of different avoided costs streams pegged to specific end‐uses.  

• Calculation of the basic economic tests (described later), but also calculates the Total Resource

Cost (TRC) test for the base year and for each year in the forecast. The TRC value is used to

screen what is included in the achievable potential estimates, and as technology costs and

impacts change over time, so do the TRC values.  

o The TRC screening value is a variable set by the user.  

o For emerging technologies, the TRC screen is allowed to be lower, if desired by the user.  

• In addition to the TRC values changing over time, the model incorporates time vectors for other

key variables as well including:

o Technology cost

o Administrative cost

o Technology impact

o Consumer awareness and willingness

o Avoided costs  

• Impacts of known codes and standards and the timing of those impacts are included in the

EERAM modeling structure. These impacts are estimated at the measure level.

• The ability to include the interactive effects on other fuel usage (such as natural gas) when

electric measures are implemented.  

 Recognition that at the end of measure life, actions may take place that affect both cumulative

potential and program participation.  

o The model has a variable called measure re‐engagement, which is a percentage estimate

of those who continue with the same or more efficient version of the initially‐

implemented measure. The remainder is assumed to fall back to the baseline efficiency.

The model adjusts cumulative potential to account for those who fall back to the

baseline efficiency.  

o The model has another variable called re‐participants. This is an estimated percentage of

the measure re‐engagers who also again participate in the program administrator’s

program and receive an incentive. For these customers, their energy savings is assumed

to continue, but is not counted as new incremental savings. Although there is no new

incremental savings, there is a cost associated with maintaining these savings. The

additional incentive and administrative costs are added to the program costs.

 Mutually‐exclusive measures are placed into competition groups. Within the competition

groups, the mutually‐exclusive measures share the same base population but each measure uses

its own unique decision‐maker adoption rate algorithm to estimate year by year market

potential. The base population is reduced each year by the sum of the mutually‐exclusive

measures participation.  

2.3 Calculating Energy Efficiency Potential  

The model partitions its evaluation of each measure into technical, economic and achievable potential.

Each assessment includes building stock estimates, technology densities, and measure impacts, each

using a different algorithm.
14 

Technical potential is calculated using the product of a measure‘s savings per unit, the quantity of

applicable units in each facility, and the number of facilities in a program administrator’s area. The

assessment includes measures that might not be cost‐effective or have the backing of a strong consumer

market. By disregarding these factors, the technical potential assessment provides an upper bound of

efficiency potential regardless of cost or market penetration. For measures considered to replace

inefficient measures on burnout (ROB), the quantity of applicable units per year is limited to the number

that need to be replaced, which is determined by measure life. As time passes, this potential population

grows until meeting the full measure life. For other, non‐replace‐on‐burnout measures, the full

populations of baseline units are considered available. No net‐to‐gross adjustments occur with technical

potential.

Economic potential estimates the amount of technical potential that is cost‐effective, as defined in this

case by the results of the Total Resource Cost (TRC) test. The TRC test is a cost‐benefit analysis of

relevant energy efficiency measures, excluding market barriers, such as lack of consumer knowledge.

Benefits are meant to include the avoided costs of generation, transmission and distribution investments,

avoided fuel costs, and other benefits that may accrue to participants and/or to the utility. For purposes

of this study, only avoided generation investments and fuel costs were considered. Costs are meant to –

and for this study did – include incremental measure costs and program administration costs. The TRC

screening value for emerging technologies can be set lower than other technologies if the user wishes it

to be. Replacement on burnout measures are treated the same as with technical potential and there are

no net‐to‐gross adjustments.

Achievable potential is the third of EERAM‘s energy efficiency algorithms, calculating the amount of

economic energy efficiency potential that could be captured by program administrator programs over

the forecast period. This calculation varies with the program‘s parameters, such as the program design

or magnitude of incentives or rebates for customer installations.  

2.4 Measure Types Addressed

EERAM recognizes seven measure types, including:  

 Replacement on Burnout (ROB): An energy efficiency measure is installed after the existing

equipment fails.  

 Early Retirement: An energy efficiency measure normally regarded as ROB is installed before

the effective life of the measure it is replacing is reached.  

 Retrofit: An energy efficiency measure that can be implemented immediately. The lifetime of the

base technology is not a factor as retrofit measures generally do not replace existing

technologies, but rather improve the efficiency of existing technologies. The energy impact is

therefore the amount of that improvement.

 Dual Baseline: A dual baseline measure uses a less efficient baseline in the first part of its

measure life based on the remaining useful life (RUL), resulting in higher energy savings

initially, with savings becoming lower at a point later in the measure life when the measure

effective useful life (EUL) would have resulted in the measure reaching the end of its useful life.

At the end of what would have been the end of the measure’s EUL, measure costs also are

adjusted.  
15 

 Emerging Technology: An energy efficiency measure is just entering or about to enter the

marketplace. Achievable potential is calculated differently for emerging technologies and uses a

Bass diffusion model rather than the traditional measure payback.  

 Behavioural Programs: These are programs that are designed to influence consumer behaviour

through the provision of training and/or information. As with emerging technologies, achievable

potential is calculated using a Bass diffusion model rather than the traditional measure payback.  

 New Construction: A measure or package of measures is installed at the time of construction.

2.5 Financial Tests Calculated

EERAM also calculates several financial tests2, including:  

 Total Resource Cost (TRC): This test includes all quantifiable costs and benefits of an energy

efficiency measure that may accrue to participants or the utility. For example, a measure passing

the TRC test is cost effective from this perspective if the sum of its avoided costs and other

benefits accruing to participants or the utility are greater than the sum of the measure costs and

the program administrator’s administrative costs.

 Program Administrator Cost Test (PAC): This test measures the net costs of an energy efficiency

program based on the costs incurred by the program administrator (including incentive costs)

and excluding any net costs incurred by the participant. For example, a measure passing the

PAC test is cost effective from this perspective if the sum of the avoided costs (costs avoided by

the measure’s energy and demand savings) and other utility benefits are greater than the

program administrator’s costs to promote the measure, including incentives provided to

customers.

 Ratepayer Impact Measure Test (RIM): This test measures what happens to a dwelling or

business’ electric bills or rates due to changes in utility revenue and operating costs caused by

the program. For example, a measure passing the RIM test is cost effective from this perspective

if its avoided costs are greater than the sum of the program administrator’s costs and the “lost

revenues” caused by the measure.

 Participant Cost Test (PCT): This test measures the quantifiable benefits and costs to the

customer due to participation in the program. For example, a measure passing the PCT test is

cost effective from this perspective if the reduced electric costs to the participating customer

from the measure exceed the after‐incentive cost of the measure to the customer.

 Simple Customer Payback: This measurement calculates the program payback by taking the

measure cost less the incentive received and divides it by first year energy bill savings.  

 Levelized Measure Cost/kWh: This measure multiplies the energy efficiency measure costs by

the Capital Recovery Factor, and divides by the first year kWh savings.

Outputs from the model are designed to accomplish multiple objectives, including:  

 Determining the total cost‐effective energy savings available over the forecast period, both

annually and cumulatively. This is determined for 100% of electric utility energy use in a service

territory. These estimates are provided at the sector, program type, and end‐use classification

levels.  

                                                          

2 Appendix E provides further details of each economic test.
16 

 Providing guidance for the program administrator’s energy efficiency goals at an aggregate

level, as well as at the measure category level, where appropriate. As discussed, the EERAM

calculations are calibrated to past program administrator achievement levels to ensure

continuity with past program achievements.

2.6 Approach to Multi‐Life Benefits

The EERAM model is built to recognize that the impacts of most DSM measures extend beyond the

initial estimate of measure life. Taking this reality into account can affect benefit/cost ratios, such as the

TRC, PCT, PAC, and RIM, by incorporating future expectations of avoided costs, as well as changes in

measure costs and impacts, and cumulative energy and demand impact estimates. The estimation of

multi‐life benefits and costs begins with two variables:

 Measure Re‐Engagement. A variable that estimates the share of measure installations that

continue to provide efficiency benefits at least equal to the initial DSM measure installed. The

complimentary share of installations not part of re‐engagement is returned to the population

totals of available stock for program participation.

 Portion of Re‐Engagement Receiving an Incentive (Re‐Participation). A variable that

represents the percent of re‐engagers that also re‐participate in the DSM program, if it is still

offered. This recognizes that from a program administrator’s perspective, the program

administrator will incur some amount of incentive and administrative cost from re‐participants.

With re‐participants, the program administrator continues to maintain the original savings but

does not accrue additional incremental savings.  

Estimating measure re‐engagement and re‐participation is a two‐step function used both to identify

what share of initial participants continue saving energy by installing a new measure similar to the

original measure and second, what portion of these re‐engagers again partake in a DSM program. For

example, if the re‐engaging share is 85% (which is what the variable uses for the current default value),

then 15% are thought of as returning to the baseline population. As this 15% are once again members of

the baseline population, they can now participate in any program that affects this baseline.

There are no incremental savings accruing from this re‐engaging population. However, cumulative

savings must be adjusted in two ways. First, the 15% that go back to the baseline population needs to

have their savings removed from cumulative savings. Second, for the 85%, adjustment to cumulative

potential is dependent upon whether the savings are different from what was achieved at the time of the

original participation. If unchanged, no changes to cumulative potential are made. If savings are

different, then the cumulative potential is adjusted by this delta difference.

At the point in time of re‐engagement, factors may exist that affect the estimate of continuing DSM

measure saving and costs.

 A code or standard (C&S) may have come into effect since the initial point of participation. The

effects of the C&S become an attribution issue. Since C&S are mandatory, savings affected by

C&S are attributed to the C&S. The share of savings may be 100% or may be a share lower than

100%. If 100%, then no further savings or costs are attributed to the DSM program measure. If

the attribution is less than 100%, then the attribution share still applicable to the program

administrator is accounted for.
17 

 A measure’s estimated energy savings may increase or decrease in the future. For example, LED

lighting is still improving in efficacy and, as it does, savings per measure increase. In contrast,

appliance recycling programs, such as refrigerator recycling, are expected to have lower savings

per unit over time as the population of refrigerators becomes a more recent (more efficient)

vintage each passing year.

 A measure’s estimated cost may increase or decrease in the future. For example, LEDs and other,

newer technologies are expected to decline in cost as these become more popular in the

marketplace. The declining cost of CFLs over the past decade is an example of such an effect.

Any changes in energy savings at the point of re‐engagement are calculated. These changes in energy

savings are applied to the Cumulative Potential and do not affect the Achievable Incremental Potential.

Avoided costs of energy, capacity, and any externality that is quantified are all calculated per measure

unit. The impacts are separated into “first life” avoided costs and “beyond first life” avoided costs. The

“first life” values are the present value of avoided costs using measure life, the discount rate, and the

utility’s avoided cost stream. The “beyond first life” estimate of avoided cost is calculated only for the

population of “re‐engagers”. For this population, the present value of future avoided costs beyond the

first life3 is calculated starting at this future point in time. This calculation uses any revised future

estimate of measure impact, along with measure life, the discount rate, and the avoided cost stream

appropriate for that time frame.

In a similar manner, future costs for incremental technology, incentives, and administrative costs are

calculated. Each of these three costs are for re‐engagers only, with estimates of future incentive and

administrative costs for the portion of re‐engagers that are re‐participants in the DSM program. For each

of these costs, the appropriate future technology, incentive, and administrative costs are utilized in the

present value calculation.

2.7 Codes and Standards Modified Baseline Effects on Cumulative Potential

The effects of codes and standards within the EERAM model are viewed as an attribution issue between

what is credited to codes and standards and what is credited to the DSM program. The “Code Based

Impact Change” identifies the specific codes and standards expected to affect measure savings over the

forecast period. The effects to measure savings are in the form of time vectors where a specific code or

standard is associated with the measures it is expected to affect. The measure effect is in the form of a

percent change in savings starting at the point when the code or standard goes into force. As an example,

if a specific code or standard effectively reduces savings by 50% starting in the year 2015, the DSM

measure’s first year incremental measure impact would be 100% of the estimated measure impact up to

the year 2015. Starting in 2015 and thereafter, the program administrator’s share of the measure savings

is reduced by 50% with the other 50% being attributed to the code or standard.

Treating the savings achieved by the DSM program after a code or standard goes into force is done in

two parts. First addressed, at the moment the code or standard goes into force, is whether there are any

adjustments to the first measure lifetime savings, costs, and benefits. The EERAM model treats these

already existing program achievements, benefits, and costs as unchanged (maintained) over the

remaining first life of the measure.  

                                                          

3 Several lifetimes may be calculated, depending on the measure’s estimated life and the length of the forecast

period.
18 

However, at the time of re‐engagement, adjustments do occur. For those measures assumed to re‐engage

after the first lifetime is complete, the measure impacts, benefits, and costs are calculated based on the

code or standard adjusted savings level. It is assumed that attribution of the savings transfers to the code

or standard at this point forward. To accommodate this, the Cumulative Potential is adjusted downward

to reflect the lowered savings resulting from the impact of the code or standard at the time of re‐

engagement. Additionally, post first lifetime benefits and costs are calculated to reflect the lower

savings.

2.8 Dual Baseline Measures

Certain DSM measures are candidates for an early replacement program that utilize a dual baseline. A

dual baseline measure uses a less efficient baseline in the first part of its measure life, resulting in higher

energy savings initially, with savings becoming lower at a point later in the measure life. Measure costs

also are adjusted. Such measures include those with long measure lifetimes that have recent C&S

impacts affecting their baseline efficiency at the time of measure burnout and replacement (ROB).

Examples of these measures include early replacements of long‐lived appliances, such as T‐12 lighting

fixtures. The existing baseline stock is generally significantly less efficient than the C&S baseline

technology.  

It is in the program administrator’s interest to replace existing stock as quickly as possible, which it does

through early replacement programs. Navigant will use an example of a fluorescent light fixture

efficiency measure with a 20 year measure life in an early replacement program. The first part of the

measure life covers the remaining life of the measure being replaced (T12 light fixtures), which in this

example is 5 years or 25 percent. Using existing stock as the efficiency baseline, the measure has a

savings of 100 kWh/year per unit for the first 15 years when replaced one‐for‐one with a high

performance T8 (HPT8) fluorescent fixture. The standard practice baseline (a standard T8 fixture)

provides savings of 15 kWh/year per unit when replaced with a HPT8 fixture. This 15 kWh/year savings

value is applied to years 16 through 20 of the measure life, the 25 percent mentioned above.  

The incremental first year measure impacts calculated in the Market Incremental Potential utilizes the

100 kWh/unit annual savings estimate. However, the cumulative savings must be adjusted after the first

fifteen years to reflect the lower savings for the remaining 15 years (years 16 to 20). This estimate of

lowered savings is used in the Cumulative Potential to adjust the cumulative savings downward.

Another modeling element affected by dual baseline measures is avoided costs over the first measure life

and subsequent periods of re‐engagement. During the first measure life, the calculation of avoided costs

are split between the first 15 years and the last 5 years and then added together. At the point of re‐

engagement, it is appropriate to utilize only the standard practice baseline energy savings as represented

by the last 5 years of the first measure life. These standard practice baseline energy savings affect the

entire 20 years of the efficient measure life.  

The final modeling element affected is incremental measure cost. With the initial lifetime, the

incremental measure cost includes any labour costs along with full measure costs. However, at the point

of re‐engagement, only incremental measure costs are incurred.

2.9 Transitioning to Market Transformation

EERAM recognizes that a program administrator‐sponsored DSM program measure reaches a point

where it can be considered part of a transformed market. This market transformation point is estimated
19 

when a certain saturation point is reached. This saturation point is an input variable expressed as a

saturation percentage. The model also assumes that when market transformation is reached, market

penetration increases. The reason for increasing market penetration is because under Market

Transformation, the entire market place is affected and not just a population affected by a DSM program.

At the point when market transformation is reached, it is assumed that the measure is no longer

receiving an incentive or incurring any additional program administration costs. However, the program

still claims the benefits of the measure’s avoided costs. Adjustments to the incentives and costs after the

Market Transformation Point (MTP) when market transformation is reached are calculated. The results

of the calculations are used to adjust the administrative and incentive costs.

Market penetration is increased (with corresponding energy savings), and incentive and administrative

costs are removed after the point of Market Transformation. This results in market potential increasing

from the Market Transformation year forward (limited by available base population). However, within

the benefit/cost tests, the program administrator’s benefits (in terms of avoided costs) are limited to the

portion of the future estimated measure impacts if no market transformation is assumed to occur. The

cost portions of the benefit/cost tests improve because of the assumption of no further administrative or

incentive costs being incurred.

2.10 Appliance Recycling

Appliance recycling measures need special treatment because of the unique characteristics of the base

population. Unlike other base technologies, the used appliance stock available for recycling is constantly

being refreshed with new populations of appliances. Due to past improvements to appliance efficiencies

(primarily codes and standards), the constantly refreshing population of available appliances for

recycling is more efficient (and thus saves less energy) from year to year. Available populations of

appliances for recycling do not change significantly from year to year, but the time vector of savings per

unit does decline.

2.11 Behaviour Based Energy Savings Potential

Savings potential from behaviour‐based initiatives was included in the EERAM model. For the purposes

of this study, Navigant defines behaviour‐based initiatives as those providing information about energy

use and efficiency actions, rather than financial incentives, equipment, or services. These initiatives use a

variety of implementation strategies including mass media marketing, community‐based social

marketing, competitions, training, and feedback.4  

Outcomes from behaviour‐based initiatives that result in energy savings can be broadly characterized as

equipment‐based and usage‐based:  

 Equipment‐based behaviour – Savings from the purchase and installation of higher efficiency

equipment, relative to baseline conditions.5 Examples of equipment‐based behaviour include the

replacement of lights with higher efficiency lights, purchasing Energy Star®‐qualified appliances,

and purchasing premium efficiency motors. In the EERAM Model, these savings are modeled at

                                                          

4 Evaluation of Consumer Behavioural Research, Navigant (Summit Blue Consulting) for the Northwest Energy

Efficiency Alliance, April 6, 2010, Page 4.  

5 This could be either the early retirement of older equipment or the installation of high‐efficiency equipment at the

natural time of installation or replacement.
20 

the equipment level as contributions to the percentages of the population that are aware of the

measure and that are willing to adopt this measure. Equipment‐based behaviour can be sub‐

categorized as:

o Non‐incented equipment‐based behaviour – The purchase of higher efficiency

equipment for which no incentives are provided.

o Incented equipment‐based behaviour– The purchase of higher efficiency equipment for

which incentives are provided. Also known as channeling.

 Usage‐based behaviour – Savings from changes in usage and maintenance of existing

equipment. Examples of usage‐based behaviour include turning off lights, unplugging

electronics and chargers, programming thermostats, and improving the efficiency of equipment

through modified maintenance practices. In the EERAM model, these savings are modeled as an

equipment‐independent module with savings unassociated with equipment improvement.

The behaviour measure savings used within EERAM reflect estimates of usage‐based and non‐incented

based behaviour. The incented equipment‐based behaviour is assumed to be addressed by the program

administrator’s other incentive‐based DSM programs. The measure life is assumed to be one year,

reflecting the need to continually reinforce the behaviour program’s message to conserve and use energy

efficiently.
21 

3. Efficiency Measure Characterization

A significant component underlying Navigant’s estimate of energy efficiency and peak demand

reduction potential is the set of residential and business, non‐profit and institutional (BNI) measure

characterizations. These measure characterizations were developed by Navigant both to act as a base

component of the potential analysis, as well as key inputs to the ENSC Technical Reference Manual

(TRM).  

This section of the report provides an overview of the process undertaken by Navigant to develop the

measure characterizations. Additional detail regarding the measures themselves, the sources used, and

the underlying measure assumptions is available in the Excel measure characterization spreadsheets

provided to ENSC by Navigant.  

The main efficiency measure attributes estimated as part of the measure characterization exercise were:

1. Base technology and efficient measure levels of annual consumption of all relevant resources.  

2. The effective useful life (EUL) of the base technology and efficient measure.

3. The labour and materials cost of the base technology and efficient measure.

4. The net‐to‐gross ratio of the efficient measure (in a given program).

5. The density in the province of the base and efficient technology.6  

This section of the report is divided into the following sub‐sections:

 Measure Selection and Inclusion: This sub‐section contains a brief description of the manner in

which measures were chosen for characterization.  

 Base and Efficient Consumption, EUL and Incremental Measure Cost: This sub‐section

describes the approach undertaken by Navigant to estimate these measure characteristics.

 Loadshapes: This sub‐section describes how the hourly savings profiles were derived and

applied for various end‐uses.

 Net‐To‐Gross: This sub‐section briefly describes how net‐to‐gross values were ascribed to each

measure.

 Measure Densities: This sub‐section describes the general approach to estimating a technology’s

density if the density was not drawn directly from the baseline report.

3.1 Measure Selection and Inclusion

Measures for inclusion in the ENSC TRM and EERAM potential model were chosen by ENSC and

provided in a list to Navigant, with the measures broken down by program. In some cases measures

originally requested by ENSC were not characterized because:

                                                          

6 Densities for BNI technologies were informed by the findings of the Navigant 2012 Baseline Study, and from phone

surveys conducted by Corporate Research Associates (CRA)  on behalf of ENSC (and not as part of the process of

measure characterization, as were many of those for residential technologies.) In some cases for residential

technologies, publically‐available statistical information (from NRCan or Statistics Canada) was found by Navigant

to be applicable and was used as a secondary source to supplement the baseline study results where appropriate.  
22 

 It was determined that the technology was insufficiently developed to be included as part of an

ENSC program (for example: LED linear fluorescent high bay fixtures).

 Savings (beyond the assumed baseline technology) were insufficiently high relative to the

savings of other efficiency measures in the same program to justify characterizing the measure

(for example: pre‐rinse spray valves).

 These would no longer be offered as part of an ENSC program (for example: all residential

natural gas fuel‐switching measures).

 These would be handled under a different umbrella, such as Codes and Standards (for example:

LED traffic signals).

Prescriptive measures were characterized for the following ENSC residential programs:

 Efficient Products (Appliance Retirement)

 Efficient Products (Direct Install)

 Efficient Products (Instant Savings)

 New Construction (Performance Plus)

 Existing Homes (Green Heating)

 Existing Homes (Solar Program)7

 Home Energy Assessment

 Multi‐Unit Residential Building (MURB) Program (individual suite measures)

Prescriptive measures were characterized for the following ENSC BNI programs:

 Business Energy Solutions

 Business Energy Rebates (for: commercial kitchens, commercial laundry, commercial

refrigeration, motors and variable speed drives, compressed air equipment, central heating and

cooling, commercial lighting and agriculture)

 Multi‐Unit Residential Building (MURB) Program (common areas)

3.2 Base and Efficient Consumption, Effective Useful Life, and Incremental Measure

Cost

The overall approach for estimating base and efficient consumption (of electricity and other resources),

effective useful life (EUL), and incremental measure cost was governed by a set of sources of information

that were consulted in a descending order determined by:

 How locally relevant the source was (e.g., a Nova Scotia‐specific source is better than an Ontario

source, but an Ontario source is better than a California source, etc.)

 Vintage of the information (how recent)

 How closely the parameters of the measure characterized in the source matched those of the

measure to be characterized by ENSC (e.g., same wattage or flow rate? same size? same type of

application?)  

 The transparency and reproducibility of the source’s method for calculating consumption and

savings.

                                                          

7 Note that despite the name, new construction installing solar measures receives its incentives from the “Existing

Homes” program.
23 

Selecting the most appropriate source for each component of the measure characterization at times

required some compromise – in some cases the most locally‐specific source was a number of years older

than a source that focused on a jurisdiction considerably distant from Nova Scotia. Thus, in some

instances, the choice of source may have been a matter of Navigant’s professional judgment.

For residential measures, the two most frequently‐cited sources were Econoler’s “Evaluation of 2011

DSM Programs” document and the Ontario Power Authority’s “2011 Prescriptive Measures and

Assumptions”8.

Other documents frequently used as sources for the characterization measure savings, measure life and

measure cost include:

 Ontario Power Authority, 2011 Quasi‐Prescriptive Measures and Assumptions (March 2011)9

 Illinois Energy Efficiency Stakeholder Advisory Group, State of Illinois Energy Efficiency Technical

Reference Manual (June 2012)10

 Ontario Energy Board, Measures and Assumptions for Demand Side Management (April 2009)11

 Natural Resource Canada (NRCan)’s Comprehensive Energy Use Database.

12

 US Environmental Protection Agency, Energy Star® website (measure calculators)13

 National Grid, Rhode Island Technical Reference Manual for Estimating Savings from Energy Efficiency

Measures (2012)14

 Efficiency Vermont, Technical Reference User Manual No. 2010‐67A (December 2010)

 Massachusetts Electric and Gas Energy Efficiency Program, Massachusetts Technical Reference

Manual for Estimating Savings from Energy Efficiency Measures (October 2010)15

In addition to the documents cited above, Navigant used a plethora of online savings calculators (such as

those published by the Food Service Technology Center16 and CEATI17), measure‐specific studies and

measure installation case studies to estimate base and efficient consumption. Full citations for all

measures may be found in the measure characterization spreadsheets provided to ENSC by Navigant.

                                                          

8 http://www.powerauthority.on.ca/evaluation‐measurement‐and‐verification/measures‐assumptions‐lists

9

http://www.powerauthority.on.ca/sites/default/files/2011%20Quasi%20Prescriptive%20Release%20Version%201%20

%28December%202010%29.pdf

10

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDIQFjAA&url=http%3A%2F%2Filsa

g.org%2Fyahoo_site_admin%2Fassets%2Fdocs%2FIllinois_Statewide_TRM_Effective_060112_Final_Technical_Versi

on_082012_Clean.267210945.docx&ei=wn9tUbGCK_PB4AONzoGgCA&usg=AFQjCNFhuELMLWeTPtH9VYxhLrUe

mepvQw&sig2=003ELUpQVTpGbahbK92vyQ&bvm=bv.45175338,d.dmg

11 http://www.ontarioenergyboard.ca/OEB/_Documents/EB‐2008‐

0346/Navigant_Appendix_C_substantiation_sheet_20090429.pdf

12 http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/trends_egen_ca.cfm

13 https://www.energystar.gov/index.cfm?c=home_improvement.hm_improvement_index&s=m

14 http://www.nationalgridus.com/non_html/eer/ri/Rhode%20Island%20TRM_PY2013_final.pdf

15 http://www.ma‐eeac.org/docs/MA%20TRM_2011%20PLAN%20VERSION.PDF

16 http://www.fishnick.com/

17 http://www.ceati.com/
24 

In Navigant’s experience, frequently, the least reliable secondary‐source measure characterization

estimates are those pertaining to measure cost. This is due to the fact that a rapidly‐developing

marketplace in energy‐efficient technologies and more widespread adoption of these technologies tends

to drive prices down over time. Therefore, Navigant’s approach to estimating cost was typically to turn

to primary sources, including online retailers (amazon.ca, homedepot.ca, bulbtown.ca, etc.) and local

contractors and installers, particularly for newer technologies. For more established measures with

known labour and material costs, research from existing TRMs was used to characterize measure costs

and incremental costs.  

3.3 Loadshapes

The potential study relied on annual end use loadshapes to develop results, allocating savings potential

across the different sectors (residential, commercial, and industrial.) Loadshapes were derived by

Navigant from a variety of primary and secondary sources, including whole building energy modeling

performed by Navigant based on data from the Navigant 2012 baseline study, and secondary research

such as Pacific Gas & Electric (PG&E) Climate Zone 2/16. This section describes what loadshapes are and

how they were developed for a variety of end uses to support the Nova Scotia potential study and

Technical Reference Manual (TRM).

3.3.1 Loadshapes Overview

A loadshape is an hour by hour description of how a particular technology type, such as HVAC heating

equipment or Interior Lighting, uses energy, every hour from January 1 through December 31 (with 24

hours over 365 days for a total of 8,760 hours annually.) Energy savings loadshapes provide annual

consumption patterns for energy consuming equipment such as light fixtures and air conditioners.

Electrical energy savings for energy efficiency measures related to these end uses will follow these same

consumption loadshapes, since an efficient measure generates savings when the equipment is operating.

Loadshapes for Potential Studies

Loadshapes address impacts on system capacity by assuming that savings for a given end use match

consumption patterns. Loadshapes are used to allocate a proportion of the annual savings to each hour

of the day for each end use. While the overall Nova Scotia avoided cost calculation is currently not

time‐differentiated, correct end use loadshapes positively impact the total savings potential by

accurately characterizing variable‐driven measures such as weather sensitive measures. This reduces

uncertainty in the overall estimation of program potential. The loadshapes can also be utilized during

day‐to‐day program implementation as part of the ENSC Technical Reference Manual (TRM), providing

an accurate means of estimating measure‐level demand savings during peak hours.

Loadshapes for TRMs

Loadshapes used for TRMs are normalized to an annual maximum value for each end use, such as

HVAC or lighting. Where the hourly fraction is 1.00, the hourly usage is at the annual maximum kW per

whole building energy modeling (eQuest). Where the hourly fraction, or average fraction across several

hours, is less than one, the end use is, on average, operating at part load. This allows the user of the TRM

to estimate relative average energy savings based on equipment usage during certain time windows,

such as the winter peak, for energy conservation measures.
25 

3.3.2 Characteristic Loadshapes for Nova Scotia

The loadshapes used to generate the Nova Scotia potential study results were as follows (NS indicates

the end use was modeled for Nova Scotia specific weather conditions):

Residential Loadshapes

 Refrigeration  

 Clothes Washer  

 Miscellaneous Loads  

 Domestic Hot Water (NS)  

 Interior Lighting (NS)

 HVAC (NS)

Commercial Loadshapes

 HVAC (NS)

 Refrigeration (NS)  

 Laundry

 Miscellaneous Loads  

 Domestic Hot Water (NS)  

 Interior Lighting (NS)

 Agricultural

 Process/Industrial

The next section outlines how region specific loadshapes are developed using whole building energy

modeling.

3.3.3 Whole Building Modeling of EE Loadshapes for the Potential Study

Nova Scotia‐specific loadshapes were developed using whole building energy modeling with eQuest

modeling software, particularly important for weather dependent measures, such as HVAC. For both

residential and BNI buildings, specific data were input into the modeling software including the

following:

 Building and equipment data from the 2012 Nova Scotia baseline study

 Typical daily schedules for Nova Scotia

 U.S. National Renewable Energy Laboratory (NREL) typical meteorological year (TMY) data

The resulting region‐specific outputs of the model were then disaggregated into the different end use

categories, and normalized so that the total usage for each end use sums to 1.00. These normalized

loadshapes were associated with certain measures in EERAM, such as HVAC and Lighting measures,

such that the total aggregated savings reflects region‐specific variables, such as weather, daylight, and

local system usage.

3.4 Net‐to‐Gross

Net‐to‐gross (NTG) values are specific to a unique combination of measure and delivery program, not

just specific to a given measure (as are savings and EUL.) NTG values used in the measure
26 

characterizations were drawn exclusively from the Econoler “Evaluation of 2011 DSM Programs”

document or estimated as some combination of values across different programs in that document.

3.5 Measure Densities

Measure or technology density is a requirement for the estimation of energy efficiency and demand

reduction potential. “Density” in this case refers to the prevalence of the technology in question

throughout the province. For example, the base technology density for the “Outdoor Motion Sensor”

residential measure would be the average number of outdoor light bulbs installed per single‐family

home in Nova Scotia not connected to a motion sensor. The efficient technology density would be the

average number of outdoor light bulbs installed per single‐family home in Nova Scotia that are also

connected to a motion sensor.

BNI measure densities were estimated using the data obtained from the BNI sector baseline survey, and

phone surveys conducted by Corporate Research Associates (CRA) on behalf of ENSC. In the case of the

residential sector just under half of the densities used were derived from the residential sector baseline

survey. Densities not derived from the baseline study were derived by Navigant staff during the

measure characterization process using a variety of different sources. The most common sources for

information for the estimation of residential measure densities include:

 Natural Resource Canada (NRCan)’s Comprehensive Energy Use Database18

 NRCan’s Survey of Household Energy Use (2007)19

 Efficiency Nova Scotia’s 2011 Socket Study (December 2011)20

A variety of other tables obtained from Statistics Canada and a variety of provincial authorities were also

used to determine measure densities.

                                                          

18 http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/trends_egen_ca.cfm

19 http://oee.nrcan.gc.ca/publications/statistics/sheu‐summary07/sheu.cfm

20 http://efficiencyns.ca/images/uploads/ENSC%202013‐2015%20DSM%20Plan%20Part%20II.pdf
27 

4. Conclusion

Navigant used the EERAM model to estimate the Nova Scotia long‐term (2015‐2040) electrical energy

efficiency potential. The results for Technical and Economic Potential are provided for context and

include both savings from DSM programs as well as savings outside of DSM programs, such as Codes

and Standards. The Achievable Potential results include savings from DSM programs only and are

presented in four scenarios of varying incentive amounts; Low, Base, Mid, and High.  

The results of this potential study can be used to develop energy efficiency investment strategies, and

define specific objectives to meet provincial energy efficiency goals. The yearly results of the Achievable

Potential scenarios are presented in terms of energy and demand savings along with their associated

costs for the Residential, Commercial, and Industrial sectors and therefore may be used within the

utility’s Integrated Resource Planning (IRP) process.

As Navigant’s EERAM tool accounts for code‐mandated efficiency effects, and is based on existing

market characterization via the baseline study and other research conducted by Navigant and others, the

results of this study are robust to baseline uncertainties and foreseeable market transformations.
28 

Appendix A. Technical Potential

Figure 6 presents the 2015 – 2040 Technical Potential with the breakdown of savings from DSM

programs and savings from Codes and Standards.

Figure 6. Total All Sectors Energy Efficiency Technical Potential – 2015 to 2040 (Gross at Generator)

Total All Sectors Technical Potential (Gross at Generator)

Peak Demand Savings (MW) Energy Savings (GWh)

Year Programs Codes &

Standards Total Programs Codes &

Standards Total

2015 1,141 9 1,150 5,277 55 5,332

2016 1,152 16 1,168 5,343 91 5,434

2017 1,167 23 1,190 5,426 128 5,554

2018 1,177 31 1,208 5,486 168 5,654

2019 1,189 39 1,227 5,548 209 5,757

2020 1,199 47 1,246 5,607 252 5,859

2021 1,210 56 1,265 5,665 295 5,961

2022 1,219 64 1,284 5,710 337 6,047

2023 1,227 72 1,300 5,747 374 6,120

2024 1,310 81 1,391 6,301 417 6,719

2025 1,318 87 1,406 6,342 455 6,797

2026 1,327 91 1,418 6,384 482 6,866

2027 1,335 95 1,430 6,426 509 6,935

2028 1,342 98 1,440 6,465 534 6,999

2029 1,351 102 1,452 6,510 559 7,068

2030 1,358 105 1,463 6,551 582 7,133

2031 1,366 107 1,472 6,593 602 7,195

2032 1,373 109 1,481 6,635 622 7,257

2033 1,380 111 1,491 6,677 642 7,320

2034 1,388 113 1,500 6,721 667 7,388

2035 1,395 115 1,510 6,766 694 7,460

2036 1,403 117 1,520 6,812 721 7,533

2037 1,410 119 1,529 6,859 749 7,608

2038 1,417 122 1,538 6,900 776 7,676

2039 1,423 124 1,547 6,944 803 7,747

2040 1,430 126 1,556 6,990 831 7,820
29 

Appendix B. Economic Potential

Figure 7 presents the total Economic Potential results of the analysis, 2015 through 2040, across all

sectors; Residential, Commercial and Industrial.

Figure 7. Total All Sectors Energy Efficiency Economic Potential – 2015 to 2040 (Gross at Generator)

Total All Sectors

Economic Potential

(Gross at Generator)

Year Peak

Demand

Savings

(MW)

Energy

Savings

(GWh)

2015 990 4,662

2016 999 4,719

2017 1,014 4,796

2018 1,024 4,852

2019 1,035 4,909

2020 1,046 4,970

2021 1,114 5,253

2022 1,137 5,348

2023 1,187 5,546

2024 1,197 5,592

2025 1,204 5,629

2026 1,212 5,668

2027 1,220 5,707

2028 1,227 5,743

2029 1,235 5,786

2030 1,242 5,824

2031 1,249 5,863

2032 1,280 6,024

2033 1,287 6,063

2034 1,294 6,104

2035 1,301 6,145

2036 1,308 6,188

2037 1,315 6,232

2038 1,322 6,270

2039 1,328 6,311

2040 1,334 6,354
30 

Appendix C. Achievable Potential

For the Base Scenario, Figure 8 presents the Total All Sectors Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 8. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario

Total All Sectors Achievable Potential (Net‐at‐Generator) – Base Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 29.8 29.8 137.8 137.8 $50.7 $50.7

2016 30.9 50.4 139.9 226.1 $50.5 $101.2

2017 31.6 77.3 141.7 360.8 $50.0 $151.2

2018 29.9 102.9 136.2 480.3 $52.4 $203.6

2019 28.9 127.5 134.8 597.4 $57.0 $260.6

2020 28.1 150.0 133.9 708.9 $61.5 $322.1

2021 27.3 172.4 130.1 817.1 $56.9 $379.0

2022 26.9 195.6 127.6 932.3 $54.1 $433.2

2023 26.7 217.8 126.6 1,040.9 $51.5 $484.7

2024 27.0 241.1 127.4 1,155.4 $50.8 $535.5

2025 27.6 261.2 130.1 1,257.1 $50.6 $586.1

2026 28.8 284.1 135.6 1,371.0 $52.1 $638.3

2027 30.6 313.2 143.5 1,509.5 $54.8 $693.0

2028 32.8 341.5 153.4 1,644.6 $58.5 $751.5

2029 34.9 373.7 163.3 1,797.6 $60.7 $812.2

2030 36.6 407.1 170.7 1,956.3 $63.1 $875.3

2031 37.2 439.8 173.5 2,112.9 $62.6 $937.9

2032 36.6 471.6 171.4 2,266.2 $61.4 $999.4

2033 35.4 502.6 166.0 2,415.4 $59.3 $1,058.7

2034 33.8 532.7 159.3 2,560.6 $56.7 $1,115.4

2035 32.3 561.2 153.0 2,699.4 $47.7 $1,163.1

2036 30.8 588.6 147.0 2,833.1 $46.5 $1,209.6

2037 29.6 612.3 141.5 2,955.0 $45.4 $1,254.9

2038 28.4 638.6 136.1 3,080.8 $44.4 $1,299.3

2039 27.4 662.2 131.6 3,197.5 $43.5 $1,342.8

2040 26.6 684.5 127.9 3,308.7 $42.3 $1,385.0

   
31 

For the Low Scenario, Figure 9 presents the Total All Sectors Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 9. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario

Total All Sectors Achievable Potential (Net‐at‐Generator) – Low Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 23.9 23.9 107.7 107.7 $46.5 $46.5

2016 24.3 38.9 106.0 162.2 $44.8 $91.3

2017 24.2 57.8 104.0 259.1 $42.9 $134.3

2018 23.7 77.5 102.3 345.9 $39.2 $173.5

2019 23.0 97.0 100.0 431.1 $37.6 $211.1

2020 22.5 114.9 98.7 508.5 $37.8 $248.8

2021 22.1 131.7 98.1 582.0 $38.5 $287.3

2022 21.9 149.6 98.7 670.4 $39.8 $327.1

2023 21.9 166.7 99.5 754.2 $39.7 $366.8

2024 22.0 184.6 101.1 841.0 $40.4 $407.1

2025 22.4 200.8 103.2 922.0 $40.8 $447.9

2026 23.1 217.6 106.8 1,005.3 $42.0 $489.9

2027 24.2 240.2 111.8 1,111.5 $43.7 $533.5

2028 25.7 261.2 118.5 1,211.0 $46.1 $579.6

2029 27.4 285.8 126.5 1,326.9 $47.0 $626.6

2030 29.0 313.3 134.2 1,455.8 $49.5 $676.2

2031 30.1 339.1 139.5 1,579.0 $50.8 $726.9

2032 30.3 364.7 141.1 1,702.6 $51.1 $778.0

2033 29.7 389.5 139.2 1,822.7 $50.4 $828.4

2034 28.7 413.9 135.2 1,941.9 $48.9 $877.3

2035 27.4 437.2 130.5 2,057.0 $47.2 $924.5

2036 26.3 459.9 125.9 2,169.1 $45.6 $970.1

2037 25.2 479.2 121.6 2,271.4 $44.0 $1,014.1

2038 24.2 501.4 117.8 2,379.5 $42.5 $1,056.7

2039 23.4 521.2 114.3 2,479.8 $41.3 $1,098.0

2040 22.7 539.8 111.2 2,575.6 $40.2 $1,138.1

   
32 

For the Mid Scenario, Figure 10 presents the Total All Sectors Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 10. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario

Total All Sectors Achievable Potential (Net‐at‐Generator) – Mid Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 35.4 35.4 164.0 164.0 $56.1 $56.1

2016 37.9 62.6 171.0 283.4 $57.5 $113.6

2017 39.8 98.1 177.7 454.2 $59.0 $172.6

2018 37.9 131.4 170.0 606.0 $66.1 $238.6

2019 37.0 163.3 167.4 752.5 $76.4 $315.0

2020 36.6 194.5 166.2 897.5 $86.7 $401.7

2021 36.1 225.8 162.2 1,038.8 $81.7 $483.4

2022 35.7 258.1 159.6 1,186.0 $79.1 $562.5

2023 35.6 289.1 158.8 1,325.9 $76.3 $638.7

2024 36.1 321.6 161.6 1,474.8 $76.8 $715.6

2025 37.3 349.7 167.4 1,603.8 $78.5 $794.1

2026 39.3 383.0 176.6 1,758.8 $81.9 $876.0

2027 41.7 423.1 187.5 1,940.7 $85.7 $961.7

2028 44.4 462.9 199.1 2,121.0 $90.1 $1,051.8

2029 46.2 506.5 207.8 2,318.3 $92.0 $1,143.9

2030 46.7 548.6 210.6 2,511.5 $92.2 $1,236.1

2031 45.5 589.5 206.8 2,700.6 $87.4 $1,323.5

2032 43.2 627.8 198.5 2,880.9 $82.3 $1,405.8

2033 40.5 664.5 188.4 3,055.0 $76.8 $1,482.6

2034 37.8 699.1 178.4 3,220.7 $71.2 $1,553.8

2035 35.4 730.8 169.5 3,376.3 $57.9 $1,611.7

2036 33.2 760.6 161.4 3,523.9 $54.8 $1,666.5

2037 31.3 785.9 154.2 3,657.9 $52.0 $1,718.5

2038 29.7 813.1 147.8 3,794.4 $49.0 $1,767.5

2039 28.3 837.4 142.6 3,921.4 $46.5 $1,814.1

2040 27.3 858.6 139.0 4,039.3 $43.6 $1,857.7
33 

For the High Scenario, Figure 11 presents the Total All Sectors Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 11. Total All Sectors Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario

Total All Sectors Achievable Potential (Net‐at‐Generator) – High Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 39.6 39.6 174.9 174.9 $76.3 $76.3

2016 45.5 74.3 191.3 314.6 $92.0 $168.3

2017 49.9 120.1 205.5 513.1 $104.6 $272.9

2018 46.3 161.4 193.9 687.3 $107.4 $380.3

2019 43.2 198.4 186.9 849.1 $112.8 $493.1

2020 41.0 234.5 182.9 1,012.2 $119.3 $612.4

2021 39.6 269.6 177.7 1,170.2 $106.9 $719.3

2022 39.0 305.3 175.6 1,334.1 $102.2 $821.5

2023 39.2 340.0 176.9 1,492.3 $99.0 $920.5

2024 40.7 377.2 184.7 1,664.0 $104.0 $1,024.5

2025 43.1 410.8 196.0 1,820.5 $110.9 $1,135.4

2026 45.8 450.3 209.2 2,006.7 $118.6 $1,254.0

2027 47.8 496.1 217.7 2,217.3 $121.1 $1,375.1

2028 48.3 539.7 220.6 2,418.5 $120.4 $1,495.5

2029 47.1 584.1 216.5 2,624.4 $114.5 $1,609.9

2030 44.5 623.9 207.1 2,813.4 $106.5 $1,716.4

2031 41.4 661.0 195.4 2,991.8 $94.6 $1,811.0

2032 38.4 694.9 183.8 3,158.6 $85.9 $1,896.9

2033 35.7 727.5 173.7 3,320.5 $65.8 $1,962.8

2034 33.3 757.8 164.7 3,473.0 $61.1 $2,023.8

2035 31.3 784.6 157.0 3,614.3 $56.6 $2,080.4

2036 29.7 810.3 150.4 3,749.5 $52.4 $2,132.9

2037 28.5 832.8 145.1 3,874.4 $49.6 $2,182.5

2038 27.5 857.9 141.3 4,005.0 $44.2 $2,226.7

2039 26.6 881.0 137.5 4,128.6 $43.4 $2,270.1

2040 25.9 900.7 134.0 4,241.0 $42.6 $2,312.6
34 

For the Base Scenario, Figure 12 presents the Residential Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 12. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator)

– Base Scenario

Residential Sector Achievable Potential (Net‐at‐Generator) – Base Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 16.4 16.4 64.1 64.1 $25.3 $25.3

2016 18.0 30.3 68.3 116.4 $25.6 $50.9

2017 18.7 45.1 69.7 170.7 $25.2 $76.1

2018 16.6 57.6 62.4 217.5 $27.4 $103.6

2019 16.1 69.6 63.4 264.3 $33.1 $136.7

2020 15.7 81.5 64.4 314.7 $38.2 $174.9

2021 15.3 92.8 61.9 361.0 $34.3 $209.2

2022 15.0 103.6 60.1 405.6 $31.7 $240.9

2023 14.9 114.4 59.0 448.8 $29.1 $270.0

2024 14.9 124.9 58.6 491.7 $28.3 $298.3

2025 15.1 136.0 58.8 533.8 $27.5 $325.9

2026 15.6 147.9 60.1 580.6 $28.2 $354.1

2027 16.3 160.1 62.3 627.3 $29.5 $383.6

2028 17.3 173.8 65.3 679.8 $31.5 $415.1

2029 18.2 188.1 68.1 732.8 $32.9 $448.0

2030 18.9 203.0 70.0 787.8 $34.1 $482.0

2031 19.1 218.2 70.3 843.4 $34.0 $516.0

2032 18.7 232.8 69.0 897.2 $33.1 $549.1

2033 18.1 247.0 66.5 948.7 $31.8 $580.9

2034 17.3 260.3 63.6 997.3 $30.3 $611.2

2035 16.5 272.5 61.1 1,042.4 $22.3 $633.5

2036 15.8 284.4 58.6 1,085.8 $22.0 $655.5

2037 15.1 293.2 56.3 1,121.0 $21.6 $677.1

2038 14.5 305.2 53.7 1,162.9 $21.3 $698.4

2039 14.0 315.1 51.5 1,198.8 $21.1 $719.5

2040 13.5 324.2 49.6 1,231.2 $20.9 $740.4
35 

For the Low Scenario, Figure 13 presents the Residential Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 13. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator)

– Low Scenario

Residential Sector Achievable Potential (Net‐at‐Generator) – Low Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 14.0 14.0 49.9 49.9 $21.6 $21.6

2016 15.1 25.0 51.7 85.6 $21.1 $42.7

2017 15.3 36.4 51.0 121.2 $19.8 $62.5

2018 13.9 46.3 45.3 150.9 $14.6 $77.1

2019 13.4 55.7 43.9 178.2 $13.9 $91.0

2020 13.0 64.8 43.5 207.8 $14.4 $105.4

2021 12.7 73.5 43.2 235.4 $15.3 $120.7

2022 12.6 81.9 44.1 264.4 $16.8 $137.5

2023 12.5 90.9 44.7 297.7 $16.6 $154.2

2024 12.6 98.8 45.4 326.1 $17.3 $171.5

2025 12.6 107.6 45.7 355.4 $17.2 $188.7

2026 12.8 116.6 46.3 387.4 $17.6 $206.3

2027 13.2 125.6 47.4 418.8 $18.1 $224.4

2028 13.8 135.7 49.0 454.2 $18.9 $243.3

2029 14.5 146.2 51.0 490.0 $19.5 $262.8

2030 15.2 157.5 52.9 528.1 $20.3 $283.1

2031 15.6 169.2 54.0 567.3 $20.8 $303.8

2032 15.5 180.5 53.7 605.8 $20.8 $324.6

2033 15.0 191.6 52.3 643.0 $20.3 $345.0

2034 14.3 202.0 50.2 678.2 $19.6 $364.6

2035 13.6 211.2 47.8 710.1 $18.8 $383.3

2036 12.8 220.1 45.6 740.7 $17.9 $401.3

2037 12.2 226.1 43.6 763.3 $17.1 $418.3

2038 11.7 235.2 41.7 793.7 $16.3 $434.6

2039 11.2 242.4 40.0 818.6 $15.7 $450.3

2040 10.7 248.9 38.5 840.9 $15.1 $465.5
36 

For the Mid Scenario, Figure 14 presents the Residential Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 14. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator)

– Mid Scenario

Residential Sector Achievable Potential (Net‐at‐Generator) – Mid Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 18.6 18.6 71.9 71.9 $26.5 $26.5

2016 21.0 35.5 78.3 134.1 $27.6 $54.0

2017 22.4 54.0 81.7 200.4 $28.6 $82.6

2018 20.6 70.5 73.8 258.6 $36.3 $119.0

2019 20.7 87.0 75.7 317.7 $48.2 $167.2

2020 20.9 104.1 77.8 381.5 $59.4 $226.5

2021 20.9 121.0 76.0 442.0 $55.5 $282.0

2022 20.8 137.6 74.5 501.0 $53.2 $335.2

2023 20.7 154.1 73.5 557.3 $50.4 $385.6

2024 20.9 170.7 74.3 615.9 $50.6 $436.2

2025 21.4 188.0 76.0 675.1 $51.4 $487.6

2026 22.2 206.6 78.7 741.0 $53.6 $541.1

2027 23.3 225.7 81.7 807.0 $55.6 $596.7

2028 24.4 246.5 84.9 879.2 $58.0 $654.7

2029 25.1 267.7 87.0 950.8 $59.2 $713.8

2030 25.0 288.7 86.8 1,022.6 $58.8 $772.6

2031 24.1 309.0 84.1 1,091.9 $55.5 $828.1

2032 22.6 327.5 79.7 1,156.6 $51.6 $879.7

2033 20.9 344.5 74.7 1,216.3 $47.3 $927.0

2034 19.3 359.8 69.7 1,270.7 $43.0 $970.0

2035 17.7 373.1 65.3 1,319.8 $30.8 $1,000.8

2036 16.3 385.4 61.3 1,365.8 $28.7 $1,029.5

2037 15.1 394.2 57.7 1,402.3 $26.7 $1,056.2

2038 14.0 405.6 54.1 1,444.5 $25.0 $1,081.2

2039 13.1 414.5 51.1 1,479.7 $23.6 $1,104.8

2040 12.4 420.7 48.8 1,507.0 $22.4 $1,127.2
37 

For the High Scenario, Figure 15 presents the Residential Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 15. Residential Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator)

– High Scenario

Residential Sector Achievable Potential (Net‐at‐Generator) – High Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 22.0 22.0 78.8 78.8 $42.1 $42.1

2016 27.2 45.1 90.9 153.7 $56.2 $98.3

2017 30.4 71.5 98.2 236.5 $67.5 $165.8

2018 27.2 94.6 87.7 308.7 $71.5 $237.3

2019 25.4 115.8 86.2 378.3 $78.7 $316.1

2020 24.0 136.0 85.6 449.9 $86.1 $402.2

2021 23.0 155.0 82.4 516.8 $75.1 $477.4

2022 22.5 173.3 80.6 582.0 $70.6 $548.0

2023 22.4 191.6 80.3 645.0 $67.2 $615.2

2024 23.2 210.3 83.7 713.1 $71.2 $686.3

2025 24.4 230.7 88.4 784.7 $76.3 $762.7

2026 25.7 252.7 93.5 865.2 $82.3 $845.0

2027 26.4 275.0 95.2 944.6 $83.2 $928.2

2028 26.4 297.9 94.6 1,026.7 $81.7 $1,010.0

2029 25.3 319.3 91.2 1,102.4 $76.8 $1,086.7

2030 23.6 338.9 85.8 1,173.1 $70.0 $1,156.7

2031 21.6 356.6 79.5 1,237.8 $61.3 $1,218.0

2032 19.6 372.1 73.4 1,296.2 $54.1 $1,272.1

2033 17.8 386.0 68.3 1,349.5 $35.4 $1,307.5

2034 16.3 398.2 63.6 1,397.4 $31.9 $1,339.4

2035 14.9 408.1 59.4 1,439.0 $29.2 $1,368.6

2036 13.8 417.5 55.3 1,478.0 $27.0 $1,395.6

2037 12.9 424.2 52.1 1,509.1 $25.6 $1,421.2

2038 12.4 433.8 49.9 1,547.1 $21.5 $1,442.7

2039 12.0 442.0 48.4 1,580.4 $21.0 $1,463.8

2040 11.7 447.6 47.3 1,606.5 $20.5 $1,484.3
38 

For the Base Scenario, Figure 16 presents the Commercial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 16. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐

Generator) – Base Scenario

Commercial Sector Achievable Potential (Net‐at‐Generator) – Base Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 9.0 9.0 48.4 48.4 $14.0 $14.0

2016 8.6 13.0 47.0 69.0 $13.6 $27.6

2017 8.6 21.1 47.6 123.0 $13.3 $40.9

2018 8.8 29.8 47.8 170.0 $13.3 $54.2

2019 8.4 38.0 46.0 215.1 $12.5 $66.7

2020 8.1 45.0 44.6 254.2 $12.1 $78.8

2021 7.9 52.3 43.6 293.6 $11.6 $90.3

2022 7.8 60.3 43.1 338.6 $11.4 $101.8

2023 7.7 67.6 43.0 380.0 $11.4 $113.1

2024 7.8 76.0 43.8 425.8 $11.5 $124.6

2025 8.1 81.6 45.4 462.2 $11.8 $136.4

2026 8.6 88.4 48.1 503.5 $12.4 $148.8

2027 9.3 99.6 51.8 563.2 $13.3 $162.0

2028 10.1 109.1 56.4 616.3 $14.4 $176.5

2029 10.9 120.9 61.1 681.0 $15.1 $191.6

2030 11.5 133.1 64.6 747.9 $16.0 $207.6

2031 11.7 144.4 65.9 812.0 $15.7 $223.4

2032 11.5 155.3 64.9 874.6 $15.3 $238.7

2033 11.1 166.0 62.4 935.7 $14.7 $253.4

2034 10.5 176.7 59.3 995.7 $13.8 $267.1

2035 9.9 187.0 56.3 1,053.3 $12.9 $280.1

2036 9.3 196.7 53.5 1,108.2 $12.2 $292.3

2037 8.9 205.9 51.0 1,160.5 $11.6 $303.9

2038 8.5 214.7 48.9 1,210.5 $11.1 $315.0

2039 8.1 223.0 47.1 1,258.3 $10.6 $325.5

2040 7.9 231.0 45.9 1,304.7 $9.7 $335.2
39 

For the Low Scenario, Figure 17 presents the Commercial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 17. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐

Generator) – Low Scenario

Commercial Sector Achievable Potential (Net‐at‐Generator) – Low Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 6.5 6.5 36.9 36.9 $13.1 $13.1

2016 5.9 8.4 34.4 44.9 $12.1 $25.3

2017 5.7 13.2 33.6 84.9 $11.6 $36.9

2018 6.3 19.6 35.8 120.7 $12.4 $49.3

2019 6.2 26.1 35.2 157.3 $11.9 $61.2

2020 6.1 31.7 34.5 187.5 $11.6 $72.9

2021 6.1 36.7 34.3 215.1 $11.5 $84.4

2022 6.0 42.9 34.0 252.9 $11.4 $95.8

2023 6.0 47.9 34.0 283.3 $11.4 $107.1

2024 6.1 54.5 34.5 319.7 $11.3 $118.5

2025 6.2 58.8 35.6 350.0 $11.6 $130.1

2026 6.5 63.5 37.4 380.1 $12.1 $142.2

2027 7.0 72.4 39.9 427.9 $12.9 $155.1

2028 7.5 79.3 43.1 467.5 $14.0 $169.0

2029 8.2 88.4 46.9 518.0 $14.4 $183.4

2030 8.8 99.0 50.5 576.0 $15.5 $198.9

2031 9.2 108.0 53.0 627.9 $16.0 $214.8

2032 9.3 116.9 53.8 679.8 $16.0 $230.8

2033 9.2 125.2 53.0 729.8 $15.7 $246.5

2034 8.9 133.9 51.3 780.3 $15.0 $261.5

2035 8.5 142.5 49.2 829.9 $14.3 $275.8

2036 8.1 150.9 47.2 878.0 $13.6 $289.5

2037 7.8 159.0 45.4 924.6 $13.0 $302.5

2038 7.5 166.8 43.8 969.7 $12.5 $315.0

2039 7.2 174.3 42.3 1,013.0 $12.0 $327.0

2040 7.0 181.5 41.1 1,054.8 $11.5 $338.5
40 

For the Mid Scenario, Figure 18 presents the Commercial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 18. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐

Generator) – Mid Scenario

Commercial Sector Achievable Potential (Net‐at‐Generator) – Mid Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 11.2 11.2 60.5 60.5 $17.1 $17.1

2016 11.2 17.6 61.0 95.1 $17.3 $34.4

2017 11.7 29.0 63.8 165.2 $17.3 $51.7

2018 11.5 40.2 62.3 225.8 $16.7 $68.4

2019 10.8 50.1 59.1 281.6 $15.6 $84.0

2020 10.2 59.4 56.8 333.8 $14.9 $99.0

2021 9.9 68.8 55.2 385.4 $14.2 $113.1

2022 9.7 78.9 54.4 441.3 $13.9 $127.0

2023 9.7 88.3 54.4 494.4 $13.9 $140.9

2024 9.9 98.8 55.7 552.1 $14.1 $155.1

2025 10.3 105.5 58.4 596.2 $14.8 $169.8

2026 11.1 114.7 62.6 651.9 $15.5 $185.4

2027 12.0 128.7 67.9 727.2 $16.8 $202.1

2028 13.0 141.0 73.4 797.1 $18.2 $220.3

2029 13.8 155.7 77.7 878.4 $18.8 $239.1

2030 14.0 169.3 79.3 955.7 $19.1 $258.2

2031 13.8 182.5 78.0 1,031.5 $18.0 $276.2

2032 13.2 195.1 74.8 1,103.7 $17.1 $293.3

2033 12.4 207.7 70.9 1,175.2 $16.1 $309.4

2034 11.7 219.9 67.0 1,244.1 $15.0 $324.4

2035 11.0 231.4 63.4 1,309.3 $14.1 $338.5

2036 10.4 242.2 60.2 1,370.9 $13.3 $351.8

2037 9.9 252.4 57.5 1,429.4 $12.6 $364.4

2038 9.5 262.1 55.5 1,485.6 $11.6 $376.0

2039 9.2 271.4 53.9 1,540.0 $10.9 $386.9

2040 9.0 280.5 53.2 1,593.6 $9.3 $396.3
41 

For the High Scenario, Figure 19 presents the Commercial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 19. Commercial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐

Generator) – High Scenario

Commercial Sector Achievable Potential (Net‐at‐Generator) – High Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 11.8 11.8 63.2 63.2 $20.5 $20.5

2016 12.3 19.1 66.3 103.0 $21.5 $42.0

2017 13.2 32.2 71.6 181.0 $22.2 $64.3

2018 12.7 44.3 69.2 247.5 $21.2 $85.5

2019 11.8 54.5 65.4 306.4 $20.0 $105.5

2020 11.2 64.9 63.1 365.9 $19.4 $124.9

2021 10.9 75.6 61.6 424.8 $18.4 $143.3

2022 10.8 86.9 61.4 488.3 $18.2 $161.5

2023 11.0 97.6 62.4 549.5 $18.4 $179.9

2024 11.4 109.7 65.2 616.5 $19.1 $199.0

2025 12.2 118.1 69.6 671.2 $20.3 $219.3

2026 13.2 129.2 75.0 738.5 $21.4 $240.7

2027 13.9 144.9 79.3 824.3 $22.4 $263.2

2028 14.3 158.4 81.4 901.6 $23.0 $286.1

2029 14.1 173.5 80.5 985.6 $22.2 $308.4

2030 13.5 186.4 77.2 1,060.3 $21.2 $329.6

2031 12.7 198.7 73.0 1,131.4 $19.0 $348.6

2032 11.9 210.2 68.7 1,198.3 $17.8 $366.4

2033 11.2 222.1 64.9 1,265.9 $16.7 $383.1

2034 10.6 233.4 61.5 1,330.1 $15.6 $398.7

2035 10.1 244.0 58.9 1,390.7 $14.2 $412.8

2036 9.7 254.1 56.9 1,448.8 $12.6 $425.4

2037 9.5 263.8 55.4 1,505.1 $11.3 $436.8

2038 9.2 273.2 54.4 1,560.6 $10.2 $446.9

2039 8.9 282.3 52.9 1,614.4 $9.9 $456.9

2040 8.5 290.7 51.3 1,665.4 $9.8 $466.6
42 

For the Base Scenario, Figure 20 presents the Industrial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 20. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Base Scenario

Industrial Sector Achievable Potential (Net‐at‐Generator) – Base Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 4.4 4.4 25.3 25.3 $6.2 $6.2

2016 4.3 7.1 24.6 40.8 $6.1 $12.3

2017 4.2 11.1 24.4 67.2 $6.0 $18.2

2018 4.5 15.6 26.1 92.9 $6.2 $24.4

2019 4.3 19.9 25.4 118.0 $5.9 $30.3

2020 4.2 23.5 24.9 140.0 $5.7 $36.0

2021 4.2 27.4 24.6 162.5 $5.5 $41.6

2022 4.1 31.7 24.5 188.1 $5.5 $47.1

2023 4.1 35.7 24.5 212.1 $5.5 $52.6

2024 4.2 40.1 25.0 237.9 $5.6 $58.1

2025 4.4 43.7 26.0 261.2 $5.8 $63.9

2026 4.6 47.8 27.5 286.9 $6.1 $70.0

2027 5.0 53.5 29.3 319.0 $6.5 $76.5

2028 5.4 58.5 31.7 348.4 $7.0 $83.5

2029 5.8 64.7 34.1 383.8 $7.1 $90.7

2030 6.2 71.0 36.2 420.6 $7.6 $98.2

2031 6.4 77.3 37.4 457.4 $7.4 $105.7

2032 6.4 83.5 37.6 494.4 $7.4 $113.1

2033 6.2 89.6 37.1 531.0 $7.3 $120.4

2034 6.1 95.7 36.4 567.6 $7.2 $127.6

2035 5.9 101.7 35.6 603.7 $7.0 $134.6

2036 5.7 107.5 34.9 639.0 $6.8 $141.4

2037 5.6 113.2 34.2 673.6 $6.6 $148.0

2038 5.4 118.7 33.6 707.3 $6.5 $154.5

2039 5.3 124.1 33.0 740.4 $6.3 $160.8

2040 5.2 129.3 32.4 772.8 $6.2 $167.0
43 

For the Low Scenario, Figure 21 presents the Industrial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 21. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Low Scenario

Industrial Sector Achievable Potential (Net‐at‐Generator) – Low Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 3.4 3.4 20.9 20.9 $6.6 $6.6

2016 3.3 5.5 19.9 31.7 $6.3 $12.9

2017 3.2 8.2 19.3 53.1 $6.1 $19.0

2018 3.5 11.7 21.2 74.3 $6.6 $25.6

2019 3.4 15.2 20.9 95.6 $6.3 $31.9

2020 3.4 18.4 20.8 113.2 $6.2 $38.2

2021 3.4 21.5 20.7 131.5 $6.1 $44.3

2022 3.4 24.7 20.6 153.1 $6.1 $50.4

2023 3.4 27.9 20.7 173.2 $6.1 $56.6

2024 3.4 31.3 21.2 195.1 $6.2 $62.8

2025 3.6 34.4 21.9 216.7 $6.4 $69.2

2026 3.8 37.6 23.1 237.7 $6.8 $76.0

2027 4.0 42.2 24.5 264.9 $7.2 $83.1

2028 4.3 46.2 26.4 289.2 $7.7 $90.9

2029 4.7 51.2 28.5 318.9 $7.7 $98.5

2030 5.1 56.8 30.8 351.8 $8.3 $106.8

2031 5.4 62.0 32.6 383.9 $8.5 $115.4

2032 5.5 67.4 33.6 416.9 $8.8 $124.2

2033 5.5 72.6 33.9 449.8 $8.9 $133.0

2034 5.5 78.1 33.8 483.4 $8.8 $141.8

2035 5.4 83.5 33.5 517.0 $8.7 $150.5

2036 5.3 88.9 33.1 550.4 $8.5 $159.0

2037 5.2 94.2 32.7 583.4 $8.4 $167.4

2038 5.1 99.4 32.3 616.1 $8.3 $175.6

2039 5.0 104.5 31.9 648.3 $8.1 $183.8

2040 5.0 109.5 31.5 679.9 $8.0 $191.8
44 

For the Mid Scenario, Figure 22 presents the Industrial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 22. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

Mid Scenario

Industrial Sector Achievable Potential (Net‐at‐Generator) – Mid Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 5.6 5.6 31.7 31.7 $7.3 $7.3

2016 5.6 9.4 31.7 54.3 $7.4 $14.8

2017 5.7 15.1 32.3 88.7 $7.5 $22.3

2018 5.9 20.8 33.9 121.6 $7.5 $29.8

2019 5.6 26.1 32.6 153.2 $7.1 $36.9

2020 5.4 31.0 31.7 182.2 $6.9 $43.8

2021 5.3 36.0 31.1 211.5 $6.5 $50.4

2022 5.2 41.5 30.8 243.6 $6.5 $56.8

2023 5.2 46.6 30.9 274.2 $6.5 $63.3

2024 5.3 52.2 31.7 306.8 $6.6 $69.9

2025 5.6 56.2 33.1 332.5 $6.9 $76.8

2026 6.0 61.7 35.3 366.0 $7.3 $84.1

2027 6.5 68.8 37.9 406.5 $7.9 $92.0

2028 7.0 75.4 40.8 444.6 $8.5 $100.5

2029 7.4 83.1 43.1 489.1 $8.6 $109.1

2030 7.6 90.5 44.5 533.2 $8.9 $117.9

2031 7.6 98.0 44.7 577.2 $8.3 $126.2

2032 7.4 105.2 44.0 620.6 $8.2 $134.4

2033 7.2 112.4 42.9 663.5 $7.9 $142.3

2034 6.9 119.5 41.8 705.9 $7.7 $150.0

2035 6.7 126.3 40.7 747.2 $7.5 $157.5

2036 6.5 132.9 39.8 787.2 $7.3 $164.8

2037 6.3 139.3 38.9 826.2 $7.1 $172.0

2038 6.1 145.4 38.2 864.2 $7.0 $178.9

2039 6.1 151.5 37.6 901.7 $6.5 $185.4

2040 5.9 157.4 37.0 938.7 $6.4 $191.8
45 

For the High Scenario, Figure 23 presents the Industrial Sector Incremental and Cumulative (Net‐at‐

Generator) Achievable Potential in terms of Peak Demand Savings (MW) and Energy Savings (GWh)

from energy efficiency programs, 2015 through 2040, along with Program Administrator Cost ($

millions).

Figure 23. Industrial Sector Energy Efficiency Achievable Potential – 2015 to 2040 (Net‐at‐Generator) –

High Scenario

Industrial Sector Achievable Potential (Net‐at‐Generator) – High Scenario

Peak Demand Savings

(MW)

Energy Savings

(GWh)

Program Administrator Costs

($ millions)

Year Incremental   Cumulative Incremental   Cumulative Incremental   Cumulative  

2015 5.8 5.8 32.9 32.9 $8.5 $8.5

2016 6.1 10.1 34.1 57.8 $9.0 $17.5

2017 6.4 16.4 35.7 95.6 $9.3 $26.9

2018 6.5 22.5 37.0 131.1 $9.1 $36.0

2019 6.1 28.1 35.3 164.4 $8.6 $44.6

2020 5.8 33.5 34.2 196.4 $8.3 $52.9

2021 5.7 39.1 33.7 228.6 $7.8 $60.7

2022 5.7 45.1 33.7 263.8 $7.8 $68.5

2023 5.8 50.9 34.2 297.8 $7.9 $76.5

2024 6.1 57.2 35.7 334.4 $8.3 $84.7

2025 6.5 62.0 38.0 364.6 $8.8 $93.5

2026 7.0 68.3 40.8 403.1 $9.3 $102.9

2027 7.4 76.2 43.1 448.4 $9.9 $112.7

2028 7.6 83.4 44.6 490.2 $10.2 $123.0

2029 7.6 91.3 44.8 536.4 $10.0 $132.9

2030 7.4 98.6 44.1 580.0 $9.8 $142.7

2031 7.2 105.7 42.9 622.6 $8.8 $151.5

2032 6.9 112.6 41.7 664.0 $8.5 $160.0

2033 6.7 119.4 40.5 705.2 $8.3 $168.3

2034 6.5 126.1 39.5 745.5 $8.0 $176.3

2035 6.3 132.5 38.7 784.5 $7.8 $184.1

2036 6.2 138.7 38.1 822.7 $7.3 $191.4

2037 6.1 144.8 37.6 860.2 $7.1 $198.5

2038 6.0 150.8 37.0 897.3 $7.0 $205.6

2039 5.8 156.7 36.2 933.8 $6.9 $212.5

2040 5.6 162.3 35.4 969.1 $6.8 $219.3
46 

Appendix D. Glossary of Terms

Achievable Potential: the amount of energy use that efficiency through DSM programs can realistically

be expected to displace assuming the most aggressive program scenario possible (such as providing end‐

users with payments for the entire incremental cost of more efficient equipment). This is often referred to

as maximum achievable potential. Achievable potential takes into account real‐world barriers to

convincing end‐users to adopt efficiency measures, the non‐measure costs of delivering programs (for

administration, marketing, tracking systems, monitoring and evaluation, etc.), and the capability of

programs and administrators to ramp up program activity over time.

Cost‐effectiveness: a measure of the applicable economic effects resulting from the implementation of

an energy efficiency measure. If the benefits outweigh the cost, the measure is said to be cost‐effective.

  

Cumulative Annual: refers to the overall savings occurring in a given year from both new participants

and savings continuing to result from past participation with measures that are still in place. Cumulative

annual does not always equal the sum of all prior year incremental values as some measures have

relatively short measure lives and, as a result, their savings drop off over time.

Dual Baseline: A dual baseline approach uses a less efficient baseline in the first part of its measure life

based on the remaining useful life (RUL), resulting in higher energy savings initially, with savings

becoming lower at a point later in the measure life when the measure effective useful life (EUL) would

have resulted in the measure reaching the end of its useful life. At the end of what would have been the

end of the measure’s EUl, Measure costs also are adjusted.

Early Replacement: refers to an energy efficiency measure normally regarded as ROB is installed before

the effective life of the measure it is replacing is reached.

Economic Potential: the subset of the technical potential screen that is economically cost‐effective as

compared to conventional supply‐side energy resources. Both technical and economic potential screens

are theoretical numbers that assume immediate implementation of efficiency measures, with no regard

for the gradual “ramping up” process of real‐life programs. In addition, they ignore market barriers to

ensuring actual implementation of efficiency. Finally, they only consider the costs of efficiency measures

themselves, ignoring any programmatic costs (such as marketing, analysis, administration) that would

be necessary to capture them.  

Effective Useful Life (EUL): the number of years (or hours) that the new energy efficient equipment is

expected to function based on the assumed or average general life of the measure. Useful life is also

commonly referred to as “measure life.”

Emerging Technology: An energy efficiency measure just entering or about to enter the marketplace.

End‐use: a category of equipment or service that consumes energy (e.g., lighting, refrigeration, heating,

process heat).  

Energy Efficiency: using less energy to provide the same or an improved level of service to the energy

consumer in an economically efficient way. Sometimes “conservation” is used as a synonym, but that
47 

term is usually taken to mean using less of a resource even if this results in a lower service level (e.g.,

setting a thermostat lower or reducing lighting levels). This distinction recognizes that energy efficiency

includes using less energy at any time, including at times of peak demand through demand response

and peak shaving efforts.

Free Rider: participants in an energy efficiency program who would have adopted an energy efficiency

technology or improvement in the absence of a program or financial incentive.

Incremental: savings or costs in a given year associated only with new installations happening in that

year.

Market Characterization: refers to evaluations focused on program‐induced market effects when the

program being evaluated has a goal of making longer‐term lasting changes in the way a market operates.

These evaluations examine changes within a market that are caused, at least in part, by the energy

efficiency programs attempting to change that market.

Measure: any action taken to increase efficiency, whether through changes in equipment, control

strategies, or behaviour. Examples are higher‐efficiency central air conditioners, occupancy sensor

control of lighting, and retro‐commissioning. In some cases, bundles of technologies or practices may be

modeled as single measures. For example, an ENERGY STAR® home package may be treated as a single

measure.  

Megawatt (MW): a unit of electrical output, equal to one million watts or one thousand kilowatts. It is

typically used to refer to the output of a power plant.  

Megawatt‐hour (MWh): one thousand kilowatt‐hours, or one million watt‐hours. One MWh is equal to

the use of 1,000,000 watts of power in one hour.

New Construction: A measure or package of measures is installed at the time of construction.

Net‐to‐gross (NTG) Ratio: a factor representing net program savings divided by gross program savings

that is applied to gross program impacts to convert them into net program load impacts

Portfolio: either a collection of similar programs addressing the same market, technology, or

mechanisms; or the set of all programs conducted by one organization.

Program: a mechanism for encouraging energy efficiency. May be funded by a variety of sources and

pursued by a wide range of approaches. Typically includes multiple measures.

Program Potential: the efficiency potential possible given specific program funding levels and designs.

Often, program potential studies are referred to as “achievable” in contrast to “maximum achievable.”

Replacement on Burnout (ROB): a measure is not implemented until the existing technology it is

replacing fails. An example would be an energy efficient water heater being purchased after the failure

of the existing water heater.
48 

Remaining Useful Life (RUL): The length of time a measure is expected to remain in operation (the

length of time until its effective useful life is at an end).

Retrofit: refers to an efficiency measure or efficiency program that seeks to encourage the replacement of

functional equipment before the end of its operating life with higher‐efficiency units (also called “early

retirement”) or the installation of additional controls, equipment, or materials in existing facilities for

purposes of reducing energy consumption (e.g., increased insulation, low flow devices, lighting

occupancy controls, economizer ventilation systems).

Technical Potential: the theoretical maximum amount of energy use that could be displaced by

efficiency, disregarding all non‐engineering constraints such as cost‐effectiveness and the willingness of

end‐users to adopt the efficiency measures. It is often estimated as a “snapshot” in time assuming

immediate implementation of all technologically feasible energy saving measures, with additional

efficiency opportunities assumed as they arise from activities such as new construction.

   
49 

Appendix E. EERAM Economic Tests

Measure, program, end‐use, building type, and overall portfolio level costs and benefits are calculated in

EERAM. Some of these costs and benefits are calculated as net and others as gross. Net values take into

account free riders by using net‐to‐gross adjustment values. The costs and benefits calculated include:

• Administrative costs (always gross)

• Avoided cost benefits (always net)

• Incentive costs (always gross)

• Incremental technology costs (gross or net)

• Utility bill reductions (gross or net)

Within the “Financial Tests” worksheet, these streams of costs and benefits are converted to a net present

value using the discount rate input in the “Summary Parameters” worksheet. Using this data, four

financial tests are calculated. These include:

Total Resource Cost (TRC)21,22,23: The TRC test measures the net resource benefits from the perspective of

all ratepayers by combining the net benefits of the program to participants and non‐participants. The

benefits are meant to be the sum of the avoided costs of the supply‐side resources avoided or deferred,

and other benefits that accrue to participants or the utility.24 The TRC costs encompass the cost of the

measures/equipment installed, and the costs incurred by the program administrator. The formulation:

TRC = Benefits / Costs where:

 Benefits = avoided costs + other utility benefits + other participant benefits

 Costs = administrative costs + net incremental technology costs  

Program Administrator Cost Test (PAC)21: Sometimes referred to as the utility cost test, this test

compares the utilityʹs avoided cost benefits with energy efficiency program expenditures (incentives

plus administrative costs). The formulation:

PAC = Benefits / Costs where:

 Benefits = avoided costs + other utility benefits

 Costs = administrative costs + incentives

                                                          

21 California Standard Practice Manual: Economic Analysis of Demand‐Side Programs and Projects. October 2001.

http://www.energy.ca.gov/greenbuilding/documents/background/07‐

J_CPUC_STANDARD_PRACTICE_MANUAL.PDF

22 CPUC D0606063, Attachment 9. http://www.cpuc.ca.gov/NR/rdonlyres/101F0713‐7277‐43A8‐883D‐

8EF2712EFA8A/0/NumericalExamplesNTGAdjtoTRCD0709043.pdf

23 CPUC http://docs.cpuc.ca.gov/published/final_decision/73172‐10.htm 24 For purposes of this study, only avoided generation investments and fuel purchases were included in the benefits.
50 

Ratepayer Impact Test (RIM)21: This test measures what happens to customer bills or rates due to

changes in utility revenue and operating costs caused by the program. The formulation:  

RIM = Benefits / Costs where:

 Benefits = avoided costs

 Costs = administrative costs + incentives  + net bill reductions

Participant Cost Test (PCT)21: This test measures the quantifiable benefits and costs to the customer due

to participation in the program. The formulation:  

PCT = Benefits / Costs where:

 Benefits = incentives  + gross bill reductions  

 Costs = gross incremental technology costs  

In the 2012 model for the PCT, instead of gross benefits and costs, net benefits and costs were used.

Overall, the effect and resulting calculations are the same between the two (gross or net) approaches to

valuing benefits and costs in the PCT

Figure 24 presents the formula for each of the four benefit/cost tests.    

Figure 24. Benefit/Cost Test Formulas

Cost Test Formula Key of Terms 

Program Administrator 

Cost Test (PAC) 

PAC = (A + B) / (D + E) A = PV Avoided Costs 

(always net) 

E = PV Incentive Costs

(always gross) 

Participant 

Cost Test (PCT) 

PCT = (E + gross G) / gross F B = PV Other Utility Benefits 

(always net) 

F = PV Technology Costs 

(gross or net) 

Rate Impact Measure 

Cost Test (RIM) 

RIM = A / (D + E + net G) C = PV Other Participant Benefits 

(always net) 

G = PV Bill Reductions 

(gross or net) 

Total Resource 

Cost Test (TRC) 

TRC = (A + B + C) / (E + net F) D = PV Administrative Costs 

(always gross) 

PV = Present Value
51 

Appendix F.     2012 Baseline Study

Nova Scotia 2012 Baseline Study:

Residential Sector &

Business, Nonprofit and Institutional Sector  

Presented to

November 20th, 2013

Presented by

Todd Williams, Managing Director

333 Bay Street, Suite 1250

Toronto, ON M5H 2R2

647.288.5204, TWilliams@Navigant.com

Stu Slote, Associate Director (Project Manager)

802.526.5113, Stu.Slote@Navigant.com

Nick Beaman, Managing Consultant (Baseline Study Lead)

802.526.5107, Nick.Beaman@Navigant.com  

Navigant

www.navigant.com
i 

Table of Contents

Executive Summary ...................................................................................................................1

E.1 Study Objectives .................................................................................................................................. 1

E.2 Study Methods..................................................................................................................................... 1

E.3 Key Residential Findings.................................................................................................................... 3

E.4 Key Business, Nonprofit and Institutional (BNI) Findings ......................................................... 10

3. Introduction .....................................................................................................................17

3.1 Purpose of the Study......................................................................................................................... 17

3.2 Organization of the Report .............................................................................................................. 17

3.3 Data Sources....................................................................................................................................... 17

4. Residential Baseline Study Methodology..................................................................19

4.1 Data Sources....................................................................................................................................... 19

4.2 Data Analysis Approach .................................................................................................................. 20

5. Business, Nonprofit and Institutional Baseline Study Methodology.................. 21

5.1 Data Sources....................................................................................................................................... 21

5.2 On‐Site Sampling Approach ............................................................................................................ 21

5.3 Data Collection Approach................................................................................................................ 22

5.4 Data Analysis Approach .................................................................................................................. 22

6. Residential Market Characterization..........................................................................24

6.1 Residential Home Basic Characteristics ......................................................................................... 24

6.2 Residential Building Shell ................................................................................................................ 27

6.3 Residential Lighting.......................................................................................................................... 31

6.4 Residential HVAC............................................................................................................................. 32

6.5 Residential Plug Loads ..................................................................................................................... 34

6.6 Residential Water Heating ............................................................................................................... 78

6.7 Residential Appliances ..................................................................................................................... 82

7. Business, Nonprofit and Institutional Market Characterization...........................88

7.1 BNI General Building Information ................................................................................................. 88

7.2 BNI Building Envelope..................................................................................................................... 89

7.3 BNI Lighting....................................................................................................................................... 98

7.4 BNI HVAC and Water Heating..................................................................................................... 103

7.5 BNI Heating Systems ...................................................................................................................... 109

7.6 BNI Cooling Systems ...................................................................................................................... 114
ii 

7.7 BNI HVAC Controls ....................................................................................................................... 116

7.8 BNI Refrigeration ............................................................................................................................ 121

7.9 BNI Motors and Compressed Air ................................................................................................. 123

Appendix A. References..................................................................................................129

Appendix B. Residential On‐Site Data Collection Tool ..........................................130

Appendix C. Commercial and Industrial On‐Site Data Collection Tool..............153
iii 

List of Figures

Figure 1. Residential Summary of Data Sources...................................................................................................2

Figure 2. Residential Dwelling Type Profile .........................................................................................................3

Figure 3. Residential Building Shell Profile...........................................................................................................4

Figure 4. Residential Lighting Characterization Profile ......................................................................................5

Figure 5. Residential Heating Equipment Profile.................................................................................................6

Figure 6. Residential Cooling Equipment Profile.................................................................................................6

Figure 7. Residential Plug Load Profile .................................................................................................................7

Figure 8. Residential Water Heating Profile..........................................................................................................8

Figure 9. Residential Appliances Profile................................................................................................................9

Figure 10. BNI General Buildings Profile ............................................................................................................10

Figure 11. BNI Building Shell Profile ...................................................................................................................11

Figure 12. BNI Indoor Lighting Profile................................................................................................................12

Figure 13. BNI Outdoor Lighting Profile.............................................................................................................13

Figure 14. BNI HVAC Profile................................................................................................................................14

Figure 15. BNI Refrigeration Profile.....................................................................................................................15

Figure 16. BNI Motors and Compressed Air Profile..........................................................................................16

Figure 17. BNI Summary of Data Sources ...........................................................................................................18

Figure 18. Residential Sample Design..................................................................................................................19

Figure 19. BNI Sample Design ..............................................................................................................................21

Figure 20. BNI Sample Distribution by Facility Square Footage......................................................................22

Figure 21. Residential Dwellings Profile..............................................................................................................24

Figure 22. Residential Dwelling Type..................................................................................................................25

Figure 23. Residential Year of Construction........................................................................................................26

Figure 24. Residential Building Shell Profile.......................................................................................................28

Figure 25. Residential Exterior Wall Construction and Insulation Profile......................................................29

Figure 26. Residential Ceiling Insulation R‐Values............................................................................................30

Figure 27. Residential Lighting Location by Wattage........................................................................................31

Figure 28. Residential Lighting Profile ................................................................................................................32

Figure 29. Residential Heating Equipment Profile.............................................................................................32

Figure 30. Residential Cooling Equipment Profile.............................................................................................33

Figure 31. Residential Plug Loads Profile............................................................................................................34

Figure 32. Televisions and Computers per Household .....................................................................................76

Figure 33. Television Display Types and Computer Types and Percent ENERGY STAR®..........................76

Figure 34. Terminology for Home Entertainment System Components.........................................................77

Figure 35. Residential Water Heating Profile......................................................................................................78

Figure 36. Residential Average Water Heating Capacity..................................................................................79

Figure 37. Residential Water Heating Capacity Distribution: Percent of Overall Distribution...................79

Figure 38. Residential Water Heating Location by Fuel Type ..........................................................................80

Figure 39. Residential Water Heating Auxiliary Measures Profile ..................................................................80

Figure 40. Residential Water Heater Temperature Settings..............................................................................81
iv 

Figure 41. Residential Faucet Aerators Profile....................................................................................................81

Figure 42. Residential Appliances Profile............................................................................................................82

Figure 43. Refrigerators Profile .............................................................................................................................83

Figure 44. Residential Primary Refrigerator Types............................................................................................84

Figure 45. Residential Size of Refrigerators.........................................................................................................84

Figure 46. Residential Age of Refrigerators ........................................................................................................85

Figure 47. Residential Freezers Profile.................................................................................................................86

Figure 48. Residential Freezer Type Distribution...............................................................................................86

Figure 49. Residential Freezer Age Distribution ................................................................................................87

Figure 50. BNI General Buildings Profile ............................................................................................................88

Figure 51. BNI Building Shell Profile ...................................................................................................................90

Figure 52. BNI Exterior Wall Construction Types..............................................................................................91

Figure 53. BNI Exterior Wall Insulation Types...................................................................................................92

Figure 54. BNI Roof Construction Types, Surfaces, Finishes, and Colours ....................................................93

Figure 55. BNI Floor Types....................................................................................................................................94

Figure 56. BNI Window Panes ..............................................................................................................................94

Figure 57. BNI Window Glazing Type.................................................................................................................95

Figure 58. BNI Window Glazing Features...........................................................................................................95

Figure 59. BNI Window Frame Types..................................................................................................................96

Figure 60. BNI Window Film ................................................................................................................................96

Figure 61. BNI Window Exterior Solar Shade.....................................................................................................97

Figure 62. BNI Window Interior Shades..............................................................................................................97

Figure 63. BNI Lighting Location Distribution...................................................................................................98

Figure 64. BNI Indoor Lighting Profile................................................................................................................98

Figure 65. BNI Installed Lamps vs. Installed Wattage.......................................................................................99

Figure 66. BNI Installed Lamps vs. Installed Wattage.......................................................................................99

Figure 67. BNI Distribution of Fixture Type .....................................................................................................100

Figure 68. BNI Fixture Distribution by Building Type ....................................................................................101

Figure 69. BNI Outdoor Lighting Profile...........................................................................................................102

Figure 70. BNI Fixture Type Distribution..........................................................................................................103

Figure 71. BNI Control Types for Sites with Exterior Lights ..........................................................................103

Figure 72. BNI HVAC and Water Heating Profile ...........................................................................................104

Figure 73. BNI Distribution of Single‐Zone and Multi‐Zone Systems ..........................................................106

Figure 74. BNI Distribution of Single‐Zone HVAC Systems ..........................................................................107

Figure 75. BNI Single‐Zone HVAC Maintenance.............................................................................................108

Figure 76. BNI Single‐Zone System Fuel Type .................................................................................................109

Figure 77. BNI Single‐Zone Heating System Type...........................................................................................110

Figure 78. BNI Boiler Delivery System by Type ...............................................................................................111

Figure 79. BNI Boiler Fuel Types by Delivery System.....................................................................................112

Figure 80. BNI Hot Water Circulation Pump Distribution by Size................................................................113

Figure 81. BNI Hot Water Circulation Pump Speed Control Type................................................................113

Figure 82. BNI Types of Cooling for Single‐Zone HVAC ...............................................................................114

Figure 83. BNI Distribution of Sizes for Single‐Zone Cooling........................................................................115

Figure 84. BNI Direct Expansion Average Efficiency (EER) by Size Range..................................................116

Figure 85. BNI HVAC Control Types.................................................................................................................116
v 

Figure 86. BNI Distribution of Exhaust Fan Control Type..............................................................................117

Figure 87. BNI Distribution of Exhaust Fan Drive Speed Type......................................................................117

Figure 88. BNI Water Heater Type Distribution...............................................................................................118

Figure 89. BNI Distribution of Fuel Types Among Water Heaters................................................................118

Figure 90. BNI Distribution of Water Heating Temperature Set Point..........................................................119

Figure 91. BNI Distribution of Water Heating Insulation ...............................................................................120

Figure 92. BNI Water Heat Recirculation Pump...............................................................................................120

Figure 93. BNI Refrigeration Profile...................................................................................................................121

Figure 94. BNI Refrigeration Types....................................................................................................................121

Figure 95. BNI Refrigeration Equipment Types Distribution.........................................................................122

Figure 96. Non‐BNI Type Refrigeration Equipment Types Distribution......................................................122

Figure 97. BNI Motors and Compressed Air Profile........................................................................................123

Figure 98. BNI Distribution of Process Motor Types .......................................................................................124

Figure 99. BNI Motor Controls, Efficiency and NEMA Type .........................................................................124

Figure 100. BNI Total Motor Horsepower by Type .........................................................................................125

Figure 101. BNI Distribution of Air Compressor Type....................................................................................125

Figure 102. BNI ‘Other Commercial’ Air Compressor Hours of Weekly Use by Horsepower..................126

Figure 103. BNI Industrial Air Compressor Hours of Weekly Use by Horsepower...................................127

Figure 104. BNI Air Dryer Verification by Air Compressor Type .................................................................128

   
2012 Baseline Study Page 1 

Executive Summary

E.1 Study Objectives

The primary objectives of this study were to:

1. Develop data by sector and end use that is specific to the Province of Nova Scotia.  

2. Develop data to inform the Nova Scotia energy efficiency potential study.

E.2 Study Methods

Market Assessment Approach

The industry practice in developing market characterizations for demand‐side management (DSM)

assessments and baseline forecasts has been to use, where needed, data from secondary sources such as

Statistics Canada. These data are often used as starting points, with other available data that is more

specific to the jurisdiction being examined used to update these starting point data.

In this assignment, the focus is on the Province of Nova Scotia. Navigant made full use of Efficiency

Nova Scotia Corporation (ENSC) and Nova Scotia Power, Inc. (NSPI) data, including consumption data

by sector and rate class, as well as any demand forecasting and demand‐side management (DSM)

evaluation work by ENSC. To the fullest extent practicable, available Nova Scotia data was used to

inform the assessment, including the 2009 Nova Scotia Residential Baseline Study and the 2011 Nova

Scotia Residential Socket Study.  

Baseline Data Sources

Figure 25 provides a summary of all the data sources used throughout this report. When possible, studies

which included higher statistical confidence and precision were used.  

Residential Sector Baseline

The primary source of data used in determining the residential baseline was a 2012 online general

population panel survey conducted by Corporate Research Associates (CRA), Inc. on behalf of Navigant,

referred to as the Energy Usage Study. On‐site data collection was used to supplement the online study,

as well as other data sources, when needed. Additional data used to calculate baselines and measure

densities were obtained from a recent ENSC study (the 2011 Socket Study) and from NRCan and

Statistics Canada data sets (the Comprehensive Energy Use Database, the 2007 Survey of Household

Energy Use, the 2006 Census and the 2011 Census).

Business, Nonprofit and Institutional (BNI) Sector Baseline

The primary source of data used in determining the BNI baseline was a 2012 telephone business survey

conducted by Corporate Research Associates (CRA), Inc. on behalf of Navigant. On‐site data collection

was used to supplement the survey, as well as other data sources, when needed.  
2012 Baseline Study Page 2 

Sample Design and Segmentation

For the Residential sector, the primary data source consisted of an online survey conducted by CRA on

behalf of Navigant (n=1,501). The online data were supplemented with on‐site surveys with a sample

size of 31 residents. For the BNI sector, the primary data collection approach consisted of on‐site surveys

with a sample size of 30. Due to the small sample size, results from the on‐site surveys are used in a

qualitative manner. Where possible, Navigant has provided supplementary data.

Figure 25. Residential Summary of Data Sources

Source

Label

Source Sample

Size

A Corporate Research Associates, Inc. on behalf of Efficiency Nova Scotia, 2011 Socket Study. (2011). n= 1004

B Corporate Research Associates, Inc. on behalf of Navigant, 2012 Residential Energy Usage Online

Panel Study. (2012).

n=1,501

C Corporate Research Associates, Inc. on behalf of Navigant, Commercial & Industrial Business

Baseline End‐User Telephone Study. (2012).

n=96

D ENERGY STAR website. (2013, April). From

http://www.energystar.gov/?c=home_sealing.hm_improvement_insulation_table

N/A

E Health Canada website. (2013, April). From http://www.hc‐sc.gc.ca/hl‐vs/iyh‐vsv/diseases‐

maladies/legionnaire‐eng.php#a5

N/A

F Natural Resources Canada (NRCan), Office of Energy Efficiency Commercial and Institutional

Building Energy Use Survey 2000. (2002, December). From

http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/data_e/cibeus_description.cfm

n=20,332

G Natural Resources Canada (NRCan), Office of Energy Efficiency 2007 Survey of Household

Energy Use (SHEU‐2007). (2007). From http://oee.rncan.gc.ca/publications/statistics/sheu‐

summary07/sheu.cfm

n=9,773

H Natural Resources Canada (NRCan), Office of Energy Efficiency 2008 Commercial and

Institutional Consumption of Energy Survey. (2008). From

http://oee.nrcan.gc.ca/publications/statistics/cices08/index.cfm

n=3,342

I Natural Resources Canada (NRCan), Survey of Commercial and Institutional Energy Use –

Building 2009 (2012, December) From

http://oee.nrcan.gc.ca/publications/statistics/scieu09/scieu_e.pdf

n=5,704

J Natural Resources Canada (NRCan), Comprehensive Energy Use Database, 1990 to 2010.

(2012).From http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/comprehensive_tables/list.cfm

N/A

K Statistics Canada, 2006 Census of Population: Statistics Canada Catalogue no 97‐554‐XCB2006022.

(2006). From https://www12.statcan.gc.ca/census‐recensement/2006/dp‐pd/index‐eng.cfm

N/A

L Statistics Canada, 2011 Census of Population: Statistics Canada Catalogue no.98‐313‐XCB2011022.

(2011). From http://www12.statcan.gc.ca/census‐recensement/2011/dp‐pd/index‐eng.cfm

N/A

M Residential On‐Site Survey n=31

N BNI On‐Site Survey n=30

O Navigant Consulting, on behalf of Nova Scotia Power Incorporated, 2009 Nova Scotia Residential

Baseline Study. (2010).   

n=68
2012 Baseline Study Page 3 

E.3 Key Residential Findings

Basic Home Characteristics

This section summarizes the basic home characteristics of residential dwellings in Nova Scotia. Figure 26

summarizes the findings for basic home characteristics.  

Figure 26. Residential Dwelling Type Profile  

Measure/Characteristic Market Characterization Source

Dwelling Type 75% of all dwellings are single‐family (detached

and attached) homes.

B

Dwelling Age The majority (58%) of all dwellings in Nova Scotia

were built after 1971, and the majority of single‐

family homes (58%) were also built after this date.

K

Dwelling Size The average dwelling floor space for all dwelling

types is 1,550 square feet and 1,765 square feet for

single‐family homes.

J

Nearly three‐quarters of all dwellings are between

1,000 and 3,500 square feet.

B

Dwelling Height

(Number of Stories)

55% of all dwellings are at least two stories tall,

29% are single‐story, and 10% are split‐entry.

B
2012 Baseline Study Page 4 

Building Shell

This section provides a summary of the residential building shell measures present at the 31 surveyed

homes. Figure 27 summarizes the key findings of building shell measures.  

Figure 27. Residential Building Shell Profile

Measure/Characteristic Key Findings Source

Wall Construction Types For homes older than 20 years, 66% have 2x4 construction and

31% have 2x6 construction.

For newer homes, 84% reported 2x6 construction, 11% have

adiabatic or shared walls, and the remaining 5% are concrete

block walls.

M

Above Grade Wall

Insulation

10% of homes are both older than 20 years old and have loose‐

fill wall insulation. Over time, this type of insulation can

gravitate to the bottom of wall cavities and become very

ineffective.

< 2% of homes have no wall insulation at all.

M

Above Grade Wall

Insulation R‐Value

Over 73% of residential homes reported having batt insulation,

but R‐Value varied depending on wall construction type.

2x4 construction had an average insulation R‐Value of 13.2.

2x6 construction had an average insulation R‐Value of 16.5.

(These averages did not vary by home vintage.)

M

Ceiling Insulation Depth 16% of residential homes have less than 5ʺ of ceiling insulation.

6% of these homes have no ceiling insulation at all.  

M

Ceiling Insulation R‐

Value

Average insulation R‐Value for homes older than 20 years is

19.3.

Homes newer than 20 years have average insulation R‐Value of

27.4.

M

Increasing Ceiling

Insulation

Based on survey findings, there seem to be significant savings

available in increasing the ceiling insulation levels of homes

older than 20 years.

M

Roof Construction and

Colour

98% of homes have asphalt shingle roofs.

Over 85% of roofs are dark coloured, while 11% are light

coloured.

M

Single‐Pane Windows The majority of windows are single pane in 5% of homes in

Atlantic Canada.

G

Low‐E Tinted Windows The majority of windows are Low‐E in 11% of homes in Atlantic

Canada.

G
2012 Baseline Study Page 5 

Residential Lighting

A summary of the distribution of all lighting types for both interior and exterior lamps is shown in

Figure 28. There was an average of 51 lamps installed per household surveyed with a variety of different

lamp types ranging from incandescent, CFL, linear fluorescent, and LED, among others.  

Figure 28. Residential Lighting Characterization Profile

Measure/Characteristic Distribution Source

Incandescent Incandescent lighting represents 51% of all indoor lamps

installed.

A

Halogen Halogen lamps represent 10% of the total lamps installed and

16% of the total wattage installed.

M

CFL CFLs represent 43% of all lamps installed and 19% of the total

installed wattage.

A

Fluorescent Lighting 43% of homes in Atlantic Canada use fluorescent tubes. Of this

43%, the majority (71%) have 5 or fewer such fixtures installed in

their homes.

G 

LED LED lamps represent 4% of the total installed lamps and less

than 1% of the total installed wattage.

A

Other / Don’t Know Other lamps, including unidentifiable lamp types, represent less

than 0.5% of the total lamps installed and less than 0.25% of the

total wattage.

M
2012 Baseline Study Page 6 

Residential HVAC

This section summarizes the details of the residential space heating systems. Figure 29 summarizes the

key findings for residential heating equipment

Figure 29. Residential Heating Equipment Profile

Measure/Characteristic Key Findings Source

Electric Heating ‐ 

Primary

31% of all primary heating systems are electric. B

Electric Heating ‐ 

Backup

59% of households have a secondary heating

system installed. Of these, 32% are electric

baseboard or room units.

G

Fuel Oil Heating 46% of primary heating systems are fuel oil. B

Programmable

Thermostats

27% of all primary systems currently have a

programmable thermostat.

B

Primary Heating

System Maintenance

Just over 40% of all primary heating systems

have been maintained within the last year.  

32% have never been serviced.

M

A summary of air‐conditioning systems is shown in Figure 30.  

Figure 30. Residential Cooling Equipment Profile

Measure/Characteristic Key Findings Source

Electric Fans 42% of residential homes have electric fans as

their primary cooling system.

B

Room Air

Conditioning (RAC)

18% of residential homes report owning air

conditioning units

B

Electric Heat Pump 5% of residential homes had electric heat pumps

as their primary cooling system.

B

Central Air

Conditioning

3% of residential homes have central air

conditioning as their primary cooling system.

B
2012 Baseline Study Page 7 

Residential Plug Loads

Figure 31 presents details on the plug load electrical technologies. Included are televisions,

entertainment boxes [DVD, digital video recorder (DVR), video games], and computers and computer

peripherals.  

Figure 31. Residential Plug Load Profile

Measure/Characteristic Key Findings Source

ENERGY STAR®

Televisions  

47% of televisions are ENERGY STAR® rated.   G

Television Hours of

Use  

Televisions had an average daily reported use of 3.4

hours.

M

Televisions on Energy

Management Systems

(EMS)

29% of all televisions are plugged into a power strip

that can be manually turned off at night.  

M

Entertainment Boxes   There are 2.76 entertainment boxes per residential

household. These are defined as DVD players,

Digital Video Recorders (DVRs), and video game

systems.

B

Digital Video

Recorders (DVRs)

There are 0.63 DVRs per residential household, and

51% of homes have at least one DVR.

B

Entertainment Box

Hours of Daily Use

DVRs and video game systems have an average daily

use of over 8 hours.  

B

Entertainment Boxes

on Energy

Management Systems

(EMS)  

22% of all entertainment boxes are plugged into a

power strip that can be manually turned off at night.  

M

Smart Power Strips for

Plug Loads

Navigant found no smart power strips monitoring

vampire25 energy loads of residential plug load

equipment. This looks to be an area of opportunity

for ENSC.

M

                                                          

25 Various appliances and electronic devices that are ready to operate or in standby mode act like “vampires” silently

using energy even when they are turned “off.” This energy, known as standby or phantom energy loss, represents a

relatively small percentage of a home’s electricity use.  
2012 Baseline Study Page 8 

Residential Water Heating

Figure 32 presents details on the water heating equipment present in the surveyed homes.  

Figure 32. Residential Water Heating Profile

Measure /

Characteristics

Key Findings Source

Electric Water

Heating  

61% of surveyed primary water heaters were electric resistance. B

The average capacity for electric resistance water heaters was 38

gallons. 88% of electric resistance water heaters were located in

conditioned spaces, the majority of which (64%) were set at a

medium temperature setting.

M

Non‐Electric

Water Heating  

39% of water heaters surveyed had fuel types other than electric

resistance (ER). The most common of these were fuel oil, which

constituted 35% of all water heaters.

B

The average capacity was 38 gallons, and about 53% of the non‐

electric water heaters were located in conditioned spaces. 58% of

these water heaters were set at a medium temperature setting.

M

Secondary

Water Heaters  

Of the 1500 households surveyed online, 76 contained a

secondary water heater. The water heaters were electric

resistance, oil, solar, propane, and other.  

B

Water Heater

Capacity  

The average capacity of water heating systems is 38 gallons. The

minimum capacity of surveyed systems was 15 gallons, while

the maximum capacity was 60 gallons.

M

Water Heater

Location   

72% of all residential water heaters are located in conditioned

space.  

M

Auxiliary

Measures  

Tank and pipe wrap were the only auxiliary measures installed

on surveyed water heaters. These measures are present on 28%

and 26% of water heaters26, respectively27.

B

Tank

Temperature  

Nearly 62% of all residential water heaters are set at a medium

(125°F) setpoint. Another 18% are set at a high (150°F) setpoint,

while another 14% are set at a low (90°F) setpoint. Temperature

setpoints are unknown for 7% of water heaters, all of which are

fuel oil.

M

Residential Appliances

This section presents findings for residential appliances. Figure 33 summarizes the findings for all

residential appliances.  

                                                          

26 According to the 2012 Energy Use survey, 28% of respondents (regardless of fuel type) had tank wrap on their

water heater, 26% had pipe wrap, and 3% used water heater timers.

27 It should be noted that the majority of new water heaters installed typically have insulation integral to the tank.
2012 Baseline Study Page 9 

Figure 33. Residential Appliances Profile

Measure /

Characteristic

Key Findings Source

Residential

Range

Cooktops

95% of all Residential ranges are electric B

8% of residential ranges are ENERGY STAR® rated. M

Residential

Ovens

All homes have an oven; 96% are electric.   B

Of the electric ovens, 7% are convection and the rest are standard.

The average age of ovens is 10 years old.

M

Residential

Dishwashers

64% of residential dwellings contain a dishwasher. B

20% of dishwashers are ENERGY STAR® rated.

18% of dishwasher owners said they used the Energy Saver option

all of the time, while another 16% said they only use it some of the

time. 66% use the Heat Booster option all of the time.

The average age of dishwashers is nearly 10 years old.

M

Residential

Clothes

Washers

98% of households own a clothes washer, and 31% are front

loading.  

B

The majority (65%) of households in Atlantic Canada complete

between two to three (28%) and four to seven (37%) loads of

laundry per week in both summer and winter.

G

Residential

Clothes Dryers

96% of homes own a clothes dryer. B

The majority (62%) of households dry between two and three

(26%) and four and seven (36%) loads of laundry during winter

weeks.  

The majority (62%) of households dries between one or less (38%)

and two and three (24%) loads of laundry during summer weeks.

G
2012 Baseline Study Page 10 

E.4 Key Business, Nonprofit and Institutional (BNI) Findings

It should be noted that certain results below were derived from a sample size of 30 on‐site visits and

should be used in a qualitative manner. Where possible, Navigant has used results from additional

primary sources.  

BNI Sector – General Building Information

Surveyors collected information regarding general building characteristics such as age, size, operating

schedules, and more. The following sections present the results for key BNI building characteristics. A

summary of general building information is listed in Figure 34.  

Figure 34. BNI General Buildings Profile

Characteristic Key Findings Source

Facility Age 85% of BNI facilities were built post‐1970 (weighted by floor

space).

I

The average year of construction was between 1970 and 1999

(weighted by floor space).

I

Building Size 41% of all BNI facilities are 5,000 sq. ft. or less.  

14% of facilities were between 5,001 and 10,000 sq. ft.  

18% of facilities were between 10,000 and 25000 sq. ft.  

8% of facilities were between 25,001 and more.

C

Business

Hours

4% of facilities have weekly hours of 36 hours of less, 22%

have hours between 37 and 48, 17% have weekly hours

between 49 and 72 hours, 14% have hours between 73 and 96,

8% have hours between 97 and 120, and 34% have hours

between 121 and 168, weighted by number of facilities.

     I

Building Envelope

This section provides details of the commercial building shell measures present at the on‐site surveyed

commercial buildings envelope types. Figure 35 summarizes the key findings of building shell measures.  
2012 Baseline Study Page 11 

Figure 35. BNI Building Shell Profile

Measure/Characteristic Key Findings Source

Wall Construction Types Considering all commercial exterior wall area, 20% has

concrete block construction, 15% has 2x6 wood frame

construction, 14% has 2x6 metal frame construction, and

14% has adiabatic or shared walls.

N

Exterior Wall Insulation

Types  

43% of all commercial wall area has batt or blanket

insulation.

27% of all commercial wall area does not have any

insulation.

N

Roof Construction Type 46% of commercial roof area is framed (28% with an attic,

18% without an attic).  

31% of commercial roof area is constructed with metal

decking.

N

Roof Surface 33% of commercial roof area has a metal surface.

16% of commercial roofs have an asphalt roll/shingle

surface.

N

Roof Finish 51% of commercial roof area has a flat finish.

24% of commercial roof area has a reflective finish.

N

Roof Colour Only 12% of commercial roof area is dark. N

Floor Types   More than half of commercial floor area is slab‐on‐grade. N

Window Panes   99.6% of commercial window area has two panes. N

Window Glazing Types Grocery/supermarket and health care/hospital facilities

only contain clear window glazing, and most non‐food

retail centers only have clear window glazing.

The majority of industrial facilities and restaurants have

tinted window glazing.

The majority of ‘other commercial’ facilities contain

reflective window glazing.

N

Window Glazing Features The majority of commercial windows do not have any

glazing features, with the exception of health care/hospital

facilities and restaurants which each have about 90% low‐

E and gas‐filled windows.

N

Window Frame Types Commercial window frames are metal, with the main

exception being vinyl windows in health care/hospital

facilities.

N

Window Film   99.8% of commercial window area does not have any

added window film.

N
2012 Baseline Study Page 12 

Lighting

Sixty‐one percent of all lighting types by installed wattage were indoor, while 39 percent were outdoor.  

Indoor Lighting

Figure 36 lists key findings for the BNI indoor lighting profile in Nova Scotia. Findings include high

level results regarding fixture types, wattages, and control types.

Figure 36. BNI Indoor Lighting Profile

Measure/Characteristic Key Findings Source

Fixture Types Fluorescent lights, including both standard and

high performance models, make up 94% of

installed lamps and 89% of installed wattage.  

The second most common lamp type is CFLs,

which make up 3% of installed lamps and 1% of

installed wattage.

N

Wattage The average wattage of indoor lamps is 36 Watts.

The highest average wattage was in industrial

facilities, with an average of 42 Watts per lamp,

followed by ‘Other Commercialʹ sites at an average

of 40 Watts per lamp.

N

Control Type Only 6% of indoor BNI lighting has controls

beyond a standard manual switch. 58% of lighting

is controlled by an on/off switch, while another 32%

is continuous lighting with no controls.

N

   
2012 Baseline Study Page 13 

Outdoor Lighting

Figure 37 lists key findings for the BNI outdoor lighting profile in Nova Scotia. Findings include high

level results regarding fixture types, wattages, and control types.  

Figure 37. BNI Outdoor Lighting Profile

Measure/Characteristic Key Findings Source

Fixture Types One‐quarter of all outdoor fixtures are High

Pressure Sodium Vapor.  

CFLs and Metal Halides each constituted 17% of

the rest of the outdoor lighting fixture types.  

Neon lights made up another 14% of the

distribution.  

The rest of the lighting types made up less than

10% each.

N

Wattage Neon lamps are the dominant lamp type by

wattage, making up over 85% of the distribution.

The average wattage for all outdoor lamp types is

138 watts. Wattages range from 10‐100 Watts,

depending on the lamp.

N

Control Type 82% of sites have motion sensors, photocells, or

time clocks as outdoor lighting controls. Of these

control types, 86% are time clocks.

N

   
2012 Baseline Study Page 14 

HVAC and Water Heating

Figure 38 summarizes the key findings of the BNI HVAC survey.  

Figure 38. BNI HVAC Profile

Measure /

Characteristic

Key Findings Source

HVAC

Single‐Zone

HVAC

System Age

The on‐site surveyors identified 477 single‐zone HVAC systems.

Two‐thirds (66%) of single‐zone HVAC systems are

baseboard/radiant systems, followed by unit ventilators or heaters

(13%).  

N

Primary

Heating Fuel

Type

Nearly two‐thirds (65%) of all single‐zone HVAC systems used

electricity as a fuel type. Fuel oil and liquid propane gas (LPG)

represent 3% each (6% total) of fuel types for single‐zone HVAC

systems.

N

Heating

System Type

More than half (53%) of all facilities sampled had baseboard

heaters. Boilers and electric resistance heaters were in 19% and

10%, respectively, of facilities sampled. Air source heat pumps,

furnaces, and radiant heaters comprise the remaining heating

systems and were in 8% of surveyed facilities.  

N

Boiler

Delivery

Systems

The majority (79%) of boilers were hot water boilers. 21% of boilers

were a steam delivery system. The mean boiler age was 12.6 years.

N

Hot Water

Circulation

Pump Speed

Controls

28% of all pumps were 0.15 horsepower (hp). Pumps that were 0.2,

0.25, 0.75, and 3 hp in size represented 20%, 8%, 8%, and 4% of all

pumps, respectively.

N

Cooling

System Types

According to the Commercial and Institutional Building Energy

Use Survey (CIBEUS) 2000 reported that in Atlantic Canada as a

whole, 60% of C&I buildings had mechanical cooling of some kind.

F

Of the systems that did have a cooling component, 95% were direct

expansion while 5% were chillers. The mean size of all cooling

units was 2.9 tons with a mean efficiency of 9.8 EER and 13 SEER.

N

Water Heating

Water

Heating

Equipment

Types

88% of all water heaters were standard water heaters (i.e., tank

water heaters). 8% of water heaters were instantaneous, or

tankless, water heaters, while 5% were boilers. The average age of

all water heaters was 6.6 years. The average size water heater was

4.1 kW and 96.5 kBtuh.

N

Water

Heating Fuel

Types

The majority of water heaters were fueled by electricity. Five

percent of units were fueled by liquid propane gas (LPG), while

fuel oil and natural gas represented 2.5% each (5% total) of fuel

types for water heaters.

N
2012 Baseline Study Page 15 

Measure /

Characteristic

Key Findings Source

Water

Heating Pipe

and Tank

Insulation

85% of pipes leading into the water heater were not insulated,

while 12.5% were insulated. Similarly, 82.5% of tanks were not

externally insulated (e.g., blanket wrap), while 12.5% were

externally insulated28.

N

Water

Heating

Recirculation

Pump

85% of water heaters did not have recirculation pumps installed,

while 10% did have recirculation pumps installed. 5% of water

heaters were noted as unknown, or “Don’t Know”, if a

recirculation pump was installed.  

N

Refrigeration

Figure 39 lists key findings for the BNI refrigeration profile in Nova Scotia.  

Figure 39. BNI Refrigeration Profile

Measure/Characteristic Key Findings Source

Compressors Compressors were in industrial facilities, restaurants, and

‘other commercial’ facilities. However, insufficient data exists

to properly characterize the market in terms of compressors.29

N

Walk‐in space lighting Nearly 63% of space lighting in BNI walk‐ins is fluorescent,

while another 25% is incandescent. The rest are unknown.30

N

Walk‐in lighting

controls

88% of lighting in BNI walk‐ins is controlled via a wall switch.

The rest of the control types are unknown.

N

Walk‐in motor types A quarter of the motors in BNI walk‐ins are shaded pole, while

the rest are unknown. 14% of motors have evaporative fan

controls.

N

   

                                                          

28 It should be noted that the vast majority of new water heaters installed typically have insulation integral to the

tank.

29 Regardless of the types and capacities of refrigeration compressor in Nova Scotia, significant opportunity exists to

implement compressor system controls, such as free cooling refrigeration economizers, and floating head pressure

control with liquid pressure amplification (LPA). Refrigeration economizer controls turn off the compressor and

evaporator when the outdoor air temperature is at or below the cooler or freezer dry bulb temperature setpoint,

making use of cold outdoor air to meet setpoints with lower power circulation fans to ensure proper air mixing.

Floating head pressure controls on larger refrigeration racks in grocery stores can likewise be an additional source of

annual energy savings.

30 There is significant opportunity for LED lighting savings in refrigerated spaces.
2012 Baseline Study Page 16 

Motors and Compressed Air

Figure 40 provides a summary characterization of motor and compressed air measures for the BNI

segments.  

Figure 40. BNI Motors and Compressed Air Profile

Measure/Characteristic Key Findings Source

Process motor types The majority (62%) of motors are pump

motors and material handling/conveying

motors.

N

Motor controls and

efficiency

All identified motors are standard efficiency

and constant speed.31

N

Air compressor types The majority (82%) of air compressors are

reciprocating two‐stage, single acting (RTS)

units.

N

Air compressor hours

of use

RTS type air compressors are using 73 hp

about 49 hours a week.32

N

Air dryer on

compressed air systems

The majority (90%) of air compressors do

not have an air dryer.

N

                                                          

31 It should be noted that, although no high efficient motors or variable‐speed drives were observed in the facilities

surveyed, it would be expected that a portion of all motors and motor control types would be considered efficient.

32 Compressed air is expensive. End uses can often be served at lower cost using electrically‐powered solutions,

rather than compressed air. Additionally, separating end uses to allow each compressed air system to operate at its

lowest possible air pressure setpoint is another way to reduce costs associated with compressed air use. Navigant

recommends investigation of electrically‐powered alternatives to compressed air use, and recommends lowering air

pressure setpoints for existing systems to the extent possible.
2012 Baseline Study Page 17 

3. Introduction

ENSC has indicated that NSPI may be updating its Integrated Resource Plan (IRP) prior to 2014.

Navigant understands that ENSC requires estimates of DSM potential energy and winter peak demand

savings for a 26‐year period (2015 through 2040) to inform the development of this IRP.  

Navigant developed a 26‐year DSM potential analysis to inform the IRP process. To inform and support

the potential analysis, and to ensure that the assumptions for the current baseline of energy efficiency in

Nova Scotia are current, Navigant conducted a market assessment of the current baseline of energy

efficiency equipment and building characterization. Navigant also reviewed, and as appropriate, updated

measure‐level details that will be included in the potential analysis.  

3.1 Purpose of the Study

The purpose of conducting this study for ENSC includes:

 To develop Nova Scotia baseline information, with saturations of DSM measures in Nova Scotia

for residential and business, nonprofit and institutional buildings. This information will ensure

that the starting point for the DSM potential estimation process is accurate and current.

3.2 Organization of the Report

The remainder of the report includes the following sections:

Section 2 – Residential Baseline Study Methodology

Section 3 – Business, Nonprofit and Institutional Baseline Study Methodology

Section 4 – Residential Market Characterization

Section 5 – Business, Nonprofit and Institutional Market Characterization  

Appendix A – References

Appendix A – On‐site Data Collection Tools

3.3 Data Sources

Figure 41 provides a summary of all the data sources used throughout this report. The primary source of

data used in determining the baseline was the 2012 study conducted by Corporate Research Associates

(CRA), Inc. on behalf of Navigant, referred to as the Energy Usage Study. On‐site data collection was

used to supplement the aforementioned study, as well as other data sources, when needed.  

Additional data used to calculate baselines and measure densities were obtained from a recent ENSC

study (the 2011 Socket Study) and from NRCan and Statistics Canada data sets (the Comprehensive

Energy Use Database, the 2007 Survey of Household Energy Use, the 2006 Census and the 2011 Census).
2012 Baseline Study Page 18 

Figure 41. BNI Summary of Data Sources

Source

Label

Source Sample Size

A Corporate Research Associates, Inc. on behalf of Efficiency Nova Scotia, 2011 Socket

Study. (2011).

n= 1004

B Corporate Research Associates, Inc. on behalf of Efficiency Nova Scotia, 2012

Residential Energy Usage Online Panel Study. (2012).

n=1,501

C Corporate Research Associates, Inc. on behalf of Efficiency Nova Scotia, Commercial &

Industrial Business Baseline End‐User Telephone Study. (2012).

n=96

D ENERGY STAR website. (2013, April). From

http://www.energystar.gov/?c=home_sealing.hm_improvement_insulation_table

N/A

E Health Canada website. (2013, April). From http://www.hc‐sc.gc.ca/hl‐vs/iyh‐

vsv/diseases‐maladies/legionnaire‐eng.php#a5

N/A

F Natural Resources Canada (NRCan), Office of Energy Efficiency Commercial and

Institutional Building Energy Use Survey 2000. (2002, December). From

http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/data_e/cibeus_description.cfm

n=20,332

G Natural Resources Canada (NRCan), Office of Energy Efficiency 2007 Survey of

Household Energy Use (SHEU‐2007). (2007). From

http://oee.rncan.gc.ca/publications/statistics/sheu‐summary07/sheu.cfm

n=9,773

H Natural Resources Canada (NRCan), Office of Energy Efficiency 2008 Commercial and

Institutional Consumption of Energy Survey. (2008). From

http://oee.nrcan.gc.ca/publications/statistics/cices08/index.cfm

n=3,342

I Natural Resources Canada (NRCan), Survey of Commercial and Institutional Energy

Use – Building 2009 (2012, December) From

http://oee.nrcan.gc.ca/publications/statistics/scieu09/scieu_e.pdf

n=5,704

J Natural Resources Canada (NRCan), Comprehensive Energy Use Database, 1990 to

2010. (2012).From

http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/comprehensive_tables/list.cfm

N/A

K Statistics Canada, 2006 Census of Population: Statistics Canada Catalogue no 97‐554‐

XCB2006022. (2006). From https://www12.statcan.gc.ca/census‐recensement/2006/dp‐

pd/index‐eng.cfm

N/A

L Statistics Canada, 2011 Census of Population: Statistics Canada Catalogue no.98‐313‐

XCB2011022. (2011). From http://www12.statcan.gc.ca/census‐recensement/2011/dp‐

pd/index‐eng.cfm

N/A

M Residential On‐Site Survey n=31

N BNI On‐Site Survey n=30
2012 Baseline Study Page 19 

4. Residential Baseline Study Methodology

This section provides an overview of the data sources and methods used to develop the Residential

baseline characterization.

4.1 Data Sources

The primary source of data used in determining the residential baseline was the 2012 study conducted by

Corporate Research Associates (CRA), Inc. on behalf of Efficiency Nova Scotia, referred to as the Energy

Usage Study. On‐site data collection was used to supplement the CRA study, as well as other data

sources, when needed.  

Additional data used to calculate baselines and measure densities were obtained from a recent ENSC

study (the 2011 Socket Study) and from NRCan and Statistics Canada data sets (the Comprehensive

Energy Use Database, the 2007 Survey of Household Energy Use, the 2006 Census and the 2011 Census).

4.1.1 Sample Design and Segmentation

For the residential sector, the primary data source consisted of online surveys conducted by CRA that

were supplemented with on‐site surveys with a sample size of 31 residents. For the BNI sector, the

primary data collection approach consisted of on‐site surveys with a sample size of 30.  

4.1.1.1 Residential On‐Site Surveys

Figure 42 summarizes the residential on‐site sample design for this study. The sample was based on

residential dwelling construction type and fuel type. Although an original sample of 30 was planned, a

total of 31 residential dwellings were included in the final analysis.  

Figure 42. Residential Sample Design

Dwelling Construction Type

Fuel Type

Total

Electricity Oil Other

Multi‐Family and Others 2 2 0 4

Single‐family ‐ Less than 1,500 square feet 3 6 2 11

Single‐family ‐ Larger than 1,500 square feet 5 7 3 15

Total 10 15 5 30

4.1.2 On‐Site Data Collection Approach

4.1.2.1 Residential On‐Site Surveys

On‐site surveys were conducted with 31 Nova Scotia residential dwellings. Surveyors collected detailed

equipment and building information for each residence by inspection. In addition to the on‐site surveys,
2012 Baseline Study Page 20 

Navigant leveraged data collected as part of an on‐site survey to supplement the study conducted by

Corporate Research Associates (CRA) which included 1,501 respondents.  

The on‐site inspections collected a detailed inventory of lighting, HVAC, water heating, refrigeration,

other kitchen appliances, and electronic equipment, and also collected information on building envelope

and site characteristics. Surveys generally took one surveyor two to four hours to complete. The

residential on‐site survey instrument is included in Appendix B.  

4.2 Data Analysis Approach

The approach for data analysis involved quality control, descriptive statistics, comparison of results

across data sets, and summary of corroborated results to describe the market baselines. The analysis

concludes with a discussion of findings especially meaningful to program planning.

All data collected were reviewed for quality control. All on‐site survey responses were reviewed for

validity and completeness by engineers who worked closely with surveyors to ensure data quality

throughout the survey period. Initial analysis of vetted data involved tabulating frequencies of responses

and calculating saturations of standard and efficient equipment by site.  

Baseline results that are corroborated by one or more other data sets are summarized in this report.

Results meaningful to program planning are highlighted and explored in the final section.
2012 Baseline Study Page 21 

5. Business, Nonprofit and Institutional Baseline Study Methodology

This section provides an overview of the data sources and methods used to develop the BNI baseline

characterization.

5.1 Data Sources

The primary data collection approach consisted of on‐site surveys with a sample size of 30 Nova Scotia

businesses.  The surveys collected primary data from Nova Scotia businesses. On‐site surveys collected

in‐depth equipment saturation and building envelope data by inspection of a smaller sample. On‐site

survey participants were recruited randomly from a stratified sample of Nova Scotia businesses. It should

be noted that certain results below were derived from a sample size of 30 on‐site visits. Although the

results obtained represent a 90 percent confidence with +/‐ of 15 percent margin of error, they should be

used in a qualitative manner. Where possible, Navigant has used supplementary data.

Additional data used to calculate baselines and measure densities were obtained from recent ENSC

studies (the 2012 CRA Commercial & Industrial Business Baseline End User Study) and from NRCan and

Statistics Canada data sets (the 2008 Commercial & Institutional Consumption of Energy Survey (CICES)

and the Commercial and Institutional Building Energy Use Survey (CIBEUS) 2000).

5.2 On‐Site Sampling Approach

The sample was designed to represent a broad cross section of Nova Scotia BNI customers in terms of

building usage type and fuel type. Ultimately 30 BNI on‐site surveys were completed.  

5.2.1 BNI On‐Site Surveys

Figure 43 summarizes the residential sample design for this study. The sample was based on building

usage type and fuel type. A total of 30 businesses were included in the final analysis.  

Figure 43. BNI Sample Design

Business Type

Fuel Type

Total

Electricity Oil Other

Other Commercial 7 6 3 16

Restaurant 0 0 2 2

Health Care/Hospital 0 1 1 2

Retail (non‐food) 3 2 2 7

Industrial/Manufacturing   1 0 1 2

Grocery/Supermarket 0 1 0 1

Total 11 10 9 30
2012 Baseline Study Page 22 

It should be noted that, although 30 unique businesses were included in the sample, one business had

two facilities on different meters; therefore, the surveyors considered these as two projects. For this

reason, the total number of facilities included in the analysis will be n=31. Navigant also distributed the

on‐site surveys by the total facility square footage. Figure 44 summarizes the distribution of BNI facilities

by total square footage.  

Figure 44. BNI Sample Distribution by Facility Square Footage

Facility Square Footage Sites Visited

Less than 1,000 square feet 1

1,001 to 2,000 square feet 2

2,001 to 3,000 square feet 2

3,001 to 5,000 square feet 4

5,001 to 10,000 square feet 5

10,001 to 15,000 square feet 3

25,001 to 50,000 square feet 3

50,001 to 100,000 square feet 1

15,001 to 25,000 square feet 1

Donʹt know ‐ Small 4

Donʹt know ‐ Large 4

Total 30

5.3 Data Collection Approach

5.3.1 BNI On‐Site Surveys

On‐site surveys were conducted with 30 Nova Scotia businesses. Surveyors collected detailed equipment

and building information for each business by inspection. The inspection collected a detailed inventory of

lighting, HVAC, water heating, refrigeration, motors and compressors, and also collected information on

building envelope and site characteristics. Surveys generally took two surveyors four to six hours to

complete. The BNI on‐site survey instrument is included in Appendix B.

5.4 Data Analysis Approach

The approach for data analysis involved quality control, descriptive statistics, comparison of results

across data sets, and summary of corroborated results to describe the market baselines. The analysis

concludes with a discussion of findings especially meaningful to program planning.

All data collected were reviewed for quality control. All on‐site survey responses were reviewed for

validity and completeness by engineers who worked closely with surveyors to ensure data quality
2012 Baseline Study Page 23 

throughout the survey period. Initial analysis of vetted data involved tabulating frequencies of responses

and calculating saturations of standard and efficient equipment by site.  

Baseline results that are corroborated by one or more other data sets are summarized in this report.

Results meaningful to program planning are highlighted and explored in the final section.
2012 Baseline Study Page 24 

6. Residential Market Characterization

This section describes the residential sector findings obtained from the key data collection and analysis

activities conducted by the Navigant team. Data were gathered from on‐site surveys of Nova Scotia

residences (31) during the fall of 2012. The on‐site surveys were used to gather information on building

characteristics and baseline saturations of efficient and standard lighting, HVAC, water heating, and

refrigerators and other appliances, as well as awareness of energy efficiency measures and perceived

barriers to installation of energy efficiency measures. The results from the on‐site visits were used to

supplement the larger online survey.  

6.1 Residential Home Basic Characteristics

This section describes the basic home characteristics of the residential dwellings in the Nova Scotia.

Figure 45 summarizes the findings for basic home characteristics.  

Figure 45. Residential Dwellings Profile

Measure/Characteristic Market Characterization Source

Dwelling Type 75% of all dwellings are single‐

family.

B

Dwelling Age

The majority (58%) of all

dwellings in Nova Scotia were

built after 1971, and the majority

of single‐ family homes (58%)

were also built after this date.

K

Dwelling Size

The average dwelling floor space

for all dwelling types is 1,550

square feet and 1,765 square feet

for single‐family homes.

J

Nearly three‐quarters of all

dwellings are between 1,000 and

3,500 square feet.

B

The average total conditioned

square‐footage is 1,562 sq. ft.

M

Dwelling Height

(Number of Stories)

55% of all dwellings are at least

two stories tall, 29% are single‐

story, and 10% are split‐entry.

B
2012 Baseline Study Page 25 

6.1.1 Home Type

A summary of residential dwelling types is presented in Figure 46. Seventy‐four percent of residential

dwellings are classified as Single‐Family, which includes both Attached and Detached homes built to be

occupied by a single‐family. The remaining dwellings were Moveable Dwellings (4%) and Large and

Small Apartments (4% and 8% respectively).  

Figure 46. Residential Dwelling Type  

Source: Statistics Canada

Small apartment buildings are multifamily structures that have 2 to 4 units. Large apartment buildings

are multifamily structures with 5 or more units.

67%

7%

3%

19%

4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Single Family

(Detached)

Single Family

(Attached)

Small Apt

Building

Large Apt

Building

Movable

Dwelling
2012 Baseline Study Page 26 

6.1.2 Home Age

Figure 47 reflects home vintages as reported by Statistics Canada. This indicates 58 percent of homes in

Nova Scotia built since 1971.33   

Figure 47. Residential Year of Construction

Source: Statistics Canada 2006 Census

6.1.3 Home Size

The average dwelling floor space for all dwelling types is 1,550 square feet and 1,765 square feet for

single‐family homes. Nearly three‐quarters of all dwellings are between 1,000 and 3,500 square feet. The

average total conditioned34 square‐footage is 1,562 sq. ft.

                                                          

33 Note that Statistics Canada figures were drawn from the 2006 Census – as such, Navigant made the assumption

that new house construction between 2001 and 2006 was identical to that between 2006 and 2012.

34 Conditioned Space is defined as space that is designed to be thermally conditioned through either heating or

cooling for the purpose of maintaining a set temperature range.

11%

8%

12%

11%

18%

8% 8%

6% 6% 6% 6%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%
2012 Baseline Study Page 27 

6.2 Residential Building Shell

This section provides details of the residential building shell measures present at the 31 surveyed homes.

Figure 48 summarizes the key findings of building shell measures.  
2012 Baseline Study Page 28 

Figure 48. Residential Building Shell Profile

Measure/Characteristic Key Findings Source

Wall Construction

Types

For homes older than 20 years, 66% have 2x4

construction and 31% have 2x6 construction.

For newer homes, 84% reported 2x6

construction, 11% have adiabatic or shared

walls, and the remaining 5% are concrete block

walls.

M

Above Grade Wall

Insulation

10% of homes are both older than 20 years old

and have loose‐fill wall insulation.  

< 2% of homes have no wall insulation at all.

M

Above Grade Wall

Insulation  R‐Value

Over 73% of Residential homes reported

having batt insulation, but R‐Value varied

depending on wall construction type.

2x4 construction had an average insulation R‐

Value of 13.2.

2x6 construction had an average insulation R‐

Value of 16.5.

M

Ceiling Insulation

Depth

16% of Residential homes have less than 5ʺ of

ceiling insulation.

6% of homes have no ceiling insulation at all.

M

Ceiling Insulation R‐

Value

Average insulation R‐Value for homes older

than 20 years is 19.3.

Homes newer than 20 years have average

insulation R‐Value of 27.4.

M

Roof Construction and

Colour  

98% of homes have asphalt shingle roofs.

Over 85% of roofs are dark coloured, while 11%

are light coloured.

M

Single‐Pane Windows The majority of windows are single pane in 5%

of homes in Atlantic Canada.

G

Low‐E Tinted

Windows

The majority of windows are Low‐E in 11% of

homes in Atlantic Canada.

G
2012 Baseline Study Page 29 

6.2.1 Exterior Walls

A description of the findings on exterior home walls, broken out by age of home, is presented in Figure

49. Eighty‐four percent of homes newer than 20 years are built with 2x6 construction, while two‐thirds of

homes older than 20 years are built with 2x4 construction.    

Figure 49. Residential Exterior Wall Construction and Insulation Profile

Exterior Wall Criteria Homes Older

than 20 years

Homes Newer

than 20 years

Total

Residential

Source

Exterior Wall Construction       

2x4 Stick Built 66.6% 0.0% 50.1% M

2x6 Stick Built 31.6% 83.9% 44.6% M

Adiabatic (shared wall) 1.8% 10.7% 4.0% M

Concrete Block Wall 0.0% 5.4% 1.3% M

Total by Category 100.0% 100.0% 100.0%

Exterior Wall Insulation       

Batt 66.6% 0.0% 50.1% M

Expanded Foam 31.6% 83.9% 44.6% M

Loose Fill 1.8% 10.7% 4.0% M

Not Filled 0.0% 5.4% 1.3% M

Other* 100.0% 100.0% 100.0%

Total by Category      

Average Insulation R‐Value  

for 2x4 Construction 13.2 ‐  13.2 M

Average Insulation R‐Value  

for 2x6 Construction 16.3 16.9 16.6 M

Average Insulation R‐Value  

for Concrete Block Construction ‐  5.4 5.4 M

Source: Residential on‐site surveys
2012 Baseline Study Page 30 

6.2.2 Ceiling or Attic Insulation

The percent of homes with various levels of ceiling or attic insulation in terms of R‐value is shown in

Figure 50. According to ENERGY STAR® standards, the proper level of insulation for the upper Northeast

is R49 – R6035. On‐site survey results show that 97 percent of homes are under‐insulated in their ceilings

and attics.

Figure 50. Residential Ceiling Insulation R‐Values  

Source: Residential on‐site surveys

6.2.3 Windows

It should be noted, that due to the relatively small sample size of the on‐site surveys and wide margin of

error, these results should be used in a qualitative manner.

The majority of windows are single pane in 5 percent of homes in Atlantic Canada. The majority of

windows are Low‐E in 11 percent of homes in Atlantic Canada.

                                                          

35 From ENERGY STAR® website: http://www.energystar.gov/?c=home_sealing.hm_improvement_insulation_table.

R0 - R15

20%

R16 - R30

53%

R31 - R49

23%

R50 +

3%

R0 - R15

R16 - R30

R31 - R49

R50 +
2012 Baseline Study Page 31 

6.3 Residential Lighting

Figure 51 summarizes the location of the residential lighting surveyed for ENSC (weighted by wattage).

Ninety‐three percent of all lighting types were interior while 7 percent were exterior.  

Figure 51. Residential Lighting Location by Wattage  

Source: Residential on‐site surveys

Figure 52 summarizes the distribution of all lighting types for both interior and exterior lamps.

Incandescent lighting represents 51 percent of all indoor lamps installed while CFLs represent 43 percent

of all lamps installed and 19% of the total installed wattage. Forty‐three percent of homes in Atlantic

Canada use fluorescent tube while the majority (71%) has 5 or fewer such fixtures installed in their

homes. LED lamps represent 4% of the total installed lamps and less than 1 percent of the total installed

wattage.

   

93%

7%

Interior

Exterior
2012 Baseline Study Page 32 

Figure 52. Residential Lighting Profile

Measure/Characteristic Distribution Source

Incandescent Incandescent lighting represents 51% of all indoor lamps

installed.

A

Halogen Halogen lamps represent 10% of the total lamps installed and

16% of the total wattage installed.

M

CFL CFLs represent 43% of all lamps installed and 19% of the total

installed wattage.

A

Fluorescent Lighting 43% of homes in Atlantic Canada use fluorescent tubes. Of this

43%, the majority (71%) have 5 or fewer such fixtures installed in

their homes.

G 

LED LED lamps represent 4% of the total installed lamps and less than

1% of the total installed wattage.

A

Other / Don’t Know Other lamps, including unidentifiable lamp types, represent less

than 0.5% of the total lamps installed and less than 0.25% of the

total wattage.

M

6.4 Residential HVAC

6.4.1 Space Heating

Figure 53 presents details of the residential space heating systems. Thirty‐one percent of all primary

heating systems are electric, while 32 percent of all backup heating systems are electric baseboard or

room units. Fifty‐nine percent of households have a secondary heating system installed. Forty‐six percent

of primary heating systems use fuel oil. Twenty‐seven percent of all primary heating systems have a

programmable thermostat installed. Just over 40 percent of all primary heating systems have been

maintained within the last year while 32 percent have never been serviced.

Figure 53. Residential Heating Equipment Profile

Measure/Characteristic Key Findings Source

Electric Heating ‐ 

Primary

31 % of all primary heating systems are electric. B

Electric Heating ‐ 

Backup

32% of all backup heating systems are electric

baseboard or room units. 59% of households have a

secondary heating system installed.

G

Fuel Oil Heating 46% of primary heating systems are fuel oil. B

Programmable

Thermostats

27% of all primary systems currently have a

programmable thermostat.

B
2012 Baseline Study Page 33 

Primary Heating

System Maintenance

Just over 40% of all primary heating systems have

been maintained within the last year.  

32% have never been serviced.

M

6.4.2 Space Cooling

Figure 54 presents details on the air‐conditioning systems. Forty‐two percent of residential homes have

electric fans as their primary cooling system while 18 percent of homes report owning air conditioning

units. Five percent of residential homes had electric heat pumps as their primary cooling system while 3%

of homes have central air conditioning as their primary cooling system.  

Figure 54. Residential Cooling Equipment Profile

Measure/Characteristic Key Findings Source

Electric Fans 42% of homes have electric fans as their

primary cooling system

B

Room Air

Conditioning (RAC)

18% of homes report owning air conditioning

units

B

Electric Heat Pump 5% of homes had electric heat pumps as their

primary cooling system.

B

Central Air

Conditioning

3% of homes have central air conditioning as

their primary cooling system.

B
2012 Baseline Study Page 34 

6.5 Residential Plug Loads

Figure 55 presents details on the plug load electrical technologies. Included are televisions, entertainment

boxes (DVD, DVR, video games), and computers and computer peripherals.  

Figure 55. Residential Plug Loads Profile

Measure/Characteristic Key Findings Source

ENERGY STAR®

Televisions  

47% of televisions are ENERGY STAR® rated   G

Television Hours of Use   Televisions had an average daily reported use

of 3.4 hours.

M

Televisions on Energy

Management Systems

(EMS)

Only 29% of all televisions are plugged into a

power strip that can be manually turned off at

night.  

M

Entertainment Boxes   There are 2.76 entertainment boxes per

household. These are defined as DVD

players, Digital Video Recorders (DVRs), and

video game systems.

B

Digital Video Recorders

(DVRs)

There are 0.63 DVRs per household, and 51%

of homes have at least one DVR.

B

Entertainment Box Hours

of Daily Use

DVRs and video game systems have an

average daily use of over 8 hours.  

B

Entertainment Boxes on

Energy Management

Systems (EMS)  

22% of all entertainment boxes are plugged

into a power strip that can be manually

turned off at night.  

M

Smart Power Strips for

Plug Loads

Navigant found no smart power strips

monitoring vampire energy loads of plug load

equipment. This looks to be an area of

opportunity for ENSC.

M

6.5.1 ENERGY STAR® Televisions and Computers

Figure 56 shows the number of televisions and computers per home found during the on‐site survey

(Source M). Of the homes visited, seventy televisions were installed, with the average household having

two televisions. Forty‐seven percent of televisions in Atlantic Canada are ENERGY STAR ® rated.

Televisions had an average daily reported use of 3.4 hours. Only 29 percent of all televisions are plugged

into a power strip that can be manually turned off at night.  

There are 2.76 entertainment boxes per Residential household. These are defined as DVD players,

Digital Video Recorders (DVRs), and video game systems. There are 0.63 DVRs per Residential

household, and 51 percent of homes have at least one DVR. DVRs and video game systems have an
2012 Baseline Study Page 35 

average daily use of over 8 hours. Navigant’s on‐site survey found that 22 percent of all entertainment

boxes are plugged into a power strip that can be manually turned off at night.  

Source: Residential on‐site surveys

0%

10%

20%

30%

40%

50%

60%

1-10 11-20 21-30 31-40 41+ Age

Unknown
2012 Baseline Study Page 36 

7. Business, Nonprofit and Institutional Market Characterization

This section describes the BNI sector findings obtained from data collection and analysis activities

conducted by the Navigant team. Data were gathered via 30 BNI on‐site surveys during the fall of 2012

and winter of 2013. The on‐site surveys were used to gather information on building characteristics and

baseline saturations of efficient and standard lighting, HVAC, water heating, refrigeration and motors

and compressed air. The results for BNI buildings surveyed are present in the following segments:

1. General Building Information

2. Building Envelope

3. Lighting

4. HVAC

5. Refrigeration

6. Motors and Compressed Air

It should be noted that certain results below were derived from a sample size of 30 on‐site visits and are

used in a qualitative manner. Where possible, Navigant has provided results from additional primary

sources.  

7.1 BNI General Building Information

Surveyors collected information regarding general building characteristics such as age, size, operating

schedules, and more. The following sections present the results for key building characteristics. A

summary of general building information is presented in Figure 74

Figure 74. BNI General Buildings Profile

Characteristic Key Findings Source

Facility Age 85% of BNI facilities were built post‐1970 (weighted by

floor space).

I

The average year of construction was between 1970 and

1999 (weighted by floor space).

I

Building Size 41% of all BNI facilities are 5,000 sq. ft. or less. 14% of

facilities were between 5,001 and 10,000 sq. ft. 18% of

facilities were between 10,000 and 25000 sq. ft.  

8% of facilities were between 25,001 and more.

C

Business

Hours

4% of facilities have weekly hours of 36 hours of less, 22%

have hours between 37 and 48, 17% have weekly hours

between 49 and 72 hours, 14% have hours between 73 and

96, 8% have hours between 97 and 120, and 34% have hours

between 121 and 168, weighted by number of facilities.  

    I
2012 Baseline Study Page 37 

7.1.1 Facility Age (Source I)

The majority (85%) of facilities were built post‐1970. The average year of construction was between 1970

and 1999 (weighted by floor space). The year of construction is related to space heating and space cooling

energy use. Shell construction issues such as leaks, cracks, and drafts due to aging or damaged

construction materials and shifting structures, can increase the energy used for space conditioning,

particularly for older facilities.  

7.1.2 Building Size (Source C)

Forty‐one percent of all BNI facilities are 5,000 sq. ft. or less. Fourteen percent of facilities were between

5,001 and 10,000 sq. ft. Eighteen percent of facilities were between 10,000 and 25000 sq. ft. Eight percent of

facilities were 25,001 sq. ft. or more.

7.1.3 Business Hours (Source I)

Four percent of facilities have weekly hours of 36 hours of less, 22 percent have hours between 37 and 48,

17 percent have weekly hours between 49 and 72 hours, 14 percent have hours between 73 and 96, 8

percent have hours between 97 and 120, and 34 percent have hours between 121 and 168, weighted by

number of facilities.  

7.2 BNI Building Envelope

This section provides details of the commercial building shell measures present at the on‐site surveyed

commercial buildings envelope types. Figure 75  

Figure 75 summarizes the key findings of building shell properties. Envelope characteristics and shell

tightness have a significant interactive effect with space heating and cooling capacities and runtimes.

Improving the overall shell thermal insulation, including glazing, and reducing infiltration, can create an

opportunity to introduce reduced capacity high efficiency mechanical equipment.  
2012 Baseline Study Page 38 

Figure 75. BNI Building Shell Profile

Measure /

Characteristic

Key Findings Source

Wall

Construction

Types

Considering all commercial exterior wall area, 20% has concrete

block construction, 15% has 2x6 wood frame construction, 14%

has 2x6 metal frame construction, and 14% has adiabatic or

shared walls.

N

Exterior Wall

Insulation

Types  

43% of all commercial wall area has batt or blanket insulation.

27% of all commercial wall area does not have any insulation.

N

Roof

Construction

Type

46% of commercial roof area is framed (28% with an attic, 18%

without an attic).  

31% of commercial roof area is constructed with metal decking.

N

Roof Surface 33% of commercial roof area has a metal surface.

16% of commercial roofs have an asphalt roll/shingle surface.

N

Roof Finish 51% of commercial roof area has a flat finish.

24% of commercial roof area has a reflective finish.

N

Roof Colour Only 12% of commercial roof area is dark. N

Floor Types   More than half of commercial floor area is slab‐on‐grade. N

Window

Panes  

99.6% of commercial window area has two panes. N

Window

Glazing

Types

Grocery/supermarket and health care/hospital facilities only

contain clear window glazing, and most non‐food retail centers

only have clear window glazing.

The majority of industrial facilities and restaurants have tinted

window glazing.

The majority of ‘other commercial’ facilities contain reflective

window glazing.

N

Window

Glazing

Features

The majority of commercial windows do not have any glazing

features, with the exception of health care/hospital facilities and

restaurants which each have about 90% low‐E and gas‐filled

windows.

N

Window

Frame Types

Commercial window frames are metal, with the main exception

being vinyl windows in health care/hospital facilities.

N

Window Film   99.8% of commercial window area does not have any added

window film.

N

Source: BNI on‐site surveys
2012 Baseline Study Page 39 

7.2.1 Exterior Wall Construction Types

Figure 76 below describes the findings on commercial exterior wall construction types, broken out by

building type. Considering all commercial building wall areas, 20 percent are concrete block construction,

15 percent are 2x6 wood frame construction, 15 percent are 2x6 metal frame construction, and 15 percent

are adiabatic or shared walls. A relatively small percentage of commercial building wall area is 2x4 wall

construction. The exterior wall construction types vary significantly by building type.  

Figure 76. BNI Exterior Wall Construction Types

Exterior Wall  

Construction Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

2x4 Wood Frame 0% 22% 0% 3% 0% 12% 5% N

2x6 Wood Frame 0% 33% 16% 20% 30% 4% 15% N

2x4 Metal Frame 0% 0% 0% 1% 0% 0% 0% N

2x6 Metal Frame 0% 0% 33% 3% 13% 15% 15% N

8” Solid Concrete 0% 22% 0% 2% 0% 10% 4% N

10” Solid Concrete 0% 0% 0% 7% 0% 4% 4% N

12” Solid Concrete 0% 0% 0% 3% 0% 0% 1% N

Concrete Block 50% 0% 33% 12% 0% 19% 20% N

Brick 0% 0% 0% 0.6% 0% 0% 0.3% N

Corrugated Metal 0% 0% 17% 13% 0% 0% 11% N

Adiabatic (shared

wall) 50% 11% 0% 23% 13% 16% 15% N

Other 0% 0% 0% 2% 0% 0% 1% N

Unidentified 0% 11% 0% 11% 43% 21% 10% N

Total 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys
2012 Baseline Study Page 40 

7.2.2 Exterior Wall Insulation Types

Figure 77 shows commercial exterior wall insulation types, broken out by building type. Forty‐three

percent of all BNI building exterior wall area has batt or blanket insulation. Twenty‐seven percent does

not have any insulation at all. Exterior wall insulation types are moderately similar across commercial

building types.

Figure 77. BNI Exterior Wall Insulation Types

Exterior Wall  

Insulation Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Batt or Blanket 0% 38% 50% 40% 43% 43% 43% N

Expanded Polystyrene 0% 25% 26% 8% 0% 10% 13% N

Loose Fill 0% 25% 0% 2% 0% 0% 2% N

Rigid Board 0% 0% 0% 0.7% 0% 0% 0.4% N

Other 50% 0% 0% 5% 0% 0% 3% N

None 50% 0% 24% 33% 13% 19% 27% N

Unidentified 0% 13% 0% 12% 43% 28% 13% N

Total 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys

7.2.3 Roof Construction Types, Surfaces, Finishes, and Colours

Figure 78 summarizes the BNI roof construction types, surfaces, finishes, and colours among various BNI

building types. Forty‐six percent of BNI roof area is framed, 28 percent has an attic and 18 percent does

not have an attic. Thirty‐one percent of BNI roof area is constructed using metal decking. All three

grocery/supermarket facilities surveyed have a framed roof with an attic. CIBEUS 2000 reports that 31

percent of roof types in Atlantic Provinces are deck type roof construction, 24 percent are framed with

attic type construction, 19 percent are metal truss roof construction, 13 percent are wood truss

construction, and 12 percent are unknown.  

Thirty‐three percent of all BNI roof area has a metal surface. Twenty‐one percent and 16 percent of BNI

roof area has built‐up and asphalt roll/shingle roof surface, respectively.  

Considering all BNI roof area finishes, fifty‐one percent is flat, 24 percent is reflective, and 25 percent is

not identified. Sixty‐seven percent of industrial/manufacturing facility roof area surveyed contains a

reflective finish.

Thirty‐six percent of all BNI roof area is a medium colour, and only twelve percent is dark. All three

grocery/supermarket facilities surveyed have a dark asphalt roll/shingle roof. Non‐food retail BNI

buildings have a fairly even distribution of roof colours.
2012 Baseline Study Page 41 

Figure 78. BNI Roof Construction Types, Surfaces, Finishes, and Colours

Roof Criteria

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Roof Construction Type

Adiabatic (shared

wall) 0% 17% 0% 2% 0% 0% 1% N

Framed With Attic 100% 33% 33% 26% 0% 26% 28% N

Framed Without Attic 0% 33% 33% 5% 35% 29% 18% N

Metal Decking 0% 0% 35% 29% 15% 38% 31% N

Unidentified 0% 17% 0% 39% 50% 8% 22% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Surface

Asphalt Roll/Shingle 100% 67% 0% 24% 0% 14% 16% N

Built‐up 0% 0% 0% 21% 35% 39% 21% N

Metal 0% 0% 66% 17% 15% 34% 33% N

Wood Shingle 0% 0% 0% 0% 0% 6% 2% N

Unidentified 0% 33% 34% 38% 50% 7% 29% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Finish

Flat 100% 67% 34% 48% 50% 84% 51% N

Reflective 0% 0% 66% 13% 0% 0% 24% N

Unidentified 0% 33% 0% 40% 50% 16% 25% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Colour

Light 0% 0% 66% 17% 0% 24% 30% N

Medium 0% 67% 34% 36% 50% 37% 36% N

Dark 100% 0% 0% 10% 0% 28% 12% N

Unidentified 0% 33% 0% 38% 50% 11% 22% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys
2012 Baseline Study Page 42 

7.2.4 Floor

Figure 79 displays the BNI floor types broken down by BNI building type. More than half of BNI floor

area is slab‐on‐grade. 34 percent of health care/hospital facilities’ floor area has an unheated basement.

Figure 79. BNI Floor Types

Floor Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Adiabatic (shared wall) 0% 0% 0% 2% 0% 0% 1% N

Crawl 0% 0% 0% 3% 0% 0% 1% N

Slab 50% 17% 50% 59% 50% 50% 53% N

Unheated Basement 0% 34% 0% 0% 0% 0% 1% N

Unidentified 50% 50% 50% 36% 50% 50% 44% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys

7.2.5 Windows/Fenestration

Figure 80 shows that almost all commercial windows have two panes. Over 99 percent of window area

surveyed was two pane.  

Figure 80. BNI Window Panes

Source: BNI on‐site surveys

   

0.6%

99.4%

One Pane

Two Panes
2012 Baseline Study Page 43 

Figure 81 displays the BNI floor types broken down by building type. Grocery/supermarket and health

care/hospital facilities do not contain any treated window glazing. Most non‐food retail centers do not

have any treated window glazing. A majority of industrial facilities and restaurants have tinted window

glazing. The majority of other BNI facilities contain reflective window glazing.

Figure 81. BNI Window Glazing Type

Source: BNI on‐site surveys

Figure 82 presents BNI window glazing features by commercial building type. The majority of BNI

windows do not have any glazing features, with the exception of health care/hospital facilities and

restaurants, which each have about 90 percent low‐E and gas‐filled windows.

Figure 82. BNI Window Glazing Features

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Tinted

Reflective

Clear

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

None

Low E Only

Both (Low E and Gas

Filled)
2012 Baseline Study Page 44 

Figure 83 displays BNI window frame types broken down by building type. Most BNI window frames

are metal, with the main exception being vinyl windows in most health care/hospital facilities.  

Figure 83. BNI Window Frame Types

  

Source: BNI on‐site surveys

Figure 84 shows that almost all BNI windows do not currently have window film.

Figure 84. BNI Window Film

Source: BNI on‐site surveys

   

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Wood

Vinyl

Metal

99.8%

0.2%

No Window Film

Window Film
2012 Baseline Study Page 45 

Almost all BNI windows do not have an exterior solar shade, as shown in Figure 85.

Figure 85. BNI Window Exterior Solar Shade

Source: BNI on‐site surveys

Figure 86 shows BNI window interior shade types broken down by building type. About 68 percent of

window area has moveable interior shades, with most of the remainder not having any interior shades at

all. Of all BNI buildings, only a small percentage of non‐food retail facilities have fixed interior shades.

Figure 86. BNI Window Interior Shades

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

External Solar Shade

No External Solar Shade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

None

Moveable

Fixed
2012 Baseline Study Page 46 

7.3 BNI Lighting

Figure 87 summarizes the location of the BNI lighting surveyed for ENSC (weighted by wattage). Sixty‐

one percent of all lighting types were indoor while 39 percent were outdoor.

Figure 87. BNI Lighting Location Distribution

Source: BNI on‐site surveys

7.3.1 Indoor Lighting

Figure 88 lists key findings for the BNI indoor lighting profile. Findings include high level results

regarding fixture types, wattages, and control types. Reduced indoor lighting power in interior

conditioned spaces typically increases the heating energy needed in the winter in occupied spaces.  

Figure 88. BNI Indoor Lighting Profile

Measure

Characteristic

Key Findings Source

Fixture Types Fluorescent lights, including both standard and high performance

models, make up 94% of installed lamps and 89% of installed wattage.  

The second most common lamp type is CFLs, which make up 3% of

installed lamps and 1% of installed wattage.

N

Wattage The average wattage of indoor lamps is 36 Watts. The highest average

wattage was in industrial facilities, with an average of 42 Watts per lamp,

followed by ‘Other Commercialʹ sites at an average of 40 Watts per lamp.

N

Control Type Only 6% of indoor BNI lighting has controls beyond a standard manual

switch. 58% of lighting is controlled by an on/off switch, while another

32% is continuous lighting with no controls.

N

Source: BNI on‐site surveys

   

61%

39%

Indoor

Outdoor
2012 Baseline Study Page 47 

Figure 89 summarizes the interior lighting lamp type distribution, weighted by installed wattage. Eighty‐

one percent of all interior lights recorded were standard‐output fluorescent lamps, while another 13

percent were high‐output fluorescent lamps. Standard fluorescent lamps also represented 77 percent of

installed wattage, while high performance HPT8s represented another 12 percent. This illustrates that

fluorescents clearly compose the dominant lighting type in ENSC BNI buildings. Exact distribution

values for all lighting types are listed in Figure 90.

Figure 89. BNI Installed Lamps vs. Installed Wattage

Source: BNI on‐site surveys

Figure 90. BNI Installed Lamps vs. Installed Wattage  

Lamp Type Installed Lamps Installed Wattage Source

Fluorescent 81% 77% N

HPT8 13% 12% N

CFL 3% 1% N

LED 2% 0% N

Incandescent 1% 5% N

U‐Tube Fluorescent 0% 0% N

Quartz/Halogen 0% 0% N

Metal Halide 0% 1% N

Neon 0% 3% N

Source: BNI on‐site surveys

   

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Installed Lamps

Installed Wattage
2012 Baseline Study Page 48 

Fluorescent lamps also dominate the market in terms of fixture types, as shown in Figure 91. Almost 60

percent of indoor fixtures in ENSC’s BNI market are standard fluorescent, while another 22 percent are

high performance T8 fluorescent fixtures. This is in contrast to the 2012 CRA CIBBS telephone survey

indicates that there is still significant potential for efficient fluorescent lighting such as HPT8 and T5HO

fixtures in Nova Scotia, particularly for T5 fixtures. A majority of participants surveyed did not state

‘already installed’ as a barrier to installing fluorescent lighting in their commercial buildings. On average

across efficient fluorescent types, 15 percent of respondents stated that Fluorescent Lighting was Already

Installed at Their Facility, and Other Reasons, were given as primary barriers to implementation.

Figure 91. BNI Distribution of Fixture Type

Lamp Type Fixture Distribution Source

Fluorescent 57% N

HPT8 22% N

CFL 11% N

LED 4% N

Incandescent 3% N

U‐Tube Fluorescent 1% N

Quartz/Halogen 1% N

Metal Halide 0% N

Neon 0% N

Source: BNI on‐site surveys

A breakdown of fixture distribution by building type is illustrated in Figure 92. Standard fluorescent

fixtures are the most common fixture type in industrial facilities, non‐food retail facilities, and ‘other

commercial’ buildings. HPT8s are the most common in grocery stores, while CFLs are most common in

health care facilities and restaurants.
2012 Baseline Study Page 49 

Figure 92. BNI Fixture Distribution by Building Type

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fluorescent

HPT8

CFL

LED

Incandescent

U-Tube Fluorescent

Quartz/Halogen

Metal Halide

Neon
2012 Baseline Study Page 50 

7.3.2 Outdoor Lighting

Figure 93 lists key findings for the BNI outdoor lighting profile. Findings include high level results

regarding fixture types, wattages, and control types.  

Figure 93. BNI Outdoor Lighting Profile

Measure

Characteristic

Key Findings Source

Fixture Types One‐quarter of all outdoor fixtures are High Pressure Sodium

Vapor.  

CFLs and Metal Halides each constituted 17% of the rest of the

outdoor lighting fixture types.  

Neon lights made up another 14% of the distribution.  

The rest of the lighting types made up less than 10% each.

N

Wattage Neon lamps are the dominant lamp type by wattage, making up

over 85% of the distribution. The average wattage for all outdoor

lamp types is 138 watts. Wattages range from 10‐100 Watts,

depending on the lamp.

N

Control Type 82% of sites have motion sensors, photocells, or time clocks as

outdoor lighting controls. Of these control types, 86% are time

clocks.

N

Source: BNI on‐site surveys (Source “N”)

As shown in Figure 94, high‐pressure sodium vapor is the most common exterior fixture type,

constituting nearly a quarter of the market share. This is followed by CFLs and metal halides, with nearly

17 percent of the market, respectively.
2012 Baseline Study Page 51 

Figure 94. BNI Fixture Type Distribution

Source: BNI on‐site surveys

As mentioned above, 82 percent of all sites have some form of automated control systems for their

exterior lights, most of which are photocells. Figure 95 shows the control types on exterior lights.

Figure 95. BNI Control Types for Sites with Exterior Lights

Source: BNI on‐site surveys

7.4 BNI HVAC and Water Heating

Figure 96 summarizes the key findings of the BNI HVAC survey

25%

17% 17%

14%

9% 9%

6%

3%

<1% <1% <1%

0%

5%

10%

15%

20%

25%

30%

Fixtures by Type

46%

26%

15%

10%

3%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Photocell Time Clock Manual

Switch

Motion

Sensor

Unknown

Sites With Control Type
2012 Baseline Study Page 52 

Figure 96. BNI HVAC and Water Heating Profile

Measure

Characteristic

Key Findings Source

HVAC

Single‐Zone

HVAC

System Age

The on‐site surveyors identified 477 single‐

zone HVAC systems. Two‐thirds (66%) of

single‐zone HVAC systems are

baseboard/radiant systems followed by unit

ventilators or heaters (13%).  

N

Primary

Heating Fuel

Type

Nearly two‐thirds (65%) of all single‐zone

HVAC systems used electricity as a fuel type.

Fuel oil and liquid propane gas (LPG)

represent 3% each (6% total) of fuel types for

single‐zone HVAC systems.

N

Heating

System Type

More than half (53%) of all facilities sampled

had baseboard heaters. Boilers and electric

resistance heaters were in 19% and 10%,

respectively, of facilities sampled. Air source

heat pumps, furnaces, and radiant heaters

comprise the remaining heating systems and

were in 8% of surveyed facilities.  

N

Boiler

Delivery

Systems

The majority (79%) of boilers were hot water

boilers. 21% of boilers were a steam delivery

system. The mean boiler age was 12.6 years.

N

Hot Water

Circulation

Pump Speed

Controls

28% of all pumps were 0.15 horsepower (hp).

Pumps that were 0.2, 0.25, 0.75, and 3 hp in

size represented 20%, 8%, 8%, and 4% of all

pumps, respectively.

N

Cooling

System Types

According to the Commercial and

Institutional Building Energy Use Survey

(CIBEUS) 2000 reported that in Atlantic

Canada as a whole, 60% of C&I buildings had

mechanical cooling of some kind.  

F

Of the systems that did have a cooling

component, 95% were direct expansion while

5% were chillers. The mean size of all cooling

units was 2.9 tons with a mean efficiency of

9.8 EER and 13 SEER.

N
2012 Baseline Study Page 53 Water Heating

Water

Heating

Equipment

Types

88% of all water heaters were standard water

heaters (i.e., tank water heaters). 8% of water

heaters were instantaneous, or tankless, water

heaters, while 5% were boilers. The average

age of all water heaters was 6.6 years. The

average size water heater was 4.1 kW and

96.5 kBtuh.

N

Water

Heating Fuel

Types

The majority of water heaters were fueled by

electricity. Five percent of units were fueled

by liquid propane gas (LPG), while fuel oil

and natural gas represented 2.5% each (5%

total) of fuel types for water heaters.

N

Water

Heating Pipe

and Tank

Insulation

85% of pipes leading into the water heater

were not insulated, while 12.5% were

insulated. Similarly, 82.5% of tanks were not

externally insulated (e.g., blanket wrap),

while 12.5% were externally insulated.

N

Water

Heating

Recirculation

Pump

85% of water heaters did not have

recirculation pumps installed, while 10% did

have recirculation pumps installed. 5% of

water heaters were noted as unknown, or

“Don’t Know”, if a recirculation pump was

installed.  

N

Source: BNI on‐site surveys

   
2012 Baseline Study Page 54 

The majority (99%) of HVAC systems are single‐zone systems while 1 percent are multi‐zone systems, as

shown in Figure 97. Single‐zone systems are typically considered “simple” systems in the energy code,

and multi‐zone systems are categorized as “complex” systems. Each system type has different

requirements to comply with the energy code, which affects HVAC systems at time of replacement.  

Figure 97. BNI Distribution of Single‐Zone and Multi‐Zone Systems

Source: BNI on‐site surveys  

Multi-Zone, 

1%

Single-Zone, 

99%
2012 Baseline Study Page 55 

7.4.1 Single‐Zone Distribution Systems

The on‐site surveyors identified 477 single‐zone HVAC (SZHVAC) distribution systems and gathered all

available data for each system. Figure 98 shows that two‐thirds (66%) of single‐zone HVAC systems are

baseboard/radiant systems followed by unit ventilators or heaters (13%). Room AC, packaged single

zone, and ductless mini‐split, make up 7 percent, 5 percent, and 2 percent of single‐zone systems,

respectively.  

Figure 98. BNI Distribution of Single‐Zone HVAC Systems

Source: BNI on‐site surveys

Figure 99 summarizes the estimated maintenance of single‐zone HVAC units. Seventy percent of the

facilities surveyed estimated that they conduct maintenance on existing equipment every 6 to 12 months.

Twenty‐three percent of those surveyed reported that they conduct maintenance on an “as‐needed” or

“on failure” need. Three percent estimated that they conduct maintenance on their single‐zone HVAC

system every 1 to 5 years, while 4 percent estimated they conducted maintenance on a period of less than

6 months.  

2-Pipe Fan Coil, 

3%

Baseboard or 

Radiant Heater, 

67%

Ductless Mini 

Split, 2%

Pckg. Single Zone 

(Unitary), 5%

Room AC 

(window unit), 

7%

Split-System 

Single Zone, 1%

Single Zone 

(built-up), 1%

Unit 

Ventilator 

or Heater, 

13%
2012 Baseline Study Page 56 

Figure 99. BNI Single‐Zone HVAC Maintenance

Source: BNI on‐site surveys

7.4.2 Multi‐Zone Systems

Although Navigant included multi‐zone HVAC systems in the BNI survey, very few of the facilities

sampled contained multi‐zone systems. This may be a factor of the relatively small sampled of 30 on‐site

visits in a very diverse market.  HVAC systems that do not serve a dedicated, single zone are classified as

multi‐zone systems. A total of one multi‐zone cooling system was observed. This system was a 10 ton

direct expansion unit. Two multi‐zone air handling units were observed, as well as one multi‐zone

electric heating unit. The heating unit was noted as “Other” by the survey team.  

3%

70%

4%

23%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-5 yrs 6-12mths < 6mths On Failure
2012 Baseline Study Page 57 

7.5 BNI Heating Systems

7.5.1 Heating Fuel Type

The BNI survey indicated that nearly two‐thirds (65%) of all single‐zone HVAC systems used electricity

as a fuel type, as shown in Figure 100. Fuel oil and liquid propane gas (LPG) represent 3 percent each (6%

total) of fuel types for single‐zone HVAC systems. The 2012 CRA CIBBS telephone survey recorded that

electrically operated air source and ground source heat pumps are not commonly installed. These

electrically operated devices are several times more efficient than electric resistance heating devices, and

could have a significant impact in Nova Scotia where 65 percent of heating is electric.

Figure 100. BNI Single‐Zone System Fuel Type

Source: BNI on‐site surveys

65%

3% <1% 3%

27%

0.6%

0%

10%

20%

30%

40%

50%

60%

70%

Electricity Fuel Oil Natural Gas LPG Not Filled Wood
2012 Baseline Study Page 58 

7.5.2 Heating System Type

The BNI survey indicated that more than half (53%) of all facilities sampled had baseboard heaters, as

shown in Figure 101. Boilers and electric resistance heaters comprised 19 percent and 10 percent,

respectively. Air source heat pumps, furnaces, and radiant heaters comprise 8 percent of the remaining

heating systems. This low penetration of air source and ground source heat pumps, coupled with the 65

percent electrical heating fuel type found by Navigant, suggests there is significant potential in Nova

Scotia to reduce energy use and cost using air source and ground source heat pumps as a heating source,

instead of electric resistance heating. Nine percent of facilities did not have a single‐zone heating system.  

Figure 101. BNI Single‐Zone Heating System Type

Source: BNI on‐site surveys

7.5.3 Boilers

Figure 102 summarizes the distribution of boiler delivery systems. The majority (79%) of boilers were hot

water boilers. In cold climates such as Nova Scotia, there may be an opportunity for condensing boilers to

operate closer to their peak condensing thermal efficiency due to lower return water temperatures.

Twenty‐one percent of boilers used a steam delivery system. The mean boiler age was 12.6 years.  

3%

19%

53%

10%

4%

9%

<1% 1%

0%

10%

20%

30%

40%

50%

60%
2012 Baseline Study Page 59 

Figure 102. BNI Boiler Delivery System by Type

Source: BNI on‐site surveys

   

Steam, 

21%

Hot Water, 

79%
2012 Baseline Study Page 60 

Figure 103 summarizes the breakdown of boiler fuel types by delivery mechanism. Two‐thirds of steam

boilers were “fuel oil” type, while one‐third were “electricity resistance” type. Hot water boilers were

distributed among four fuel types, natural gas, waste oil, fuel oil, and electricity resistance, representing

approximate, 9 percent, 18 percent, 64 percent, and 9 percent of fuel types, respectively.  

Figure 103. BNI Boiler Fuel Types by Delivery System

Source: BNI on‐site surveys

Steam Hot Water

Natural Gas 0% 9%

Waste Oil 0% 18%

Fuel Oil 67% 64%

Electricity Resistance 33% 9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent of Boilers
2012 Baseline Study Page 61 

7.5.4 Hot Water Circulation Pumps

Figure 104 summarizes the distribution of hot water circulation pumps in the facilities surveyed. Twenty‐

eight percent of all pumps were 0.15 horsepower (hp). Pumps that were 0.2, 0.25, 0.75, and 3 hp in size

represented 20 percent, 8 percent, 8 percent, and 4 percent, respectively. Twenty percent of hot water

circulation pumps were noted by surveyors as “Don’t Know.”  

Figure 104. BNI Hot Water Circulation Pump Distribution by Size

Source: BNI on‐site surveys

Figure 105 summarizes the control types of hot water circulation pumps. Eighty‐eight percent of all hot

water pumps were one speed, while 12percent were variable speed.  

Figure 105. BNI Hot Water Circulation Pump Speed Control Type

Source: BNI on‐site surveys

Other, 20%

0.1 HP, 12%

0.15 HP, 28%

0.2 HP, 20.00%

0.25 HP, 8%

0.75 HP, 8%

3 HP, 4%

One Speed, 

88%

Variable 

Speed, 12%
2012 Baseline Study Page 62 

7.6 BNI Cooling Systems

Figure 106 summarizes the type of cooling units among all single‐zone HVAC systems. Eighty‐three

percent of all single‐zone systems do not have a cooling component. Of the systems that did have a

cooling component, ninety‐five percent were noted by surveyors to be direct expansion while 5 percent

were chillers. The mean size of all cooling units was observed to be 2.9 tons with a mean efficiency of 9.8

EER and 13 SEER.  

Figure 106. BNI Types of Cooling for Single‐Zone HVAC  

Source: BNI on‐site surveys

   

1%

15%

83%

1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Chiller Direct Expansion None N/A
2012 Baseline Study Page 63 

Figure 107 summarizes the size distribution of all single‐zone cooling systems within both the Chiller and

Direct Expansion categories. Only two instances of single‐zone chillers were recorded by the surveyors,

with unit’s sizes ranging from less than 0.5 tons to between 1 and 3 tons. Direct expansion cooling

systems had a broader range in sizing, with 25 percent of units being less than 0.5 tons. Thirty‐one

percent of units were greater than 0.5 and less than or equal to 1 tons. Thirteen percent of units were

greater than 1 tons but less than or equal to 3 tons. Seventeen percent of direct expansion units were

greater than 3 tons but less than or equal to 5 tons. Fourteen percent of units were greater than 5 tons but

less than or equal to 10 tons. Only 1 percent of direct expansion units were recorded as being greater than

10 tons. For comparison, CIBEUS 2000 reported that in Atlantic Canada as a whole, 60 percent of C&I

buildings had mechanical cooling of some kind.

Figure 107. BNI Distribution of Sizes for Single‐Zone Cooling  

Source: BNI on‐site surveys

Chillers Direct Expansion

10 < ton 0% 1%

 5 < ton <=10 0% 14%

 3 < ton <=5 0% 17%

 1 < ton <=3 50% 13%

0.5 < ton <=1 0% 31%

ton <= 0.5 50% 25%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent of Cooling Systems
2012 Baseline Study Page 64 

Figure 108 summarizes the efficiency (in EER) by system size range for all direct expansion cooling

systems surveyed.  

Figure 108. BNI Direct Expansion Average Efficiency (EER) by Size Range

System Size Rage Average Efficiency Source

EER

ton <= 0.5 11.00 N

0.5 < ton <=1 9.63 N

1 < ton <=3 9.00 N

3 < ton <=5 9.70 N

5 < ton <=10 9.50 N

10 < ton 10.00 N

Source: BNI on‐site surveys

7.6.1 Chillers

Only one chiller was found in the facilities included in the survey. The chiller was an electric fuel type

with no variable speed drive (VSD) installed. The chiller had a capacity of 9 tons.  

7.7 BNI HVAC Controls

Figure 109 displays the most common control types for HVAC systems in the facilities sampled.

Thermostats represent 74 percent of HVAC control types, while programmable thermostats and manual

on/off switches represent 12 percent and 7 percent, respectively. Controls that were noted by the surveyor

as “Always On” or energy management systems (EMS) represent 3 percent and 2 percent respectively.  

Figure 109. BNI HVAC Control Types  

Source: BNI on‐site surveys

3% 1% 2% 7% 12%

74%

0%

10%

20%

30%

40%

50%

60%

70%

80%
2012 Baseline Study Page 65 

7.7.1 Exhaust Fans

Eighty‐seven percent of exhaust fans are controlled manually, as shown in Figure 110. Seven percent of

exhaust fans are controlled by a sensor, while 4 percent were reported as not having a control type (e.g.

always on fans). The control type for 1.5 percent of the exhaust fans surveyed was reported as unknown

or “Don’t Know”.  

Figure 110. BNI Distribution of Exhaust Fan Control Type  

Source: BNI on‐site surveys

Figure 111 summarizes the distribution of exhaust fan drive speed types. Ninety‐seven percent of exhaust

fans were noted to be constant speed type, while 1.5 percent were reported as variable speed fans.

Surveyors reported 1.5 percent of fan speed types as unknown or “Don’t Know”.  

Figure 111. BNI Distribution of Exhaust Fan Drive Speed Type

Source: BNI on‐site surveys

Don't Know, 

1.5%

Manual, 87%

None, 4%

Sensor, 7%

Constant, 97%

Don't Know, 

1.5% Variable, 1.5%
2012 Baseline Study Page 66 

7.7.2 Water Heating

Figure 112 summarizes the types of water heaters in the facilities surveyed. Eighty‐eight percent of all

water heaters were standard water heaters (i.e., tank water heaters). Eight percent of water heaters were

instantaneous, or tankless, water heaters, while 5 percent were boilers. The average age of all water

heaters was 6.6 years. The average size water heater was 4.1 kW and 96.5 kBtuh.

Figure 112. BNI Water Heater Type Distribution  

Source: BNI on‐site surveys

As shown in Figure 113, the majority (cumulatively 90%) of water heaters were fueled by electricity. Five

percent of units were fueled by liquid propane gas (LPG), while fuel oil and natural gas represented 2.5

percent each (5% total) of fuel types for water heaters.  

Figure 113. BNI Distribution of Fuel Types Among Water Heaters  

Source: BNI on‐site surveys

   

Boiler, 5%

Instantaneous, 

8%

Standard 

Water Heater, 

88%

3% 3% 5% 3%

85%

3%

0%

20%

40%

60%

80%

100%

Fuel Oil Natural

Gas

Electricity LPG Electricity LPG

Boiler Instantaneous Standard Water

Heater
2012 Baseline Study Page 67 

Figure 114 shows the distribution of temperature set points for water heater observed. Seventy percent of

units were set between 43 °C (110 °F) and 60 °C (140 °F) (i.e., medium), while 23 percent of units were

above 60 °C (140°F) (i.e., high) and 5 percent of units were set below 110 °F (i.e., low). It should be noted

that Health Canada recommends setting water heaters to a temperature of 50 °C (122 °F) to reduce the

risk of legionellosis.  

Figure 114. BNI Distribution of Water Heating Temperature Set Point  

Source: BNI on‐site surveys

   

23%

5%

70%

3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

High Low Medium Not Filled
2012 Baseline Study Page 68 

Figure 115 summarizes the distribution of water heating insulation either on the tank or on the pipe.

Eighty‐five percent of pipes leading into the water heater were not insulated, while 13 percent were

insulated. Similarly, 83 percent of tanks were not externally insulated (e.g., blanket wrap), while 13

percent had insulation.  

Figure 115. BNI Distribution of Water Heating Insulation  

Source: BNI on‐site surveys

As shown in Figure 116, 85 percent of water heaters did not have recirculation pumps installed, while 10

percent did have recirculation pumps installed. Five percent of water heaters were noted as unknown, or

“Don’t Know”, if a recirculation pump was installed.  

Figure 116. BNI Water Heat Recirculation Pump  

Source: BNI on‐site surveys

3%

85%

13%

5%

83%

13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Don't Know No Yes Don't Know No Yes

Pipe Insulation Tank Insulation

85%

5%

10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

No Don't Know Yes
2012 Baseline Study Page 69 

7.8 BNI Refrigeration

Market Characterization – Refrigeration

Figure 117 lists key findings for the BNI refrigeration profile.  

Figure 117. BNI Refrigeration Profile

Market

Characteristic

Key Findings Source

Compressors Compressors were in industrial facilities, restaurants, and ‘other

commercial’ facilities. However, insufficient data exists to properly

characterize the market in terms of compressors.

N

Walk‐in space

lighting

Nearly 63% of space lighting in BNI walk‐ins is fluorescent, while

another 25% is incandescent. The rest are unknown.

N

Walk‐in

lighting

controls

Eighty‐eight percent of lighting in BNI walk‐ins is controlled via a

wall switch. The rest of the control types are unknown.

N

Walk‐in

motor types

A quarter of the motors in BNI walk‐ins are shaded pole, while the

rest are unknown. 14% of motors have evaporative fan controls.

N

Source: BNI on‐site surveys

Figure 118 provides a list of refrigeration equipment in BNI sites. Of the equipment types in BNI facilities,

55 percent are glass door beverage cases, as illustrated in Figure 119.  

Figure 118. BNI Refrigeration Types

Equipment

Code

Equipment Description Size of

Default

Source

GD Glass door beverage cases (e.g. vendor supplied) from

2 to 4 doors

3 doors N

CD Closed door storage cases, one to three doors 2 doors N

UG Upright glass door freezer cases from one to three

doors

2 doors N

CF Coffin type glass top freezer cases (usually ice cream)

typically 6 or 8 ft.

7 ft. N

OT Other:  self‐contained refrigeration not listed above 1 unit N

IV Ice vending machines (hotel‐sized icemaker) – Only self‐contained units1 unit N

Source: BNI on‐site surveys
2012 Baseline Study Page 70 

Figure 119. BNI Refrigeration Equipment Types Distribution

Source: BNI on‐site surveys

Of all non‐BNI refrigeration equipment type on BNI sites, 76 percent are 1‐door cases, with another 14

percent being chest‐style refrigerators. As shown in Figure 120, the final 10 percent are classified as

‘other.’

Figure 120. Non‐BNI Type Refrigeration Equipment Types Distribution

Source: BNI on‐site surveys

55%

18%

8% 8% 3%

10%

0%

10%

20%

30%

40%

50%

60%

GD CF CD UG IV OT

76%

14% 10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-Door Chest Other
2012 Baseline Study Page 71 

7.9 BNI Motors and Compressed Air

7.9.1 Market Characterization – Motors and Compressed Air

Figure 121 provides a summary characterization of motor and compressed air measures for the BNI

segments.

Figure 121. BNI Motors and Compressed Air Profile

Measure/Characteristic Key Findings Source

Process motor types The majority (62%) of motors are pump motors and material

handling/conveying motors.

N

Motor controls and

efficiency

All identified motors are standard efficiency and constant

speed.

N

Air compressor types The majority (82%) of air compressors are reciprocating two‐

stage, single acting (RTS) units.

N

Air compressor hours

of use

RTS type air compressors are using 73 hp about 49 hours a

week.

N

Air dryer on

compressed air systems

The majority (90%) of air compressors do not have an air

dryer.

N
2012 Baseline Study Page 72 

7.9.2 Motors

The majority (62%) of motors in the BNI sample are pump motors and material handling/conveying

motors, as shown in Figure 122. Process motors were present in the industrial/manufacturing, retail non‐

food and ‘other commercial’ segments of the BNI sample.  

Figure 122. BNI Distribution of Process Motor Types

Source: BNI on‐site surveys

Figure 123 shows that all of the motors surveyed are constant speed motors and classified as standard

efficiency. All motors have a NEMA type of TEFC (totally enclosed fan cooled). Opportunities exist to

increase efficiency levels of motors through both increasing equipment efficiency and adding variable

controls.

Figure 123. BNI Motor Controls, Efficiency and NEMA Type  

Source: BNI on‐site surveys

Fan/blowing, 

19%

Grinding/milling, 

6%

Material 

handling/conveying

, 26%

Pump, 36%

Tool 

(machine), 

13%

0%

20%

40%

60%

80%

100%

Standard Efficiency Constant Speed NEMA type TEFC
2012 Baseline Study Page 73 

The rated horsepower for process motor units ranges from 0.1hp to 30hp with a mean

horsepower of 3.7hp. The total horsepower by motor type is shown in Figure 124.

Figure 124. BNI Total Motor Horsepower by Type

Motor Type Number of Motor Units Total Horsepower (hp) Source

Fan/blowing 9 40 N

Grinding/milling 3 10.8 N

Material handling/conveying 12 34.5 N

Pump 16 84.5 N

Tool (machine) 1 5.1 N

Total

6 174.8

Source: BNI on‐site surveys

7.9.3 Compressed Air

The majority (82%) of the air compressors surveyed are reciprocating two‐stage, single acting (RTS) units,

as shown in Figure 125. Only one unit out of all air compressors is a smaller capacity single‐stage model

while all other units (91%) are two‐stage compressors, which are more efficient and often longer lasting

than their single‐stage counterparts. All air compressor units were located in the

industrial/manufacturing and ‘other commercial’ segments of the BNI sample. Navigant recommends

investigation of electrically powered alternatives to compressed air use where possible, and recommends

lowering air pressure setpoints for existing systems to the extent possible to control the high cost of

compressed air systems. This can sometimes be achieved by separating unlike compressed air end uses

onto separate systems.  

Figure 125. BNI Distribution of Air Compressor Type

Air Compressor Type Number of Units Percent of Total Source

Reciprocating (Single‐stage, Single‐

acting) (RSS) 1 9% N

Reciprocating (Single‐stage, Double‐

acting) (RST) 0 0% N

Reciprocating (Two‐stage, Double‐acting)

(RTD) 1 9% N

Reciprocating (Two‐stage, Single‐acting)

(RTS) 9 82% N

Rotary Screw (Two‐Stage) 0 0% N

Centrifugal 0 0% N

Unknown 0 0% N
2012 Baseline Study Page 74 

Source: BNI on‐site surveys  

The air compressors in the ‘other commercial’ segment are using a total of 30.5hp about 8 hours per week,

as shown in Figure 126. Figure 127 shows that air compressors in the industrial/manufacturing segment

of the BNI sample are using a total of 50hp about 40 hours per week.

Figure 126. BNI ‘Other Commercial’ Air Compressor Hours of Weekly Use by Horsepower

Source: BNI on‐site surveys

5

2

10.5

3

5

22.5

0

5

10

15

20

25

RSS RTD RTS

Air Compressor Type

hours per week

horsepower

Other 0 0% N

All 11 100%
2012 Baseline Study Page 75 

Figure 127. BNI Industrial Air Compressor Hours of Weekly Use by Horsepower

Source: BNI on‐site surveys

   

38

50

0

10

20

30

40

50

60

RTS

Air Compressor Type

hours per week

horsepower
2012 Baseline Study Page 76 

The majority (90%) of air compressors do not have an air dryer, as shown in Figure 1284. Air dryers

maintain higher quality compressed air, typically necessary for industrial purposes, but also consume

more energy and typically operate continuously unless an efficient cycling air dryer is employed. Out of

all the air compressors, only one RTS unit running at 7.5hp had an air dryer present.  

Figure 128. BNI Air Dryer Verification by Air Compressor Type

Air Compressor Type Number of Units Air Dryer Present

Reciprocating (Single‐stage, Single‐acting) (RSS) 1 No

Reciprocating (Two‐stage, Double‐acting) (RTD) 1 No

Reciprocating (Two‐stage, Single‐acting) (RTS)

8 No

1 Yes

Source: BNI on‐site surveys (Source “N”)

Figure 56 shows the distribution of televisions and computers by type, as well as the percent of each type

that are ENERGY STAR® rated.

Source: Residential on‐site surveys

  

Figure 56. Televisions and Computers per Household

0 1 2 3 4 5+

Computers (n = 54)

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5+

Sampled Homes in ENSC

Televisions (n=70)

Figure 57. Television Display Types and Computer Types and Percent ENERGY STAR®

26%

41%

9% 3%

20%

42%

58%

3%

23%

7%

0% 4%

37%

55%

0%

20%

40%

60%

80%

CRT LCD LED Other Plasma Desktop Laptop

Television Display Types Computer Types

Distribution of Technology Types % ENERGY STAR®
2012 Baseline Study Page 77 

On‐site survey results found that about 25 percent of all plug load devises are on a power strip that can

be manually turned off at night. The rest are either not on a power strip, or on a strip that is left on all

the time. Due to the increase in plug load devises in homes, a “smart strips” is one way to reduce

“vampire loads” for these devises that operate at some level, 24 hours a day. A list of terminology for

home entertainment system is provided in  

.

Figure 58. Terminology for Home Entertainment System Components

Component Abbreviation

Digital Video Disk DVD

Digital Video Recorder DVR

Other OT

Videocassette Recorder VCR

Video Game System VS
2012 Baseline Study Page 78 

7.10 Residential Water Heating

This section presents details on the water heating equipment present in the surveyed homes. A summary

of residential water heating characterization is presented in Figure 59.  

Figure 59. Residential Water Heating Profile

Measure /  

Characteristics

Key Findings Source

Electric Water

Heating  

61% of surveyed primary water heaters were electric

resistance.

B

The average capacity for electric resistance water heaters was

38 gallons. 88% of electric resistance water heaters were

located in conditioned spaces, the majority of which (64%)

were set at a medium temperature setting.

M

Non‐Electric Water

Heating  

39% of water heaters surveyed had fuel types other than

electric resistance (ER). The most common of these were fuel

oil, which constituted 35% of all water heaters.

B

The average capacity was 38 gallons, and about 53% of the

non‐electric water heaters were located in conditioned spaces.

58% of these water heaters were set at a medium temperature

setting.

M

Secondary Water

Heaters  

Of the 1500 surveyed online, 76 contained a secondary water

heater. The water heaters were electric resistance, oil, solar,

propane, and other.  

B

Water Heater

Capacity  

The average capacity of water heating systems is 38 gallons.

The minimum capacity of surveyed systems was 15 gallons,

while the maximum capacity was 60 gallons.

M

Water Heater

Location   

72% of all residential water heaters are located in conditioned

space.  

M

Auxiliary Measures   Tank and pipe wrap were the only auxiliary measures

installed on surveyed water heaters. These measures are

present on 28% and 26% of water heaters, respectively36.

B

Tank Temperature   Nearly 62% of all residential water heaters are set at a

medium (125°F) setpoint. Another 18% are set at a high

(150°F) setpoint, while another 14% are set at a low (90°F)   

setpoint. Temperature setpoints are unknown for 7% of water

heaters, all of which are fuel oil.

M

                                                          

36 It should be noted that the majority of new water heaters installed typically have insulation integral to the tank.
2012 Baseline Study Page 79 

7.10.1 Water Heater Fuel Type

Sixty‐one percent of surveyed primary water heaters were electric resistance. The average capacity for

electric resistance water heaters was 38 gallons. Eighty‐eight percent of electric resistance water heaters

were located in conditioned spaces, the majority of which (64%) were set at a medium temperature

setting.

7.10.2 Water Heater Capacity

Figure 60 provides the average capacity of water heaters by fuel type. Similarly,  

Figure 61 shows the distribution of water heater capacity ranges for both electric resistance water heaters

and water heaters of other fuel types. The average water heater capacity across all fuel types is 38 gallons.

Figure 60. Residential Average Water Heating Capacity  

Water Heater Fuel Type   Average Capacity (gallons) Source

Electric Resistance 38.0 M

Natural Gas 40.0 M

Propane or Bottled gas 40.0 M

Fuel Oil 33.6 M

Source: Residential on‐site surveys

Figure 61. Residential Water Heating Capacity Distribution: Percent of Overall Distribution  

Source: Residential on‐site surveys

2%

7%

33%

7%

2% 2% 2%

25%

2%

16%

0%

5%

10%

15%

20%

25%

30%

35%

40%

15-20 21-30 31-40 41-50 51+ Don't

Know

Electric Resistance

Other Fuel Type
2012 Baseline Study Page 80 

Water Heater Location (Conditioned vs. Unconditioned)

Figure 62 illustrates the percent of both electric resistance water heaters and water heaters of other fuel

types that are located in conditioned versus unconditioned space. Eighty‐eight percent of electric

resistance water heaters are located in conditioned space, while 53 percent of water heaters with other

fuel types are located in conditioned space.

Figure 62. Residential Water Heating Location by Fuel Type

Source: Residential on‐site surveys

7.10.3 Water Heater Auxiliary Measures

Figure 63 summarizes the auxiliary water heater measures that were installed. Both tank wrap and pipe

wrap were installed on water heaters. Twenty‐one percent of water heaters surveyed had tank wraps

installed, while 19 percent of water heaters had pipe wrap installed on nearby piping.  

Figure 63. Residential Water Heating Auxiliary Measures Profile

Auxiliary

Measures  

Distribution Source

Tank Wrap Tank wraps37 are present on 28% of residential water heaters.   B

Timer 3% of dwellings have water heater timers. B

Add‐On Heat

Pump No add‐on heat pumps were present on residential water heaters. M

Pipe Wrap Pipe wrapping is present on 26% of residential water heaters.   B

Drain Water Heat

Recovery

No drainwater heat recovery systems were present on residential

water heaters.

M

                                                          

37 It should be noted that the vast majority of new water heaters installed typically have insulation integral to the

tank.

88%

12%

0%

53%

23% 24%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Conditioned Space Unconditioned

Space

Space Type

Unknown

Electric Resistance (n= 18)

Other Fuel Type (n= 14)
2012 Baseline Study Page 81 

7.10.4 Water Heater Temperature Setting

Figure 64 shows the distribution of water heater temperature settings. The majority of both electric

resistance water heaters (64%) and water heaters with other fuel types (58%) have medium (125°F)

temperature setpoints. It should be noted that Health Canada recommends setting water heaters to a

temperature of 50 °C (122 °F), which is the medium setpoint, to reduce the risk of legionellosis38.

Figure 64. Residential Water Heater Temperature Settings

Source: Residential on‐site surveys

7.10.5 Low‐Flow Faucet Aerators and Low Flow Showerheads

Figure 65 summarizes the findings for faucet aerators and low flow showerheads. Thirty‐three percent of

dwellings have faucet aerators on all faucets, while 9 percent of dwellings had aerators on some, but not

all faucets. Fifty‐six percent of dwellings have low flow showerheads installed on all showerheads, while

5 percent have low flow showerheads installed on some, but not all showers.  

Figure 65. Residential Faucet Aerators Profile

Measure/Characteristic Key Findings   Source

Faucet Aerators 33% of dwellings have faucet aerators on all faucets,

while 9% of dwellings had aerators on some, but not

all faucets.

B

Low Flow

Showerheads

56% of dwellings have low flow showerheads installed

on all showerheads, while 5% have low flow

showerheads installed on some, but not all showers.

B

                                                          

38 http://www.hc‐sc.gc.ca/hl‐vs/iyh‐vsv/diseases‐maladies/legionnaire‐eng.php#a5

10%

64%

27% 26%

58%

15%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

High Medium Low Temp

Setting

Unknown

Electric Resistance (n= 18)

Other Fuel Type (n= 14)
2012 Baseline Study Page 82 

7.11 Residential Appliances

This section presents findings residential appliances. Figure 66 provides summary findings for all

residential appliances. Over 95 percent of all residential ranges are electric, and 8 percent are ENERGY

STAR® rated. All homes sampled have an oven; 96 percent are electric. Of the electric ovens, 7 percent are

convection and the rest are standard. Sixty‐four percent of residential dwellings sampled contain a

dishwasher and about 20 percent of those are ENERGY STAR® rated. Ninety‐eight percent of homes own

a clothes washer, but only 31 percent are front loading, ENERGY STAR® rated. Ninety‐six percent of

homes own a clothes dryer, 94 percent of which are electrically operated.  

Figure 66. Residential Appliances Profile

Measure/Characteristic Key Findings Source

Residential Range

Cooktops

95% of all Residential ranges are electric B

8% of ranges are ENERGY STAR® rated.   M

Residential Ovens All homes sampled have an oven; 96% are electric.   B

Of the electric ovens, 7% are convection and the rest

are standard. The average age of ovens is 10 years

old.

M

Residential

Dishwashers

64% of residential dwellings sampled contain a

dishwasher.

B

20% of dishwashers are ENERGY STAR® rated. 18%

of dishwasher owners said they used the Energy

Saver option all of the time, while only another 16%

said they only use it some of the time. However, 66%

use the Heat Booster option all of the time. The

average age of dishwashers is nearly 10 years old.

M

Residential Clothes

Washers

98% of homes own a clothes washer, and 31% are

front loading.  

B

The majority (65%) of households in Atlantic Canada

complete between two to three (28%) and four to

seven (37%) loads of laundry per week in both

summer and winter.

G

Residential Clothes

Dryers

96% of homes own a clothes dryer. B

The majority (62%) of households dry between two

and three (26%) and four and seven (36%) loads of

laundry during winter weeks.  

The majority (62%) of households dries between one

or less (38%) and two and three (24%) loads of

laundry during summer weeks.

G
2012 Baseline Study Page 83 

7.11.1 Residential Refrigerators

This section presents details on the refrigerators observed at the surveyed homes. Each surveyed home

had at least one refrigerator. Figure 67 summarizes the key findings of refrigeration measures.

Figure 67. Refrigerators Profile

Measure/Characteristic Key Findings Source

Refrigerator ‐ Primary 66% of all refrigerators are top‐

mount, while another 19% are

bottom‐mount. The average size of

primary refrigerators is 16.4 cubic

feet, and the average age is 7 years.

M

Refrigerator ‐ Secondary 72% of secondary refrigerators are

bottom mount. The average size of

secondary refrigerators is 12.43 cu,

ft., and the average age is 12 years.

M

Energy Star

Refrigerators  

11% of all refrigerators surveyed are

Energy Star certified. All Energy Star

refrigerators are primary

refrigerators.

M

Refrigerator Size Refrigerator size ranges from 4.8 to

22 cubic feet for primary

refrigerators and from 5 to 18 cubic

feet for secondary refrigerators.

M

Refrigerator Age   The average age of refrigerators was

9 years, ranging from 2 to 17 years.

M

Source: Residential on‐site surveys

Figure 68 shows the majority of both primary and secondary refrigerators are top mount freezers

representing 65 percent of all refrigerators.
2012 Baseline Study Page 84 

Figure 68. Residential Primary Refrigerator Types  

Source: Residential on‐site surveys

The average sizes by refrigerator type are shown in Figure 69, while average ages by type are shown in

Figure 69. The average size of side‐by‐side refrigerators is 22 cu. ft., while top mount, bottom mount, and

single door without freezer have average sizes of 16.20, 18.60, and 4.80 cu. ft., respectively.  

Figure 69. Residential Size of Refrigerators

Refrigerator Type Primary Refrigerator Average

Size (cu .ft.)  

Secondary Refrigerator Average

Size (cu. ft.)

Source

Side by side 22.00  ‐  M

Top mount freezer 16.20 17.30 M

Bottom mount freezer 18.60  ‐  M

Single door w/o freezer 4.80 ‐  M

Compact (<5 cubic feet)  ‐  5.00 M

Refrigerator Type Unknown 20.40 15.00 M

Source: Residential on‐site surveys

The average age of top mount refrigerator is 9 years, while bottom mount, single door with freezer, and

single door without freezer have average ages of 8, 7, and 2 years, respectively.  

65%

19%

5% 5% 2% 0% 3%

0%

10%

20%

30%

40%

50%

60%

70%
2012 Baseline Study Page 85 

Figure 70. Residential Age of Refrigerators

Refrigerator Type Primary Refrigerator Average

Age (Years)  

Secondary Refrigerator Average

Age (Years)  

Source

Top mount freezer 9 17 M

Bottom mount freezer 8 ‐  M

Single door with freezer 7  ‐  M

Single door w/o freezer 2 ‐  M

Compact (<5 cubic feet)  ‐  8 M

Refrigerator Type Unknown ‐  ‐  M

Source: Residential on‐site surveys
2012 Baseline Study Page 86 

7.11.2 Residential Freezers

Figure 71 summarizes the key findings of the residential freezers surveyed. The online survey found that

70 percent of residential homes surveyed had at least one standalone freezer. One secondary freezer was

found in the surveyed sites. Eighteen percent of freezers surveyed were Energy Star certified.  

Figure 71. Residential Freezers Profile

Measure/Characteristic Key Findings Source

Freezer ‐ Primary   80% of freezers surveyed are chest

freezers. The average size of the primary

freezers is 12.8 cu. ft., while the average

age is 13 years. 93% of primary freezers are

located in conditioned space.

M

Freezer ‐ Secondary   Only one secondary freezer was found in

the surveyed sites. This was a four year

old Upright Frost‐Free freezer in an

unconditioned space.  

M

Energy Star Freezer   18% of freezers surveyed were Energy Star

certified.

M

Freezer Location   93% of primary freezers are located in

conditioned spaces. The one secondary

freezer survey was located in an

unconditioned space.

M

Freezer Age   The average age of freezers is 11 years.

Ages range from 1‐42 years old, with 31%

of freezers being under 4 years old.

M

Source: Residential on‐site surveys

Figure 72 shows the freezer types by primary and secondary freezers. As the table illustrates, eighteen

percent of freezers are Energy Star, all of which are primary freezers.

Figure 72. Residential Freezer Type Distribution

Freezer Type Primary Freezers Secondary Freezers % Energy Star   Source

Chest 81.1%  ‐  12.3% M

Upright Frost Free 12.3% 100.0% 5.9% M

Upright Manual 3.3%  ‐  ‐  M

Freezer Type Unknown 3.3% ‐  ‐  M

Source: Residential on‐site surveys

   
2012 Baseline Study Page 87 

Figure 73 illustrates the age range of freezers. Nearly 60 percent of all freezers are under 10 years old.

Figure 73. Residential Freezer Age Distribution

Source: Residential on‐site surveys

0%

10%

20%

30%

40%

50%

60%

1-10 11-20 21-30 31-40 41+ Age

Unknown
2012 Baseline Study Page 88 

8. Business, Nonprofit and Institutional Market Characterization

This section describes the BNI sector findings obtained from data collection and analysis activities

conducted by the Navigant team. Data were gathered via 30 BNI on‐site surveys during the fall of 2012

and winter of 2013. The on‐site surveys were used to gather information on building characteristics and

baseline saturations of efficient and standard lighting, HVAC, water heating, refrigeration and motors

and compressed air. The results for BNI buildings surveyed are present in the following segments:

7. General Building Information

8. Building Envelope

9. Lighting

10. HVAC

11. Refrigeration

12. Motors and Compressed Air

It should be noted that certain results below were derived from a sample size of 30 on‐site visits and are

used in a qualitative manner. Where possible, Navigant has provided results from additional primary

sources.  

8.1 BNI General Building Information

Surveyors collected information regarding general building characteristics such as age, size, operating

schedules, and more. The following sections present the results for key building characteristics. A

summary of general building information is presented in Figure 74

Figure 74. BNI General Buildings Profile

Characteristic Key Findings Source

Facility Age 85% of BNI facilities were built post‐1970 (weighted by

floor space).

I

The average year of construction was between 1970 and

1999 (weighted by floor space).

I

Building Size 41% of all BNI facilities are 5,000 sq. ft. or less. 14% of

facilities were between 5,001 and 10,000 sq. ft. 18% of

facilities were between 10,000 and 25000 sq. ft.  

8% of facilities were between 25,001 and more.

C

Business

Hours

4% of facilities have weekly hours of 36 hours of less, 22%

have hours between 37 and 48, 17% have weekly hours

between 49 and 72 hours, 14% have hours between 73 and

96, 8% have hours between 97 and 120, and 34% have hours

between 121 and 168, weighted by number of facilities.  

    I
2012 Baseline Study Page 89 

8.1.1 Facility Age (Source I)

The majority (85%) of facilities were built post‐1970. The average year of construction was between 1970

and 1999 (weighted by floor space). The year of construction is related to space heating and space cooling

energy use. Shell construction issues such as leaks, cracks, and drafts due to aging or damaged

construction materials and shifting structures, can increase the energy used for space conditioning,

particularly for older facilities.  

8.1.2 Building Size (Source C)

Forty‐one percent of all BNI facilities are 5,000 sq. ft. or less. Fourteen percent of facilities were between

5,001 and 10,000 sq. ft. Eighteen percent of facilities were between 10,000 and 25000 sq. ft. Eight percent of

facilities were 25,001 sq. ft. or more.

8.1.3 Business Hours (Source I)

Four percent of facilities have weekly hours of 36 hours of less, 22 percent have hours between 37 and 48,

17 percent have weekly hours between 49 and 72 hours, 14 percent have hours between 73 and 96, 8

percent have hours between 97 and 120, and 34 percent have hours between 121 and 168, weighted by

number of facilities.  

8.2 BNI Building Envelope

This section provides details of the commercial building shell measures present at the on‐site surveyed

commercial buildings envelope types. Figure 75  

Figure 75 summarizes the key findings of building shell properties. Envelope characteristics and shell

tightness have a significant interactive effect with space heating and cooling capacities and runtimes.

Improving the overall shell thermal insulation, including glazing, and reducing infiltration, can create an

opportunity to introduce reduced capacity high efficiency mechanical equipment.  
2012 Baseline Study Page 90 

Figure 75. BNI Building Shell Profile

Measure /

Characteristic

Key Findings Source

Wall

Construction

Types

Considering all commercial exterior wall area, 20% has concrete

block construction, 15% has 2x6 wood frame construction, 14%

has 2x6 metal frame construction, and 14% has adiabatic or

shared walls.

N

Exterior Wall

Insulation

Types  

43% of all commercial wall area has batt or blanket insulation.

27% of all commercial wall area does not have any insulation.

N

Roof

Construction

Type

46% of commercial roof area is framed (28% with an attic, 18%

without an attic).  

31% of commercial roof area is constructed with metal decking.

N

Roof Surface 33% of commercial roof area has a metal surface.

16% of commercial roofs have an asphalt roll/shingle surface.

N

Roof Finish 51% of commercial roof area has a flat finish.

24% of commercial roof area has a reflective finish.

N

Roof Colour Only 12% of commercial roof area is dark. N

Floor Types   More than half of commercial floor area is slab‐on‐grade. N

Window

Panes  

99.6% of commercial window area has two panes. N

Window

Glazing

Types

Grocery/supermarket and health care/hospital facilities only

contain clear window glazing, and most non‐food retail centers

only have clear window glazing.

The majority of industrial facilities and restaurants have tinted

window glazing.

The majority of ‘other commercial’ facilities contain reflective

window glazing.

N

Window

Glazing

Features

The majority of commercial windows do not have any glazing

features, with the exception of health care/hospital facilities and

restaurants which each have about 90% low‐E and gas‐filled

windows.

N

Window

Frame Types

Commercial window frames are metal, with the main exception

being vinyl windows in health care/hospital facilities.

N

Window Film   99.8% of commercial window area does not have any added

window film.

N

Source: BNI on‐site surveys
2012 Baseline Study Page 91 

8.2.1 Exterior Wall Construction Types

Figure 76 below describes the findings on commercial exterior wall construction types, broken out by

building type. Considering all commercial building wall areas, 20 percent are concrete block construction,

15 percent are 2x6 wood frame construction, 15 percent are 2x6 metal frame construction, and 15 percent

are adiabatic or shared walls. A relatively small percentage of commercial building wall area is 2x4 wall

construction. The exterior wall construction types vary significantly by building type.  

Figure 76. BNI Exterior Wall Construction Types

Exterior Wall  

Construction Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

2x4 Wood Frame 0% 22% 0% 3% 0% 12% 5% N

2x6 Wood Frame 0% 33% 16% 20% 30% 4% 15% N

2x4 Metal Frame 0% 0% 0% 1% 0% 0% 0% N

2x6 Metal Frame 0% 0% 33% 3% 13% 15% 15% N

8” Solid Concrete 0% 22% 0% 2% 0% 10% 4% N

10” Solid Concrete 0% 0% 0% 7% 0% 4% 4% N

12” Solid Concrete 0% 0% 0% 3% 0% 0% 1% N

Concrete Block 50% 0% 33% 12% 0% 19% 20% N

Brick 0% 0% 0% 0.6% 0% 0% 0.3% N

Corrugated Metal 0% 0% 17% 13% 0% 0% 11% N

Adiabatic (shared

wall) 50% 11% 0% 23% 13% 16% 15% N

Other 0% 0% 0% 2% 0% 0% 1% N

Unidentified 0% 11% 0% 11% 43% 21% 10% N

Total 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys
2012 Baseline Study Page 92 

8.2.2 Exterior Wall Insulation Types

Figure 77 shows commercial exterior wall insulation types, broken out by building type. Forty‐three

percent of all BNI building exterior wall area has batt or blanket insulation. Twenty‐seven percent does

not have any insulation at all. Exterior wall insulation types are moderately similar across commercial

building types.

Figure 77. BNI Exterior Wall Insulation Types

Exterior Wall  

Insulation Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Batt or Blanket 0% 38% 50% 40% 43% 43% 43% N

Expanded Polystyrene 0% 25% 26% 8% 0% 10% 13% N

Loose Fill 0% 25% 0% 2% 0% 0% 2% N

Rigid Board 0% 0% 0% 0.7% 0% 0% 0.4% N

Other 50% 0% 0% 5% 0% 0% 3% N

None 50% 0% 24% 33% 13% 19% 27% N

Unidentified 0% 13% 0% 12% 43% 28% 13% N

Total 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys

8.2.3 Roof Construction Types, Surfaces, Finishes, and Colours

Figure 78 summarizes the BNI roof construction types, surfaces, finishes, and colours among various BNI

building types. Forty‐six percent of BNI roof area is framed, 28 percent has an attic and 18 percent does

not have an attic. Thirty‐one percent of BNI roof area is constructed using metal decking. All three

grocery/supermarket facilities surveyed have a framed roof with an attic. CIBEUS 2000 reports that 31

percent of roof types in Atlantic Provinces are deck type roof construction, 24 percent are framed with

attic type construction, 19 percent are metal truss roof construction, 13 percent are wood truss

construction, and 12 percent are unknown.  

Thirty‐three percent of all BNI roof area has a metal surface. Twenty‐one percent and 16 percent of BNI

roof area has built‐up and asphalt roll/shingle roof surface, respectively.  

Considering all BNI roof area finishes, fifty‐one percent is flat, 24 percent is reflective, and 25 percent is

not identified. Sixty‐seven percent of industrial/manufacturing facility roof area surveyed contains a

reflective finish.

Thirty‐six percent of all BNI roof area is a medium colour, and only twelve percent is dark. All three

grocery/supermarket facilities surveyed have a dark asphalt roll/shingle roof. Non‐food retail BNI

buildings have a fairly even distribution of roof colours.
2012 Baseline Study Page 93 

Figure 78. BNI Roof Construction Types, Surfaces, Finishes, and Colours

Roof Criteria

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Roof Construction Type

Adiabatic (shared

wall) 0% 17% 0% 2% 0% 0% 1% N

Framed With Attic 100% 33% 33% 26% 0% 26% 28% N

Framed Without Attic 0% 33% 33% 5% 35% 29% 18% N

Metal Decking 0% 0% 35% 29% 15% 38% 31% N

Unidentified 0% 17% 0% 39% 50% 8% 22% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Surface

Asphalt Roll/Shingle 100% 67% 0% 24% 0% 14% 16% N

Built‐up 0% 0% 0% 21% 35% 39% 21% N

Metal 0% 0% 66% 17% 15% 34% 33% N

Wood Shingle 0% 0% 0% 0% 0% 6% 2% N

Unidentified 0% 33% 34% 38% 50% 7% 29% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Finish

Flat 100% 67% 34% 48% 50% 84% 51% N

Reflective 0% 0% 66% 13% 0% 0% 24% N

Unidentified 0% 33% 0% 40% 50% 16% 25% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Roof Colour

Light 0% 0% 66% 17% 0% 24% 30% N

Medium 0% 67% 34% 36% 50% 37% 36% N

Dark 100% 0% 0% 10% 0% 28% 12% N

Unidentified 0% 33% 0% 38% 50% 11% 22% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys
2012 Baseline Study Page 94 

8.2.4 Floor

Figure 79 displays the BNI floor types broken down by BNI building type. More than half of BNI floor

area is slab‐on‐grade. 34 percent of health care/hospital facilities’ floor area has an unheated basement.

Figure 79. BNI Floor Types

Floor Type

Grocery/

Supermarket,

Health Care/

Hospital

Industrial/

Manufacturing

Other

Commercial

Restaurant

Retail (non‐food)

Total

Commercial

Source

Adiabatic (shared wall) 0% 0% 0% 2% 0% 0% 1% N

Crawl 0% 0% 0% 3% 0% 0% 1% N

Slab 50% 17% 50% 59% 50% 50% 53% N

Unheated Basement 0% 34% 0% 0% 0% 0% 1% N

Unidentified 50% 50% 50% 36% 50% 50% 44% N

Total by Category 100% 100% 100% 100% 100% 100% 100%

Source: BNI on‐site surveys

8.2.5 Windows/Fenestration

Figure 80 shows that almost all commercial windows have two panes. Over 99 percent of window area

surveyed was two pane.  

Figure 80. BNI Window Panes

Source: BNI on‐site surveys

   

0.6%

99.4%

One Pane

Two Panes
2012 Baseline Study Page 95 

Figure 81 displays the BNI floor types broken down by building type. Grocery/supermarket and health

care/hospital facilities do not contain any treated window glazing. Most non‐food retail centers do not

have any treated window glazing. A majority of industrial facilities and restaurants have tinted window

glazing. The majority of other BNI facilities contain reflective window glazing.

Figure 81. BNI Window Glazing Type

Source: BNI on‐site surveys

Figure 82 presents BNI window glazing features by commercial building type. The majority of BNI

windows do not have any glazing features, with the exception of health care/hospital facilities and

restaurants, which each have about 90 percent low‐E and gas‐filled windows.

Figure 82. BNI Window Glazing Features

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Tinted

Reflective

Clear

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

None

Low E Only

Both (Low E and Gas

Filled)
2012 Baseline Study Page 96 

Figure 83 displays BNI window frame types broken down by building type. Most BNI window frames

are metal, with the main exception being vinyl windows in most health care/hospital facilities.  

Figure 83. BNI Window Frame Types

  

Source: BNI on‐site surveys

Figure 84 shows that almost all BNI windows do not currently have window film.

Figure 84. BNI Window Film

Source: BNI on‐site surveys

   

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Wood

Vinyl

Metal

99.8%

0.2%

No Window Film

Window Film
2012 Baseline Study Page 97 

Almost all BNI windows do not have an exterior solar shade, as shown in Figure 85.

Figure 85. BNI Window Exterior Solar Shade

Source: BNI on‐site surveys

Figure 86 shows BNI window interior shade types broken down by building type. About 68 percent of

window area has moveable interior shades, with most of the remainder not having any interior shades at

all. Of all BNI buildings, only a small percentage of non‐food retail facilities have fixed interior shades.

Figure 86. BNI Window Interior Shades

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

External Solar Shade

No External Solar Shade

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

None

Moveable

Fixed
2012 Baseline Study Page 98 

8.3 BNI Lighting

Figure 87 summarizes the location of the BNI lighting surveyed for ENSC (weighted by wattage). Sixty‐

one percent of all lighting types were indoor while 39 percent were outdoor.

Figure 87. BNI Lighting Location Distribution

Source: BNI on‐site surveys

8.3.1 Indoor Lighting

Figure 88 lists key findings for the BNI indoor lighting profile. Findings include high level results

regarding fixture types, wattages, and control types. Reduced indoor lighting power in interior

conditioned spaces typically increases the heating energy needed in the winter in occupied spaces.  

Figure 88. BNI Indoor Lighting Profile

Measure

Characteristic

Key Findings Source

Fixture Types Fluorescent lights, including both standard and high performance

models, make up 94% of installed lamps and 89% of installed wattage.  

The second most common lamp type is CFLs, which make up 3% of

installed lamps and 1% of installed wattage.

N

Wattage The average wattage of indoor lamps is 36 Watts. The highest average

wattage was in industrial facilities, with an average of 42 Watts per lamp,

followed by ‘Other Commercialʹ sites at an average of 40 Watts per lamp.

N

Control Type Only 6% of indoor BNI lighting has controls beyond a standard manual

switch. 58% of lighting is controlled by an on/off switch, while another

32% is continuous lighting with no controls.

N

Source: BNI on‐site surveys

   

61%

39%

Indoor

Outdoor
2012 Baseline Study Page 99 

Figure 89 summarizes the interior lighting lamp type distribution, weighted by installed wattage. Eighty‐

one percent of all interior lights recorded were standard‐output fluorescent lamps, while another 13

percent were high‐output fluorescent lamps. Standard fluorescent lamps also represented 77 percent of

installed wattage, while high performance HPT8s represented another 12 percent. This illustrates that

fluorescents clearly compose the dominant lighting type in ENSC BNI buildings. Exact distribution

values for all lighting types are listed in Figure 90.

Figure 89. BNI Installed Lamps vs. Installed Wattage

Source: BNI on‐site surveys

Figure 90. BNI Installed Lamps vs. Installed Wattage  

Lamp Type Installed Lamps Installed Wattage Source

Fluorescent 81% 77% N

HPT8 13% 12% N

CFL 3% 1% N

LED 2% 0% N

Incandescent 1% 5% N

U‐Tube Fluorescent 0% 0% N

Quartz/Halogen 0% 0% N

Metal Halide 0% 1% N

Neon 0% 3% N

Source: BNI on‐site surveys

   

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Installed Lamps

Installed Wattage
2012 Baseline Study Page 100 

Fluorescent lamps also dominate the market in terms of fixture types, as shown in Figure 91. Almost 60

percent of indoor fixtures in ENSC’s BNI market are standard fluorescent, while another 22 percent are

high performance T8 fluorescent fixtures. This is in contrast to the 2012 CRA CIBBS telephone survey

indicates that there is still significant potential for efficient fluorescent lighting such as HPT8 and T5HO

fixtures in Nova Scotia, particularly for T5 fixtures. A majority of participants surveyed did not state

‘already installed’ as a barrier to installing fluorescent lighting in their commercial buildings. On average

across efficient fluorescent types, 15 percent of respondents stated that Fluorescent Lighting was Already

Installed at Their Facility, and Other Reasons, were given as primary barriers to implementation.

Figure 91. BNI Distribution of Fixture Type

Lamp Type Fixture Distribution Source

Fluorescent 57% N

HPT8 22% N

CFL 11% N

LED 4% N

Incandescent 3% N

U‐Tube Fluorescent 1% N

Quartz/Halogen 1% N

Metal Halide 0% N

Neon 0% N

Source: BNI on‐site surveys

A breakdown of fixture distribution by building type is illustrated in Figure 92. Standard fluorescent

fixtures are the most common fixture type in industrial facilities, non‐food retail facilities, and ‘other

commercial’ buildings. HPT8s are the most common in grocery stores, while CFLs are most common in

health care facilities and restaurants.
2012 Baseline Study Page 101 

Figure 92. BNI Fixture Distribution by Building Type

Source: BNI on‐site surveys

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fluorescent

HPT8

CFL

LED

Incandescent

U-Tube Fluorescent

Quartz/Halogen

Metal Halide

Neon
2012 Baseline Study Page 102 

8.3.2 Outdoor Lighting

Figure 93 lists key findings for the BNI outdoor lighting profile. Findings include high level results

regarding fixture types, wattages, and control types.  

Figure 93. BNI Outdoor Lighting Profile

Measure

Characteristic

Key Findings Source

Fixture Types One‐quarter of all outdoor fixtures are High Pressure Sodium

Vapor.  

CFLs and Metal Halides each constituted 17% of the rest of the

outdoor lighting fixture types.  

Neon lights made up another 14% of the distribution.  

The rest of the lighting types made up less than 10% each.

N

Wattage Neon lamps are the dominant lamp type by wattage, making up

over 85% of the distribution. The average wattage for all outdoor

lamp types is 138 watts. Wattages range from 10‐100 Watts,

depending on the lamp.

N

Control Type 82% of sites have motion sensors, photocells, or time clocks as

outdoor lighting controls. Of these control types, 86% are time

clocks.

N

Source: BNI on‐site surveys (Source “N”)

As shown in Figure 94, high‐pressure sodium vapor is the most common exterior fixture type,

constituting nearly a quarter of the market share. This is followed by CFLs and metal halides, with nearly

17 percent of the market, respectively.
2012 Baseline Study Page 103 

Figure 94. BNI Fixture Type Distribution

Source: BNI on‐site surveys

As mentioned above, 82 percent of all sites have some form of automated control systems for their

exterior lights, most of which are photocells. Figure 95 shows the control types on exterior lights.

Figure 95. BNI Control Types for Sites with Exterior Lights

Source: BNI on‐site surveys

8.4 BNI HVAC and Water Heating

Figure 96 summarizes the key findings of the BNI HVAC survey

25%

17% 17%

14%

9% 9%

6%

3%

<1% <1% <1%

0%

5%

10%

15%

20%

25%

30%

Fixtures by Type

46%

26%

15%

10%

3%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Photocell Time Clock Manual

Switch

Motion

Sensor

Unknown

Sites With Control Type
2012 Baseline Study Page 104 

Figure 96. BNI HVAC and Water Heating Profile

Measure

Characteristic

Key Findings Source

HVAC

Single‐Zone

HVAC

System Age

The on‐site surveyors identified 477 single‐

zone HVAC systems. Two‐thirds (66%) of

single‐zone HVAC systems are

baseboard/radiant systems followed by unit

ventilators or heaters (13%).  

N

Primary

Heating Fuel

Type

Nearly two‐thirds (65%) of all single‐zone

HVAC systems used electricity as a fuel type.

Fuel oil and liquid propane gas (LPG)

represent 3% each (6% total) of fuel types for

single‐zone HVAC systems.

N

Heating

System Type

More than half (53%) of all facilities sampled

had baseboard heaters. Boilers and electric

resistance heaters were in 19% and 10%,

respectively, of facilities sampled. Air source

heat pumps, furnaces, and radiant heaters

comprise the remaining heating systems and

were in 8% of surveyed facilities.  

N

Boiler

Delivery

Systems

The majority (79%) of boilers were hot water

boilers. 21% of boilers were a steam delivery

system. The mean boiler age was 12.6 years.

N

Hot Water

Circulation

Pump Speed

Controls

28% of all pumps were 0.15 horsepower (hp).

Pumps that were 0.2, 0.25, 0.75, and 3 hp in

size represented 20%, 8%, 8%, and 4% of all

pumps, respectively.

N

Cooling

System Types

According to the Commercial and

Institutional Building Energy Use Survey

(CIBEUS) 2000 reported that in Atlantic

Canada as a whole, 60% of C&I buildings had

mechanical cooling of some kind.  

F

Of the systems that did have a cooling

component, 95% were direct expansion while

5% were chillers. The mean size of all cooling

units was 2.9 tons with a mean efficiency of

9.8 EER and 13 SEER.

N
2012 Baseline Study Page 105 Water Heating

Water

Heating

Equipment

Types

88% of all water heaters were standard water

heaters (i.e., tank water heaters). 8% of water

heaters were instantaneous, or tankless, water

heaters, while 5% were boilers. The average

age of all water heaters was 6.6 years. The

average size water heater was 4.1 kW and

96.5 kBtuh.

N

Water

Heating Fuel

Types

The majority of water heaters were fueled by

electricity. Five percent of units were fueled

by liquid propane gas (LPG), while fuel oil

and natural gas represented 2.5% each (5%

total) of fuel types for water heaters.

N

Water

Heating Pipe

and Tank

Insulation

85% of pipes leading into the water heater

were not insulated, while 12.5% were

insulated. Similarly, 82.5% of tanks were not

externally insulated (e.g., blanket wrap),

while 12.5% were externally insulated39.

N

Water

Heating

Recirculation

Pump

85% of water heaters did not have

recirculation pumps installed, while 10% did

have recirculation pumps installed. 5% of

water heaters were noted as unknown, or

“Don’t Know”, if a recirculation pump was

installed.  

N

Source: BNI on‐site surveys

   

                                                          

39 It should be noted that the vast majority of new water heaters installed typically have insulation integral to the

tank.
2012 Baseline Study Page 106 

The majority (99%) of HVAC systems are single‐zone systems while 1 percent are multi‐zone systems, as

shown in Figure 97. Single‐zone systems are typically considered “simple” systems in the energy code,

and multi‐zone systems are categorized as “complex” systems. Each system type has different

requirements to comply with the energy code, which affects HVAC systems at time of replacement.  

Figure 97. BNI Distribution of Single‐Zone and Multi‐Zone Systems

Source: BNI on‐site surveys  

Multi-Zone, 

1%

Single-Zone, 

99%
2012 Baseline Study Page 107 

8.4.1 Single‐Zone Distribution Systems

The on‐site surveyors identified 477 single‐zone HVAC (SZHVAC) distribution systems and gathered all

available data for each system.40 Figure 98 shows that two‐thirds (66%) of single‐zone HVAC systems are

baseboard/radiant systems followed by unit ventilators or heaters (13%). Room AC, packaged single

zone, and ductless mini‐split, make up 7 percent, 5 percent, and 2 percent of single‐zone systems,

respectively.  

Figure 98. BNI Distribution of Single‐Zone HVAC Systems

Source: BNI on‐site surveys

Figure 99 summarizes the estimated maintenance of single‐zone HVAC units. Seventy percent of the

facilities surveyed estimated that they conduct maintenance on existing equipment every 6 to 12 months.

Twenty‐three percent of those surveyed reported that they conduct maintenance on an “as‐needed” or

“on failure” need. Three percent estimated that they conduct maintenance on their single‐zone HVAC

system every 1 to 5 years, while 4 percent estimated they conducted maintenance on a period of less than

6 months.  

                                                          

40 Complete data was not available for every system.

2-Pipe Fan Coil, 

3%

Baseboard or 

Radiant Heater, 

67%

Ductless Mini 

Split, 2%

Pckg. Single Zone 

(Unitary), 5%

Room AC 

(window unit), 

7%

Split-System 

Single Zone, 1%

Single Zone 

(built-up), 1%

Unit 

Ventilator 

or Heater, 

13%
2012 Baseline Study Page 108 

Figure 99. BNI Single‐Zone HVAC Maintenance

Source: BNI on‐site surveys

8.4.2 Multi‐Zone Systems

Although Navigant included multi‐zone HVAC systems in the BNI survey, very few of the facilities

sampled contained multi‐zone systems. This may be a factor of the relatively small sampled of 30 on‐site

visits in a very diverse market.  HVAC systems that do not serve a dedicated, single zone are classified as

multi‐zone systems. A total of one multi‐zone cooling system was observed. This system was a 10 ton

direct expansion unit. Two multi‐zone air handling units were observed, as well as one multi‐zone

electric heating unit. The heating unit was noted as “Other” by the survey team.  

3%

70%

4%

23%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-5 yrs 6-12mths < 6mths On Failure
2012 Baseline Study Page 109 

8.5 BNI Heating Systems

8.5.1 Heating Fuel Type

The BNI survey indicated that nearly two‐thirds (65%) of all single‐zone HVAC systems used electricity

as a fuel type, as shown in Figure 100. Fuel oil and liquid propane gas (LPG) represent 3 percent each (6%

total) of fuel types for single‐zone HVAC systems. The 2012 CRA CIBBS telephone survey recorded that

electrically operated air source and ground source heat pumps are not commonly installed. These

electrically operated devices are several times more efficient than electric resistance heating devices, and

could have a significant impact in Nova Scotia where 65 percent of heating is electric.

Figure 100. BNI Single‐Zone System Fuel Type

Source: BNI on‐site surveys

65%

3% <1% 3%

27%

0.6%

0%

10%

20%

30%

40%

50%

60%

70%

Electricity Fuel Oil Natural Gas LPG Not Filled Wood
2012 Baseline Study Page 110 

8.5.2 Heating System Type

The BNI survey indicated that more than half (53%) of all facilities sampled had baseboard heaters, as

shown in Figure 101. Boilers and electric resistance heaters comprised 19 percent and 10 percent,

respectively. Air source heat pumps, furnaces, and radiant heaters comprise 8 percent of the remaining

heating systems. This low penetration of air source and ground source heat pumps, coupled with the 65

percent electrical heating fuel type found by Navigant, suggests there is significant potential in Nova

Scotia to reduce energy use and cost using air source and ground source heat pumps as a heating source,

instead of electric resistance heating. Nine percent of facilities did not have a single‐zone heating system.  

Figure 101. BNI Single‐Zone Heating System Type

Source: BNI on‐site surveys

8.5.3 Boilers

Figure 102 summarizes the distribution of boiler delivery systems. The majority (79%) of boilers were hot

water boilers. In cold climates such as Nova Scotia, there may be an opportunity for condensing boilers to

operate closer to their peak condensing thermal efficiency due to lower return water temperatures.

Twenty‐one percent of boilers used a steam delivery system. The mean boiler age was 12.6 years.  

3%

19%

53%

10%

4%

9%

<1% 1%

0%

10%

20%

30%

40%

50%

60%
2012 Baseline Study Page 111 

Figure 102. BNI Boiler Delivery System by Type

Source: BNI on‐site surveys

   

Steam, 

21%

Hot Water, 

79%
2012 Baseline Study Page 112 

Figure 103 summarizes the breakdown of boiler fuel types by delivery mechanism. Two‐thirds of steam

boilers were “fuel oil” type, while one‐third were “electricity resistance” type. Hot water boilers were

distributed among four fuel types, natural gas, waste oil, fuel oil, and electricity resistance, representing

approximate, 9 percent, 18 percent, 64 percent, and 9 percent of fuel types, respectively.  

Figure 103. BNI Boiler Fuel Types by Delivery System

Source: BNI on‐site surveys

Steam Hot Water

Natural Gas 0% 9%

Waste Oil 0% 18%

Fuel Oil 67% 64%

Electricity Resistance 33% 9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent of Boilers
2012 Baseline Study Page 113 

8.5.4 Hot Water Circulation Pumps

Figure 104 summarizes the distribution of hot water circulation pumps in the facilities surveyed. Twenty‐

eight percent of all pumps were 0.15 horsepower (hp). Pumps that were 0.2, 0.25, 0.75, and 3 hp in size

represented 20 percent, 8 percent, 8 percent, and 4 percent, respectively. Twenty percent of hot water

circulation pumps were noted by surveyors as “Don’t Know.”  

Figure 104. BNI Hot Water Circulation Pump Distribution by Size

Source: BNI on‐site surveys

Figure 105 summarizes the control types of hot water circulation pumps. Eighty‐eight percent of all hot

water pumps were one speed, while 12percent were variable speed.  

Figure 105. BNI Hot Water Circulation Pump Speed Control Type

Source: BNI on‐site surveys

Other, 20%

0.1 HP, 12%

0.15 HP, 28%

0.2 HP, 20.00%

0.25 HP, 8%

0.75 HP, 8%

3 HP, 4%

One Speed, 

88%

Variable 

Speed, 12%
2012 Baseline Study Page 114 

8.6 BNI Cooling Systems

Figure 106 summarizes the type of cooling units among all single‐zone HVAC systems. Eighty‐three

percent of all single‐zone systems do not have a cooling component. Of the systems that did have a

cooling component, ninety‐five percent were noted by surveyors to be direct expansion while 5 percent

were chillers. The mean size of all cooling units was observed to be 2.9 tons with a mean efficiency of 9.8

EER and 13 SEER.  

Figure 106. BNI Types of Cooling for Single‐Zone HVAC  

Source: BNI on‐site surveys

   

1%

15%

83%

1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Chiller Direct Expansion None N/A
2012 Baseline Study Page 115 

Figure 107 summarizes the size distribution of all single‐zone cooling systems within both the Chiller and

Direct Expansion categories. Only two instances of single‐zone chillers were recorded by the surveyors,

with unit’s sizes ranging from less than 0.5 tons to between 1 and 3 tons. Direct expansion cooling

systems had a broader range in sizing, with 25 percent of units being less than 0.5 tons. Thirty‐one

percent of units were greater than 0.5 and less than or equal to 1 tons. Thirteen percent of units were

greater than 1 tons but less than or equal to 3 tons. Seventeen percent of direct expansion units were

greater than 3 tons but less than or equal to 5 tons. Fourteen percent of units were greater than 5 tons but

less than or equal to 10 tons. Only 1 percent of direct expansion units were recorded as being greater than

10 tons. For comparison, CIBEUS 2000 reported that in Atlantic Canada as a whole, 60 percent of C&I

buildings had mechanical cooling of some kind.

Figure 107. BNI Distribution of Sizes for Single‐Zone Cooling  

Source: BNI on‐site surveys

Chillers Direct Expansion

10 < ton 0% 1%

 5 < ton <=10 0% 14%

 3 < ton <=5 0% 17%

 1 < ton <=3 50% 13%

0.5 < ton <=1 0% 31%

ton <= 0.5 50% 25%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent of Cooling Systems
2012 Baseline Study Page 116 

Figure 108 summarizes the efficiency (in EER) by system size range for all direct expansion cooling

systems surveyed.  

Figure 108. BNI Direct Expansion Average Efficiency (EER) by Size Range

System Size Rage Average Efficiency Source

EER

ton <= 0.5 11.00 N

0.5 < ton <=1 9.63 N

1 < ton <=3 9.00 N

3 < ton <=5 9.70 N

5 < ton <=10 9.50 N

10 < ton 10.00 N

Source: BNI on‐site surveys

8.6.1 Chillers

Only one chiller was found in the facilities included in the survey. The chiller was an electric fuel type

with no variable speed drive (VSD) installed. The chiller had a capacity of 9 tons.  

8.7 BNI HVAC Controls

Figure 109 displays the most common control types for HVAC systems in the facilities sampled.

Thermostats represent 74 percent of HVAC control types, while programmable thermostats and manual

on/off switches represent 12 percent and 7 percent, respectively. Controls that were noted by the surveyor

as “Always On” or energy management systems (EMS) represent 3 percent and 2 percent respectively.  

Figure 109. BNI HVAC Control Types  

Source: BNI on‐site surveys

3% 1% 2% 7% 12%

74%

0%

10%

20%

30%

40%

50%

60%

70%

80%
2012 Baseline Study Page 117 

8.7.1 Exhaust Fans

Eighty‐seven percent of exhaust fans are controlled manually, as shown in Figure 110. Seven percent of

exhaust fans are controlled by a sensor, while 4 percent were reported as not having a control type (e.g.

always on fans). The control type for 1.5 percent of the exhaust fans surveyed was reported as unknown

or “Don’t Know”.  

Figure 110. BNI Distribution of Exhaust Fan Control Type  

Source: BNI on‐site surveys

Figure 111 summarizes the distribution of exhaust fan drive speed types. Ninety‐seven percent of exhaust

fans were noted to be constant speed type, while 1.5 percent were reported as variable speed fans.

Surveyors reported 1.5 percent of fan speed types as unknown or “Don’t Know”.  

Figure 111. BNI Distribution of Exhaust Fan Drive Speed Type

Source: BNI on‐site surveys

Don't Know, 

1.5%

Manual, 87%

None, 4%

Sensor, 7%

Constant, 97%

Don't Know, 

1.5% Variable, 1.5%
2012 Baseline Study Page 118 

8.7.2 Water Heating

Figure 112 summarizes the types of water heaters in the facilities surveyed. Eighty‐eight percent of all

water heaters were standard water heaters (i.e., tank water heaters). Eight percent of water heaters were

instantaneous, or tankless, water heaters, while 5 percent were boilers. The average age of all water

heaters was 6.6 years. The average size water heater was 4.1 kW and 96.5 kBtuh.

Figure 112. BNI Water Heater Type Distribution  

Source: BNI on‐site surveys

As shown in Figure 113, the majority (cumulatively 90%) of water heaters were fueled by electricity. Five

percent of units were fueled by liquid propane gas (LPG), while fuel oil and natural gas represented 2.5

percent each (5% total) of fuel types for water heaters.  

Figure 113. BNI Distribution of Fuel Types Among Water Heaters  

Source: BNI on‐site surveys

   

Boiler, 5%

Instantaneous, 

8%

Standard 

Water Heater, 

88%

3% 3% 5% 3%

85%

3%

0%

20%

40%

60%

80%

100%

Fuel Oil Natural

Gas

Electricity LPG Electricity LPG

Boiler Instantaneous Standard Water

Heater
2012 Baseline Study Page 119 

Figure 114 shows the distribution of temperature set points for water heater observed. Seventy percent of

units were set between 43 °C (110 °F) and 60 °C (140 °F) (i.e., medium), while 23 percent of units were

above 60 °C (140°F) (i.e., high) and 5 percent of units were set below 110 °F (i.e., low). It should be noted

that Health Canada recommends setting water heaters to a temperature of 50 °C (122 °F) to reduce the

risk of legionellosis41.  

Figure 114. BNI Distribution of Water Heating Temperature Set Point  

Source: BNI on‐site surveys

   

                                                          

41 Legionnaires’ Disease and Pontiac Fever, Health Canada, February 2004, updated October 2012,

Her Majesty the Queen in Right of Canada, represented by the Minister of Health, 2012, page 2,

H13‐7/126‐2012E‐PDF, ISBN: 978‐1‐100‐21309‐5. See also: http://www.hc‐sc.gc.ca/hl‐vs/iyh‐vsv/diseases‐

maladies/legionnaire‐eng.php#a5

23%

5%

70%

3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

High Low Medium Not Filled
2012 Baseline Study Page 120 

Figure 115 summarizes the distribution of water heating insulation either on the tank or on the pipe.

Eighty‐five percent of pipes leading into the water heater were not insulated, while 13 percent were

insulated. Similarly, 83 percent of tanks were not externally insulated (e.g., blanket wrap42), while 13

percent had insulation.  

Figure 115. BNI Distribution of Water Heating Insulation  

Source: BNI on‐site surveys

As shown in Figure 116, 85 percent of water heaters did not have recirculation pumps installed, while 10

percent did have recirculation pumps installed. Five percent of water heaters were noted as unknown, or

“Don’t Know”, if a recirculation pump was installed.  

Figure 116. BNI Water Heat Recirculation Pump  

Source: BNI on‐site surveys

                                                          

42 It should be noted that the vast majority of new water heaters installed typically have insulation integral to the

tank.

3%

85%

13%

5%

83%

13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Don't Know No Yes Don't Know No Yes

Pipe Insulation Tank Insulation

85%

5%

10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

No Don't Know Yes
2012 Baseline Study Page 121 

8.8 BNI Refrigeration

Market Characterization – Refrigeration

Figure 117 lists key findings for the BNI refrigeration profile.  

Figure 117. BNI Refrigeration Profile

Market

Characteristic

Key Findings Source

Compressors Compressors were in industrial facilities, restaurants, and ‘other

commercial’ facilities. However, insufficient data exists to properly

characterize the market in terms of compressors.43

N

Walk‐in space

lighting

Nearly 63% of space lighting in BNI walk‐ins is fluorescent, while

another 25% is incandescent. The rest are unknown.44

N

Walk‐in

lighting

controls

Eighty‐eight percent of lighting in BNI walk‐ins is controlled via a

wall switch. The rest of the control types are unknown.

N

Walk‐in

motor types

A quarter of the motors in BNI walk‐ins are shaded pole, while the

rest are unknown. 14% of motors have evaporative fan controls.

N

Source: BNI on‐site surveys

Figure 118 provides a list of refrigeration equipment in BNI sites. Of the equipment types in BNI facilities,

55 percent are glass door beverage cases, as illustrated in Figure 119.  

Figure 118. BNI Refrigeration Types

Equipment

Code

Equipment Description Size of

Default

Source

GD Glass door beverage cases (e.g. vendor supplied) from

2 to 4 doors

3 doors N

CD Closed door storage cases, one to three doors 2 doors N

UG Upright glass door freezer cases from one to three

doors

2 doors N

CF Coffin type glass top freezer cases (usually ice cream)

typically 6 or 8 ft.

7 ft. N

OT Other:  self‐contained refrigeration not listed above 1 unit N

IV Ice vending machines (hotel‐sized icemaker) – Only self‐contained units1 unit N

Source: BNI on‐site surveys

                                                          

43 Regardless of the types and capacities of refrigeration compressor in Nova Scotia, significant opportunity exists to

implement compressor system controls, such as free cooling refrigeration economizers, and floating head pressure

control with liquid pressure amplification (LPA). Refrigeration economizer controls turn off the compressor and

evaporator when the outdoor air temperature is at or below the cooler or freezer dry bulb temperature setpoint,

making use of cold outdoor air to meet setpoints with lower power circulation fans to ensure proper air mixing.

Floating head pressure controls on larger refrigeration racks in grocery stores can likewise be an additional source of

annual energy savings.

44 There is significant opportunity for LED lighting savings in refrigerated spaces.
2012 Baseline Study Page 122 

Figure 119. BNI Refrigeration Equipment Types Distribution

Source: BNI on‐site surveys

Of all non‐BNI refrigeration equipment type on BNI sites, 76 percent are 1‐door cases, with another 14

percent being chest‐style refrigerators. As shown in Figure 120, the final 10 percent are classified as

‘other.’

Figure 120. Non‐BNI Type Refrigeration Equipment Types Distribution

Source: BNI on‐site surveys

55%

18%

8% 8% 3%

10%

0%

10%

20%

30%

40%

50%

60%

GD CF CD UG IV OT

76%

14% 10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1-Door Chest Other
2012 Baseline Study Page 123 

8.9 BNI Motors and Compressed Air

8.9.1 Market Characterization – Motors and Compressed Air

Figure 121 provides a summary characterization of motor and compressed air measures for the BNI

segments.

Figure 121. BNI Motors and Compressed Air Profile

Measure/Characteristic Key Findings Source

Process motor types The majority (62%) of motors are pump motors and material

handling/conveying motors.

N

Motor controls and

efficiency

All identified motors are standard efficiency and constant

speed.45

N

Air compressor types The majority (82%) of air compressors are reciprocating two‐

stage, single acting (RTS) units.

N

Air compressor hours

of use

RTS type air compressors are using 73 hp about 49 hours a

week.46

N

Air dryer on

compressed air systems

The majority (90%) of air compressors do not have an air

dryer.

N

                                                          

45 It should be noted that, although no high efficient motors or variable‐speed drives were observed in the facilities

surveyed, it would be expected that a portion of all motors and motor control types would be considered efficient.

46 Compressed air is expensive. End uses can often be served at lower cost using electrically‐powered solutions,

rather than compressed air. Additionally, separating end uses to allow each compressed air system to operate at its

lowest possible air pressure setpoint is another way to reduce costs associated with compressed air use. Navigant

recommends investigation of electrically‐powered alternatives to compressed air use, and recommends lowering air

pressure setpoints for existing systems to the extent possible.
2012 Baseline Study Page 124 

8.9.2 Motors

The majority (62%) of motors in the BNI sample are pump motors and material handling/conveying

motors, as shown in Figure 122. Process motors were present in the industrial/manufacturing, retail non‐

food and ‘other commercial’ segments of the BNI sample.  

Figure 122. BNI Distribution of Process Motor Types

Source: BNI on‐site surveys

Figure 123 shows that all of the motors surveyed are constant speed motors and classified as standard

efficiency. All motors have a NEMA type of TEFC (totally enclosed fan cooled). Opportunities exist to

increase efficiency levels of motors through both increasing equipment efficiency and adding variable

controls.

Figure 123. BNI Motor Controls, Efficiency and NEMA Type  

Source: BNI on‐site surveys

Fan/blowing, 

19%

Grinding/milling, 

6%

Material 

handling/conveying

, 26%

Pump, 36%

Tool 

(machine), 

13%

0%

20%

40%

60%

80%

100%

Standard Efficiency Constant Speed NEMA type TEFC
2012 Baseline Study Page 125 

The rated horsepower for process motor units ranges from 0.1hp to 30hp with a mean

horsepower of 3.7hp. The total horsepower by motor type is shown in Figure 124.

Figure 124. BNI Total Motor Horsepower by Type

Motor Type Number of Motor Units Total Horsepower (hp) Source

Fan/blowing 9 40 N

Grinding/milling 3 10.8 N

Material handling/conveying 12 34.5 N

Pump 16 84.5 N

Tool (machine) 1 5.1 N

Total

6 174.8

Source: BNI on‐site surveys

8.9.3 Compressed Air

The majority (82%) of the air compressors surveyed are reciprocating two‐stage, single acting (RTS) units,

as shown in Figure 125. Only one unit out of all air compressors is a smaller capacity single‐stage model

while all other units (91%) are two‐stage compressors, which are more efficient and often longer lasting

than their single‐stage counterparts. All air compressor units were located in the

industrial/manufacturing and ‘other commercial’ segments of the BNI sample. Navigant recommends

investigation of electrically powered alternatives to compressed air use where possible, and recommends

lowering air pressure setpoints for existing systems to the extent possible to control the high cost of

compressed air systems. This can sometimes be achieved by separating unlike compressed air end uses

onto separate systems.  

Figure 125. BNI Distribution of Air Compressor Type

Air Compressor Type Number of Units Percent of Total Source

Reciprocating (Single‐stage, Single‐

acting) (RSS) 1 9% N

Reciprocating (Single‐stage, Double‐

acting) (RST) 0 0% N

Reciprocating (Two‐stage, Double‐acting)

(RTD) 1 9% N

Reciprocating (Two‐stage, Single‐acting)

(RTS) 9 82% N

Rotary Screw (Two‐Stage) 0 0% N

Centrifugal 0 0% N

Unknown 0 0% N
2012 Baseline Study Page 126 

Source: BNI on‐site surveys  

The air compressors in the ‘other commercial’ segment are using a total of 30.5hp about 8 hours per week,

as shown in Figure 126. Figure 127 shows that air compressors in the industrial/manufacturing segment

of the BNI sample are using a total of 50hp about 40 hours per week.

Figure 126. BNI ‘Other Commercial’ Air Compressor Hours of Weekly Use by Horsepower

Source: BNI on‐site surveys

5

2

10.5

3

5

22.5

0

5

10

15

20

25

RSS RTD RTS

Air Compressor Type

hours per week

horsepower

Other 0 0% N

All 11 100%
2012 Baseline Study Page 127 

Figure 127. BNI Industrial Air Compressor Hours of Weekly Use by Horsepower

Source: BNI on‐site surveys

   

38

50

0

10

20

30

40

50

60

RTS

Air Compressor Type

hours per week

horsepower
2012 Baseline Study Page 128 

The majority (90%) of air compressors do not have an air dryer, as shown in Figure 1284. Air dryers

maintain higher quality compressed air, typically necessary for industrial purposes, but also consume

more energy and typically operate continuously unless an efficient cycling air dryer is employed. Out of

all the air compressors, only one RTS unit running at 7.5hp had an air dryer present.  

Figure 128. BNI Air Dryer Verification by Air Compressor Type

Air Compressor Type Number of Units Air Dryer Present

Reciprocating (Single‐stage, Single‐acting) (RSS) 1 No

Reciprocating (Two‐stage, Double‐acting) (RTD) 1 No

Reciprocating (Two‐stage, Single‐acting) (RTS)

8 No

1 Yes

Source: BNI on‐site surveys (Source “N”)

   
2012 Baseline Study Page 129 

Appendix A. References

Corporate Research Associates, Inc. on behalf of Efficiency Nova Scotia, 2011 Socket Study. (2011).

Corporate Research Associates, Inc. on behalf of Navigant, for Efficiency Nova Scotia, 2012 Energy Usage

Study. (2012).

Corporate Research Associates, Inc. on behalf of Navigant, for Efficiency Nova Scotia, Commercial &

Industrial Business Baseline End‐User Study. (2012).

ENERGY STAR website. (2013, April).

http://www.energystar.gov/?c=home_sealing.hm_improvement_insulation_table

Health Canada website. (2013, April). http://www.hc‐sc.gc.ca/hl‐vs/iyh‐vsv/diseases‐

maladies/legionnaire‐eng.php#a5

Natural Resources Canada (NRCan), Office of Energy Efficiency Commercial and Institutional Building

Energy Use Survey 2000. (2002, December).

http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/data_e/cibeus_description.cfm  

Natural Resources Canada (NRCan), Office of Energy Efficiency 2007 Survey of Household Energy Use

(SHEU‐2007). (2007). http://oee.rncan.gc.ca/publications/statistics/sheu‐summary07/sheu.cfm

Natural Resources Canada (NRCan), Office of Energy Efficiency 2008 Commercial and Institutional

Consumption of Energy Survey. (2008).

http://oee.nrcan.gc.ca/publications/statistics/cices08/index.cfm

Natural Resources Canada (NRCan), Survey of Commercial and Institutional Energy Use – Building 2009

(2012, December) http://oee.nrcan.gc.ca/publications/statistics/scieu09/scieu_e.pdf

Natural Resources Canada (NRCan), Comprehensive Energy Use Database, 1990 to 2010.

(2012).http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/comprehensive_tables/list.cfm

Statistics Canada, 2006 Census of Population: Statistics Canada Catalogue no 97‐554‐XCB2006022. (2006).

https://www12.statcan.gc.ca/census‐recensement/2006/dp‐pd/index‐eng.cfm  

Statistics Canada, 2011 Census of Population: Statistics Canada Catalogue no.98‐313‐XCB2011022. (2011).

http://www12.statcan.gc.ca/census‐recensement/2011/dp‐pd/index‐eng.cfm

Navigant Consulting, for Nova Scotia Power Incorporated, 2009 Nova Scotia Residential Baseline Study.

(2010).

   
2012 Baseline Study Page 130 

Appendix B. Residential On‐Site Data Collection Tool

Site ID #

2012 Residential

On‐Site Survey Form

For

ENSC

10/23/2012

Customer Information: (Please Print) 

Customer

Customer Acct #

Phone Number: (           )

Street Address:

City: Postal  Code:

Mailing

City: Postal Code:

County:

Survey Tracking Information: 

Date:

dd/mm/yyyy

Performed by,  First/ Last Name / Initials

Field Survey

Performed:

__ / __ / __ __ __ __ __ __ __ __ __ __

Completeness and

Quality Check”

__ / __ / __ __ __ __ __ __ __ __ __ __

Data Entry Complete: __ / __ / __ __ __ __ __ __ __ __ __ __
2012 Baseline Study Page 131 

HOME CHARACTERISTICS ‐  BY INTERVIEW

INTRODUCTION TO SURVEY

Thank you again for participating in the survey. To start, I’m going to ask you a few questions about your

home and your home’s equipment. After this brief interview, I will walk through your home so that I can

inventory your energy using equipment to record all the specific information about the equipment. I will be

recording information on the following items: lighting, heating and cooling systems, water heating system,

cooking equipment, home electronics, laundry equipment, refrigerators and freezers, fans and miscellaneous

equipment. I will have some questions for you about the equipment and how you use it, so it’s best if we

walk through the survey together.  

1. Which of the following best describes the home/residence (based on surveyor judgment):

 Single-family construction 

 Single-family factory manufactured/modular 

 Single-family mobile home 

 Two family residence (duplex)—traditional structure 

 Apartment (3 + families) --- traditional structure 

 Condominium---traditional structure 

 Row House or any “Other”: (describe_______________________________ ) 

2.  Do the occupants own or rent the residence?

 Own

 Rent 

3. What year was this residence built?  

________ Year Built Interviewer Estimate? 

 If estimate, OK to estimate by decade up to 2000, then estimate to year. 

4. How many square feet is the above‐grade conditioned living space? ____________

5. How many square feet of conditioned living space is below‐grade?  ___________

6. Total square footage of home (Add 4 & 5)   _________________________

7. Is this the main, weekend, or seasonal/vacation residence?  (CHECK ONLY ONE)

 Main Residence

 Weekend Residence

 Seasonal/Vacation Residence

8. In each season, how many people live in this household?

 ____ Summer 

 ____ Winter 

9. How many stories tall is the home?  (check one, based on surveyor judgment)

 One Story
2012 Baseline Study Page 132 

 Split Entry

 Split Level

 Two Stories

 Three or More Stories

For the remainder of the survey, 

answer questions based on 

YOUR OBSERVATION, whenever possible. 

Otherwise, ask the homeowner. 

HOME CHARACTERISTICS – BY OBSERVATION 

10. Estimate square feet of conditioned floor space: (other than garage, basement, and porch)

 _____________sq.ft. 

11. Are any of the following areas used as conditioned living space? (ENTER  ALL THAT APPLY)

___________ Garage (sq. ft).

    ___________ Basement (sq. ft).

    ___________ Porch (sq. ft).

12. Does this residence have an accessible attic?

 Yes/Vented

 Yes/Unvented

 No

13. Does this residence have an accessible crawl space?

 Yes Access/Vents Open

 Yes Access/Vents Closed

 No
2012 Baseline Study Page 133 

LIGHTING

14. Fill out the following information about the indoor and outdoor lighting of the home using the following

codes.

Fixture 

Number 

Number 

of 

identical 

fixtures 

Room 

Type 

Socket 

Type 

Bulb 

Type 

(existing) 

No. of 

bulbs 

per 

fixture 

Watts 

per 

Bulb 

Watts 

per 

Fixture 

Hours 

of use 

Code 

(1) 

standard, 

(2) 

dimmable, 

(3) 3-way 

Hardwire

? [Yes=1 

No=2] 

(1) Exposed 

flood (2) 

recessed 

can (99) 

other 

(1) Control 

(2) Manual, 

(3) Timer, 

(4)Photocell, 

(5)occupanc

y sensor 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

Lighting Room Codes Socket Type Codes Bulb Type Codes Hours of Use Code 

1. Living Room 8. Office 1. Edison Base 1. Incandescent 8. LED 1. Infrequent Use - < 1hr 5. Security Lighting 

2. Kitchen 9. Closet 2. Candelabra 2. T12 Fluorescent 9. No Bulb 2. Low Use - 1 to 2 hrs 6. Dusk-to-Dawn 

3. Dining Room 10. Utility Room 3. Pin Base 3. T8 Fluorescent 88. Don't Know 3. Med. Use - 2-5hrs 7. Not Applicable 

4. Hall/Entry 11. Basement 4. Nightlight 4. High Performance T8 99. Other 4. High Use - > 5hrs 99. Other 

5. Bedroom 12. Exterior 88. Don't know 5. CFL 

6. Bathroom 88. Don't know 99. Other 6. Halogen 

7. Garage 99. Other 7. Xenon 
2012 Baseline Study Page 134 

SPACE HEATING

15. Is the heating system accessible?

 Yes (note (1) ton = 12,000 btu’s)

 No 

16. When was the heating system last serviced or repaired by a qualified HVAC technician?

 less than 6 months 

 6 - 12 months 

 1 to 2 years 

 more than 2 years 

 Never 

 Not Sure 

17. If there is a secondary or backup heating system, how often is it used during winter?   

 Don't have one 

 Never or Rarely 

 Often 

 Always 

(IF RESIDENCE TYPE: multifamily (4 or more attached units) ASK THE NEXT QUESTION: 

18. Does the heating system serve more than one residence?

 Yes 

   No

   Not Sure

   
2012 Baseline Study Page 135 

SPACE HEATING

19. Fill out the following information about the heating system of the home using the following codes:

(Note:  Record the make and model number on the supplemental data sheet.)

(a) Type of System 

(1) Central furnace 

(2) Central heat pump - air 

(3) Central heat pump - water 

(4) Central heat pump - ground 

(5) Built-in electric room units with indiv. 

room controls (incl. baseboards) 

(6) Boiler/Steam/Hot Water 

(7) Dual fuel heat pump (electric & gas) 

(8) Wall or floor furnace 

(9) Portable electric heater 

(10) Portable kerosene heater 

(11) Wood burning stove 

(12) Coal burning stove 

(13) Fireplace 

(14) Solar 

(15) None 

(16) Electric Thermal Storage 

(17) Minisplit Heat Pump 

(18) Other ____________________________ 

(b) Fuel Type 

(1) Electricity 

(2) Natural Gas 

(3) Propane or Bottled Gas 

(4) Fuel Oil 

(5) Wood 

(6) Kerosene 

(7) Coal 

(8) Solar 

(9) Biomass Pellets 

(10) Other 

(c) Data Accuracy

(1) Estimated 

(2) Actual

(d) Air Handler Location

(1) Conditioned space 

(2) Unconditioned space 

(3) Unconditioned attic 

(4) Outdoors 

(5) No air handler 

(6) Not sure 

(e) Location of 

Majority of Duct Work

(1) Conditioned space 

(2) Unconditioned space 

(3) Unconditioned attic 

(4) Outdoors 

(5) No ducts 

 Primary 

System 

Second 

 System 

Third 

      System

1. Type of system (a) 

2. Fuel type (b) 

3. Input capacity kW

3a. Input capacity kBtuh

4. Output capacity (Btuh/hr)

5. Make 

6. Model 

7. Year of Manufacture 

8. Fan HP 

9. Fan Watts (if HP unavailable) 

10. Fan Speed (Constant, Variable) 

11. Is fan ECM? 

12. Energy Star? (yes = 1, no = 2)

13. Air handler location (d) 

14. Duct work location (e) 

15. Duct work insulated 

(yes = 1, no = 2)

16. Programmable thermostat 

 (yes = 1, no = 2)

17. Energy Recovery (yes. no) 
2012 Baseline Study Page 136 

SPACE COOLING

20. Is the cooling system accessible?

 Yes (note (1) ton = 12,000 btu’s) 

 No 

21. When was the cooling system last serviced or repaired by an HVAC technician?

 less than 6 months 

 6 - 12 months 

 1 to 2 years 

 More than 2 years 

 Never 

 Not Sure 

22. If there is a secondary or backup cooling system, how often is it used during summer?   

 Don't have one 

 Never or Rarely 

 Often 

 Always 

(IF RESIDENCE TYPE: multifamily (4 or more attached units) ASK THE NEXT QUESTION 

23. Does the air conditioning system serve more than one residence?

 Yes 

 No

 Not Sure

SPACE COOLING

24. Fill out the following information about the central air conditioning system using the following

codes: (Note:  Record the make and model number on the supplemental data sheet.)
2012 Baseline Study Page 137 

 (a) Type of System (c) Air Handler Location

(1) Central electric, straight cool/packaged (1) Conditioned space 

(2) Central electric, straight cool/split (2) Unconditioned space 

(3) Central heat pump/air-packaged (3) Unconditioned attic 

(4) Central heat pump/air-split (4) Outdoors 

(5) Central heat pump/water or ground source (5) No air handler 

(6) Central Gas (6) Not sure 

(7) Room air conditioning only 

(8) Room air conditioning with heat pump (d) Location of Majority of Duct Work

(9) Room air conditioning with strip heat 

(10) Whole house AC with window/wall mounted 

(11) Minisplit Heat Pump 

(1) Conditioned space 

(12) None (2) Unconditioned space 

(13) Other: __________________________ (3) Unconditioned attic 

 (4) Outdoors 

(b) Data Accuracy (5) No ducts 

Primary

System

Second

System

Third 

System 

1. Type of system (a) 

2. Output Capacity (kBtu/hr) 

3. Output Capacity (tons) 

4. Make 

5. Model 

6. Year of Manufacture 

7. Fan HP 

8. Fan Speed (Variable, Constant) 

9. Is Fan ECM? 

10. SEER

11. EER

12. Energy Star? (yes=1, no=2)

13. Air handler location (c) 

14. Duct work location (d) 

15. If in conditioned space, are ducts 

in need of repair? 

16. Duct work insulated 

 (yes=1, no=2)

17. Programmable thermostat 

(yes=1, no=2)
2012 Baseline Study Page 138 

Water Heating

25. Is the water heating system accessible?

 Yes

 No 

26. Where are the hot water lines located?

 In Slab

 Un-Conditioned Area (Crawl space, attic, basement)

 Conditioned Area (including finished basement and/or attic)

 Other ______________

(IF RESIDENCE TYPE: multifamily (4or more attached units) ASK THE NEXT QUESTION:

27. Is there a separate water heater serving only this residence?

 Yes

 No

      Not Sure

28. Fill out the following information about the water heating system using the following codes:

(Note:  Record the make and model number on the supplemental data sheet.)

(a) System Type (5) Fuel oil 

(1) Electric Resistance (6) Instantaneous Electric 

(2) Heat Pump (7) Instantaneous Gas 

(3) Natural Gas (8) Solar

(4) Propane or Bottled gas (9) Fossil Fuel , Unknown 

Unit 1 Unit 2 

1. Primary fuel type (a) 

2. Back-up fuel type (a)

3. Input Rating (kW) 

3a. Input Rating (kBtu/hr) 

4. Capacity in gallons

5. Tank Insulation R-Value (if labeled)

8. Location (cond = 1, uncond = 2)

9. Tank Wrap (yes = 1, no = 2)

10. Timer (yes = 1, no = 2)

11. Add-on heat pump (yes = 1, no = 2) 

12. Pipe wrap near the water heater 

(yes = 1, no = 2)

 

13. Heat Recovery from Heat Pump? 

14. Drain Water Heat Recovery? 

15. Tank temperature setting(Lo, Med, Hi)

 BRAND NAME MODEL NUMBER ENERGY STAR

 Primary Heater _____________________ ____________________ 
2012 Baseline Study Page 139 

 

 Second Heater _____________________ ____________________ 

WATER HEATING

29. Are there low‐flow showerheads (< 9.5 lpm) installed in the showers?  (Low flow takes 24 seconds or

more to fill a gallon jug.)

Number with  __________ Number without  __________

 

30. Are there faucet aerators installed on the faucets?   (Usually < 7.7 lpm)  (Low flow takes 30 seconds or

more to fill a gallon jug or 15+ seconds to fill 1⁄2 gallon.)

    Number with  __________ Number without  __________   

Refrigerators   

31. Fill out the following information about the refrigerators of the home using the following codes:  

(a) Type (4) Single door with freezer 

(1) Side by side (5) Single door w/o freezer 

(2) Top mount freezer (6) Compact (<5 cubic feet) 

(3) Bottom mount freezer 

Unit 1 Unit 2 Unit 3 

1. Type (a) 

2. Size (cubic feet) 

3. Year of manufacture 

4. Make 

5. Model 

6. Ice Maker? (yes = 1, no = 2) 

7. Energy Star Logo (yes = 1, no = 2) 

8. Unit Plugged In? (yes = 1, no = 2) 

9. Location (cond=1, uncond=2)

Freezers   

32. Fill out the following information about the freezers of the home using the following codes:
2012 Baseline Study Page 140 

(a) Type (b) Seal Condition (c) Coil Condition

(1) Chest (1) Complete (1) Clean 

(2) Upright FF (2) Cracked (2) Dirty 

(3) Upright Manual (3) Missing Sections (3) Inaccessible 

(4) None 

Unit 1 Unit 2 Unit 3 

1. Type (a) 

2. Size (cubic feet) 

3. Make

4. Model 

5. Year of manufacture 

6. Energy Star (yes = 1, no = 2) 

7. Location (cond=1, uncond=2) 

 

Cooking

33. Is there a range/cooktop in the residence?

 Yes  

 No (SKIP TO OVEN)

34. What type of range/cooktop do you primarily use in this home?(CHECK ONLY ONE)

 Electric Range/Cooktop

 Gas Range/Cooktop    

 Propane Range/Cooktop  

 Other (Describe:___________________________)  

 None [SKIP TO OVEN]

35. IF Q40 = Electric Range/Cooktop, Is the electric range/cooktop Energy Star?

 Yes

 No

36. IF Q40 = Electric Range/Cooktop, Is the electric range/cooktop induction?

 Yes

 No

37. How old is the range/cooktop?

_______ (Age in years) Estimate?

 ( If estimate, estimate to increments of 5 years or less) 

38. Is there an oven in the residence?

 Yes  

 No (SKIP TO DISHWASHER)

39. What type of oven do you primarily use in this residence?  (CHECK ONLY ONE)

 Electric Oven 
2012 Baseline Study Page 141 

 Gas Oven 

 Propane Oven 

 Other (Describe:________________ _________ )

 None [SKIP TO DISHWASHER]

40. If [45] Electric Oven, is the electric oven:   

standard

convection  

combination   

41. How old is the oven?

_______ (Age in years) Estimate?

(If estimate, estimate to increments of 5 years or less)

Dishwashers

42. Is there a dishwasher in this residence?

 Yes 

 No 

43. How many dish loads are washed per week?

 _________loads per week 

   

44. How often is the energy saver option used during the drying cycle?

 All the time

 Sometimes

 Never

45. Is the dishwasher Energy Star?

 Yes 

  No 

  Don’t Know 

46. How old is the dishwasher?

_______ (Age in years) Estimate?

 (If estimate, estimate to increments of 5 years or less) 

47. If there is a dishwasher, does it have a heat booster?

 Yes

 No 

 None 

 BRAND NAME MODEL NUMBER 

 

DISHWASHER _____________________ ____________________ 
2012 Baseline Study Page 142 

Laundry   

48. Is there a clothes washer in this residence for private use?

 Yes 

 No 

49. Is the clothes washer Energy Star?

 Yes 

 No 

50. How old is the clothes washer?

_______ (Age in years) Estimate?

 (If estimate, estimate to increments of 5 years or less) 

51. How many loads of laundry are washed per week?   _______

52. If there is a clothes washer, what is the orientation?

 Horizontal (front loading)

 Vertical (top loading)

 None 

Laundry   

53. Is there a clothes dryer in this residence?

 Yes 

      No  

54. If there is a clothes dryer, what type of fuel does it use for heat?

 Electric 

 Natural Gas [SKIP TO HOME ELECTRONICS]

 LP Gas [SKIP TO HOME ELECTRONICS]

 Other (Describe:_____________________) [SKIP TO HOME ELECTRONICS]

      None

55. Is the clothes dryer Energy Star?

 Yes 

      No  

56. How many loads of laundry are dried with the clothes dryer per week?  

   _________

57. How old is the clothes dryer?

_______ (Age in years) Estimate?

(If estimate, estimate to increments of 5 years or less) 

 
2012 Baseline Study Page 143 

 BRAND NAME MODEL NUMBER 

CLOTHES WASHER _____________________ ____________________   

CLOTHES DRYER _____________________ ____________________   
2012 Baseline Study Page 144 

Home Electronics & Other



TELEVISIONS

Display

(CRT/LCD/

Plasma/LED/O

ther)

Format

Standard/

Wide

Size

(Diag.

inches)

Energy

Star?

(Y/N)

EMS Avg. Hours

of Daily Use

Unit 1            

Unit 2

Unit 3

Unit 4

Unit 5

Unit 6

Video Game Systems and DVRs

Type (DVR

OR Video

Game

System)

EMS Avg. Hours

of Daily Use

Unit 1      

Unit 2

Unit 3

Unit 4

Unit 5

Unit 6

Computers & Peripherals

Computers Type

(Desktop

OR

Laptop)

Screen

Size

(diag.

inch)

IF Desktop,

Display: CRT

OR LCD

Additional

Display: CRT

OR LCD

Energy

Star?

(Y/N)

EMS Auto

power

down

enabled?

(Y/N)

Avg.

Hours

On per

Day

Unit 1                

Unit 2

Unit 3

Unit 4

Unit 5

Unit 6

Energy Management System

EMS Type EMS Code

Manual Power Strip  

(user turns off PS after each use)

MPS

Always On Power Strip AOPS

Timer Strip TM

Occupancy Sensor OSS

Smart Strip SMS

Other OT
2012 Baseline Study Page 145 

Computer

Peripherals

Type (Printer,

scanner, external

hard drive, modem,

router)

Energy

Star?

(Y/N)

Energy Management

System‐ (MPS, AOPS,

TM, OSS, SMS, OT)

Average

Hours

On per

Day

Unit 1        

Unit 2

Unit 3

Unit 4

Unit 5

PUMPS

Type

(pool,

spa,

fountain,

pond,

sump,

well)

Years Since

Installation

Horsepower

(HP)

Efficiency

Rating? (s)

Standard, (h)

high, (p)

premium

Motor

Speed? (s)

Single, (d)

double, (v)

variable

Timer? Months

of Use

Avg. Hours

of Daily Use

during

months of

use

      

FANS

Type (Attic,

Whole house,

Ceiling,

Ventilation)

Years Since

Installation

Horsepower

(HP)

Energy

Star?

Efficiency

Rating? (s)

Standard, (h)

high, (p)

premium

Months

of Use

Avg. Hours of Daily

Use during months

of use

    
2012 Baseline Study Page 146 

MISCELLANEOUS  

WALL, ROOF, AND FLOOR INSULATION

58. Fill out the following information about the insulation of the home using the following codes:

    

    

Type (Water bed heater,

humidifiers, dehumidifiers)

Years Since

Installation

Energy

Star?

Gallons

per

hour

Months

of Use

Avg. Hours of

Daily Use during

months of use

    

(a)  Roof Material (M)  Metal or Membrane (b)  Roof Color

(D)  Dark

(L)  Light

(W)  White

(c)  Attic Type

(N)  None (Cathedral

Ceiling)

(V)  Vented Attic

(U)  Unvented Attic

(T)  Tile/Slate/Concrete (W)  Wood Shake

(A)  Asphalt Shingles   

   

Reference

#

Roof Material

(a)

Roof Color

(b)

Attic Type

(c)

Insulation

Depth (in)

Radiant

Barrier?

% Roof Area

1 T     A     M     W D    L    W N     V     U    Y        N   

2 T     A     M     W D    L    W N     V     U    Y        N   

3 T     A     M     W D    L    W N     V     U    Y        N   

Notes:
2012 Baseline Study Page 147 

WINDOWS

59. Fill out the following information about the windows of the home using the following codes:

(a)  Type

(O) Operable

Window

(F) Fixed Window

(B) Bay Window

(G) Garden Window

(FD) French Door

(SD) Sliding Door

(SK) Skylight

(b)  Orientation (c) Shading

(D) Drapes

(B) Blinds

(SS) Solar Screen

(WF) Window Film

(N) None        

(d) Panes

(1)  Single  Pane

(2)  Double Pane

(3)  Triple Pane

(e) Frame

(A)  

Aluminum/m

etal

(W)  Wood/  

       Vinyl

(f) Tinting

(C)  Clear

(T)  Tinted

(L)  Low‐e

(N) North          (NE) Northeast

(S) South          (NW)

Northwest

(E) East            (SE) Southeast

(W) West          (SW) Southwest

(H) Horizontal (skylights only)

Ref

#

Type

(a)

Orient (b) Shade

(c)

Pane

s

(d)

Frame

(e)

Tint

(f)

Total Sq Feet Overhang Storm

Wind

ow

(Y/N?)

(Y/N)

1      1  2  3 A    W C T L      

(a) Orientation (b) Construction (c) Exterior Finish (d) Insulation Type

(N) North          (NE) Northeast (2x4) 2x4 Stick Built (B) Brick/Block   (B) Batts

(S) South          (NW) Northwest (2x6) 2x6 Stick Built (ST) Stucco (L)  Loose  Fill

(E) East            (SE) Southeast (BW) Block Wall (SI) Siding (F) Expanded foam

(W) West          (SW) Southwest    (O) Other

Wall Ref # % of Total Above Grade

Wall Area Construction (b) Ext. Finish

(c)

Rigid

Foam

Insulation?

Insulation Type (d)

1A 2x4    2x6    BW B   ST   SI Y      N

2A 2x4    2x6    BW B   ST   SI Y      N

3A 2x4    2x6    BW B   ST   SI Y      N

Wall Ref # % of Total Below Grade

Wall Area

Insulation

Thickness (in) Rigid Foam Insulation? Insulation Type (d)

1B    

2B    

3B    

Wall Ref # % of Total Floor Area Insulation

Thickness (in) Insulation Type (d)

1T    

2T

3T

Notes:
2012 Baseline Study Page 148 

2      1  2  3 A    W C T L      

3      1  2  3 A    W C T L      

4      1  2  3 A    W C T L      

5     1  2  3 A    W C T L      

6     1  2  3 A    W C T L      

7     1  2  3 A    W C T L      

8     1  2  3 A    W C T L      

9     1  2  3 A    W C T L      

10     1  2  3 A    W C T L      

11     1  2  3 A    W C T L      

12     1  2  3 A    W C T L      

13     1  2  3 A    W C T L      

14     1  2  3 A    W C T L      

15     1  2  3 A    W C T L      

16     1  2  3 A    W C T L      

17     1  2  3 A    W C T L      

18     1  2  3 A    W C T L      

19     1  2  3 A    W C T L      

20     1  2  3 A    W C T L      

Notes:
2012 Baseline Study Page 149 

DOORS

60. Fill out the following information about the doors of the home using the following codes:

(a)  Door Type (b)  Orientation (c)  Quality of Caulking/

(1)  Hollow Wood (1)  North      Weather Stripping

(2)  Solid Wood (2)  South (1)  Good

(3)  Sliding Glass (3)  East/West (2)  Fair

(4)  French Doors (4)  SE (3)  Poor or None

(5)  Other (5)  SW

(6)  Steel (6)  NE

(7)  NW

(8) unconditioned indoors

   

Reference

#

Type

(a)

Orientation

(b)

Quality

(c)

Number of  

Doors

Total

Area

Storm

Door?

1           

2

3

4

5

6

7

8

9

10           
2012 Baseline Study Page 150 

X.   Sketch out the general outline of the residence floor plan and note the dimensions in feet.

N

S

W E
Page 151 

Comments and Notes:

Question # c. Comments/Explanations/Notes
Page 152 

   
Page 153 

Appendix C. Commercial and Industrial On‐Site Data Collection Tool
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