Subcontract Report

NREL/SR-7A2-48318

June 2010

Hawaii Clean Energy Initiative 

Existing Building Energy 

Efficiency Analysis

November 17, 2009 – June 30, 2010

P. Finch and A. Potes

Booz Allen Hamilton

Honolulu, Hawaii
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Subcontract Report

NREL/SR-7A2-48318

June 2010

Hawaii Clean Energy Initiative 

Existing Building Energy 

Efficiency Analysis

November 17, 2009 – June 30, 2010

P. Finch and A. Potes

Booz Allen Hamilton

Honolulu, Hawaii

NREL Technical Monitor: S. Busche

Prepared under Subcontract No. KLDJ-6-66282-07
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iii

Acknowledgments

Booz Allen Hamilton would like to thank the Hawaiian Electric Company, the Kauai Island 

Utility Cooperative, the Hawaii Department of Business, Economic Development and Tourism, 

and The Gas Company for their assistance in the data gathering process. Booz Allen Hamilton 

would also like to thank the National Renewable Energy Laboratory for all of its work 

throughout the editing process. Without these significant contributions, the analysis conducted in 

this report would not have been possible.
iv

List of Acronyms

AC air-conditioning

ACEEE American Council for an Energy Efficient Economy

BAH Booz Allen Hamilton

CFL compact fluorescent lighting

DBEDT Hawaiian Department of Business, Economic 

Development and Tourism

DOE Department of Energy

DSM demand side management

EB existing buildings

EEPS Energy Efficiency Portfolio Standard

EER energy efficiency ratio

EMS energy management system

EPA Environmental Protection Agency

FEMP Federal Energy Management Program

GWh gigawatt-hour

HCEI Hawaii Clean Energy Initiative

HECO Hawaiian Electric Company

HELCO Hawaiian Electric Light Company

IRP Integrated Resource Plan

KIUC Kauai Island Utility Cooperative

kWh kilowatt-hour

LED light emitting diode

MECO Maui Electric Company

MWh megawatt-hour

NAECA National Appliance Energy Conservation Act

NC new construction

NREL National Renewable Energy Laboratory

O&M operations and maintenance

PBF Public Benefits Fund

SEER Seasonal Energy Efficiency Rating

SF square feet

SWAC seawater air-conditioning

WH water heating
v

Executive Summary

In June 2009, the State of Hawaii enacted an Energy Efficiency Portfolio Standard (EEPS) with a 

target of 4,300 gigawatt-hours (GWh) by 2030 (Hawaii 2009). Upon setting this goal, the Hawaii 

Clean Energy Initiative, Booz Allen Hamilton (BAH), and the National Renewable Energy 

Laboratory (NREL), working with select local stakeholders, partnered to execute the first key 

step toward attaining the EEPS goal: the creation of a high-resolution roadmap outlining key 

areas of potential electricity savings. This roadmap was divided into two core elements: savings 

from new construction and savings from existing buildings. After attaining feedback from the 

stakeholders, it was determined that BAH would focus primarily on the existing building 

analysis, while NREL would focus on new construction forecasting. This report presents the 

results of the Booz Allen Hamilton study on the existing building stock of Hawaii, along with 

conclusions on the key drivers of potential energy efficiency savings and on the steps necessary 

to attain them.

In deconstructing the various types of buildings in the state along with their respective energy 

footprints, Booz Allen Hamilton relied heavily on contributions from various stakeholders, 

including the Hawaiian Electric Company, Inc. (HECO), the Kauai Island Utility Cooperative 

(KIUC), the Department of Business, Economic Development and Tourism (DBEDT), and The 

Gas Company, among others. Combining the data received from these parties, we determined 

that the highest areas of energy intensity among all building usage categories were concentrated 

in six specific sectors: (1) offices, (2) hospitality, (3) retail on the commercial side, (4) single 

family homes, (5) multifamily homes, and (6) high-rises on the residential side. It was therefore 

determined that, given resource and time constraints, any analysis of potential existing building 

efficiency savings must begin with these key sectors, which combine to total 62% of the 

electricity usage in the state overall (BAH 2009b). 

Once the dominant energy users were identified, Booz Allen Hamilton evaluated existing state 

data to determine where best to supplement it with national building technologies and building 

operation studies. We identified a need for additional state data and worked with the HECO 

companies and KIUC to administer a limited appliance saturation survey for the Hawaii 

commercial sector (BAH 2009a). Aggregating these data by building type, we developed 

building profiles representing both “average” baseline buildings and “efficient” buildings based 

off of the most efficient currently available technologies.1

 

1 The commercial baseline and efficiency building profiles include technologies for the following end uses: cooling, 

lighting, water heating, fans and motors, building controls, building envelope and computers. For the residential 

sector, we model cooling, lighting, water heating, building envelope refrigeration and other major appliances. Some 

combination of these applies to all building types. Full details of calculations and assumptions are available in 

Appendix I.

Electricity savings by building type 

and end use were calculated as the difference in the electricity use between the building profiles. 

These savings estimates were then adjusted to include the full building stock for each of the six 

building types. 
vi

Figure ES-1. Electricity savings as a percent of 2007 Hawaii electricity usage = 13.5%

Ultimately, the study determines that the estimated potential savings from the six modeled 

building types (single family, multifamily below 20 units, high-rises above 20 units, offices, 

retail, and hospitality) are approximately 1,300 GWh/yr, or 13.5% of 2007 Hawaii electricity use 

(Figure ES-1). HECO projects annual energy use to increase to 14,300 GWh/yr by 2030, and the 

state energy efficiency target is 30% of this amount, or 4,300 GWh (HECO 2005). Since our 

model is limited to six building types and based on current energy use, we adjust our results to 

account for the entire building stock, the growth of existing building loads, and building stock 

turnover to 2030. 

After these adjustments, we estimate that potential electricity savings from existing buildings in 

2030 are between 2,100 GWh (15% of 2030 electricity use) and 3,100 GWh (22% of 2030 

electricity use). These savings account for approximately half to three-fourths of the 30% state 

efficiency target.2 Assuming a levelized cost of $83 per megawatt-hour (MWh) saved3

 

2 The exact value depends on the contribution of additional loads from existing buildings to electricity growth 

compared to that of new construction. 

(Rogers, 

Messenger, and Bender 2005), the estimated investment needed to attain required EEPS savings 

is approximately $4.1 billion by 2030, or $196 million per year. It is anticipated that the private 

sector will require incentives to make this investment, but the size of the incentives needed is not 

known at the present time. 

3 Due to the extremely high levels of efficiency being targeted by the state, this figure represents a premium over the 

figure noted in the Rogers, Messenger, and Bender California program study. The first 10% of efficiency attained 

per building is assumed to cost $50 per MWh, with the per MWh price increasing incrementally as you approach 

what is technically achievable. This results in an average of $83 per MWh of efficiency for buildings attaining an 

average electricity use reduction of 25%.
vii

Table ES-1. Top 5 Individual Efficiency Measure Savings

Building Type and End Use GWh Savings Potential % of 2007 Electricity Use4

Single Family Water Heating 250 GWh 2.5%

Single Family Lighting 194 GWh 2%

Retail Lighting 85 GWh 1%

Office Cooling 72 GWh 1%

Single Family Refrigeration 69 GWh 1%

Given the significant projected cost of attaining the EEPS target and constraints on the state 

efficiency budget, we anticipate that finding additional sources of private investment for 

efficiency efforts in the state will be critical to successfully meet the efficiency goals. 

Additionally, attaining the efficiency goals will require building retrofits on the order of 

magnitude of 80% of the current building stock in the state, as well as building retirements and 

new construction equal to approximately 20% of the current building stock. Enrollment in 

existing efficiency programs lags this 80% estimate by a substantial margin, with below 20% of 

the existing building stock currently engaged in the state efficiency programs. Therefore,

outreach and education programs on the benefits of efficiency to building owners should be 

another key area of focus for the state to move forward.

 

4 Due to the uncertain nature of how load growth and efficiency by category type will fluctuate, projections of what 

each efficiency measure savings will be as a fraction of 2030 energy usage is outlined here. 
viii

Table of Contents

Acknowledgments..........................................................................................................................iii

List of Acronyms ........................................................................................................................... iv

Executive Summary........................................................................................................................ v

Table of Contents.........................................................................................................................viii

List of Figures................................................................................................................................ ix

List of Tables ................................................................................................................................. ix

Introduction..................................................................................................................................... 1

Methodology................................................................................................................................... 2

Results............................................................................................................................................. 6

Based on 2007 Electricity Usage Levels .................................................................................... 6

Results Adjusted for Load Growth (2008–2030)...................................................................... 12

Conclusions................................................................................................................................... 13

References..................................................................................................................................... 17

Appendix I: Study Assumptions and Calculations ....................................................................... 20

Commercial Sector Modeling................................................................................................... 22

Commercial Cooling............................................................................................................. 22

Commercial Lighting............................................................................................................ 23

Commercial Water Heating .................................................................................................. 24

Commercial Controls............................................................................................................ 26

Commercial Fans and Motors............................................................................................... 27

Commercial Building Envelope............................................................................................ 28

Commercial Computers and Data Centers............................................................................ 30

Residential Sector Modeling..................................................................................................... 31

Residential Refrigeration ...................................................................................................... 32

Residential Cooling............................................................................................................... 33

Residential Lighting.............................................................................................................. 34

Residential Water Heating .................................................................................................... 35

Residential Building Envelope ............................................................................................. 36

Residential Appliances.......................................................................................................... 37

Appendix II: “Hawaii Building Stock Mapping and the Way Forward,” (Booz Allen Hamilton, 

April 22, 2009)........................................................................................................................ 40

Residential................................................................................................................................. 40

Commercial............................................................................................................................... 41

Combined.................................................................................................................................. 43
ix

List of Figures

Figure 1. Analysis methodology .................................................................................................. 3

Figure 2. Hawaii commercial existing building efficiency curve and building profile 

methodology summary.................................................................................................. 4

Figure 3. Hawaii 2030 load growth scenarios.............................................................................. 6

Figure 4. 2007 Electricity use in the state of Hawaii (MWh) ...................................................... 7

Figure 5. Electricity savings as a percent of 2007 Hawaii electricity usage = 13.5% ................. 8

Figure 6. Electricity savings by end use as a percent of 2007 Hawaii electricity usage.............. 8

Figure 7. Electricity savings by island as a percent of 2007 Hawaii electricity use .................... 9

Figure 8. High-rise profile savings............................................................................................... 9

Figure 9. Single family profile savings...................................................................................... 10

Figure 10. Multifamily profile savings......................................................................................... 10

Figure 11. Large office profile savings........................................................................................ 10

Figure 12. Small office profile savings........................................................................................ 11

Figure 13. Retail profile savings.................................................................................................. 11

Figure 14. Hospitality profile savings.......................................................................................... 11

Figure 15. 2007 Electricity use in the state of Hawaii (MWh) .................................................... 43

List of Tables

Table 1. Top 5 Individual Efficiency Measure Savings............................................................ 12

Table 2. Total Aggregate Savings by Building Type and Technology (State-wide)................ 20

Table 3. Baseline Commercial Cooling Assumptions.............................................................. 23

Table 4. Efficient Commercial Cooling Assumptions.............................................................. 23

Table 5. Baseline Commercial Lighting Assumptions ............................................................. 24

Table 6. Baseline Commercial Water Heating Assumptions.................................................... 25

Table 7. Commercial Water Heater Efficiency Values............................................................. 26

Table 8. Efficient Commercial Building Water Heating Electricity Load ............................... 26

Table 9. Saturation of Building Controls, Baseline Case ......................................................... 27

Table 10. Control Savings........................................................................................................... 27

Table 11. Baseline Fan and Motor Assumptions (Number of Fans and Motors Per building) .. 28

Table 12. Efficient Fan and Motor Assumption ......................................................................... 28

Table 13. Saturation of Insulation Types for Building Envelope, Baseline Case....................... 29

Table 14. Efficient Commercial Building Envelope Assumptions............................................. 30

Table 15. Baseline Commercial Computer and Data Center Assumptions................................ 31

Table 16. Efficient Commercial Computer and Data Center Assumptions................................ 31

Table 17. Baseline Residential Refrigeration Assumptions........................................................ 32

Table 18. Efficient Residential Building Refrigeration Assumptions ........................................ 32

Table 19. Baseline Residential Building Cooling Assumptions................................................. 33
x

Table 20. Efficient Residential Building Cooling Assumptions................................................. 34

Table 21. Baseline Residential Lighting Assumptions............................................................... 34

Table 22. Efficient Residential Lighting Assumptions (Average Number of Lamps Per 

Building)..................................................................................................................... 35

Table 23. Baseline Residential Water Heating Assumptions ..................................................... 35

Table 24. Efficient Residential Water Heating Assumptions..................................................... 36

Table 25. Baseline Residential Building Envelope Assumptions............................................... 36

Table 26. Efficient Residential Building Envelope Assumptions............................................... 37

Table 27. Baseline Residential Appliance Assumptions ............................................................ 38

Table 28. Efficient Residential Appliance Assumptions............................................................ 39

Table 29. Residential Electricity Demand, by Island (2000)...................................................... 40

Table 30. EIA Electricity Demand, by Sector (2007)................................................................. 41

Table 31. Residential Energy Demand Allocation (Base Year) and 2007 Demand Levels ....... 41

Table 32. Commercial Electricity Demand Allocation by Sector and Island (% of Total 

Commercial Demand, Base Year) .............................................................................. 42

Table 33. Commercial Electricity Demand by Sector and Island (2007)................................... 42
1

Introduction

The Hawaii Energy Efficiency Portfolio Standard (EEPS) was enacted in bill HB 1464 of the 

2009 Hawaii State Legislature (Hawaii 2009). This legislation mandates that by 2030 the state 

reduce its annual electricity consumption by 4,300 gigawatt-hours (GWh), or 30% of the 

Hawaiian Electric Company (HECO) forecasted “Business as Usual” 2030 energy consumption 

of 14,300 GWh/yr (HECO 2008, HELCO 2007, MECO 2007, KIUC 2008). Currently, the state 

is funding energy efficiency programs through a Public Benefits Fund (PBF) at a rate of 

approximately $20 million per year to the commercial and residential building sectors, or $21 

million total including Kauai Island Utility Cooperative (KIUC) programs, which are 

administered separately from the PBF’s (HCEI 2009, KIUC 2010). To inform future policy 

initiatives and funding, the Hawaii Clean Energy Initiative created a roadmap to determine how 

the state is to meet the 2030 EEPS target. For the purposes of this analysis, all savings achieved 

are assumed to be maintained until the target date of 2030, so that savings from initial 

investments do not depreciate over time. However, we fully acknowledge that significant 

operations and maintenance (O&M) and retro/recommissioning costs may accrue over time, and 

that efforts in this regard are essential to the success of attaining the EEPS.

The roadmap analysis was divided into two components: efficiency savings from new 

construction and energy savings from existing buildings. When the EEPS goal was set, the 

projected contribution in efficiency savings from each of these components was estimated to be 

73% (3,150 GWh annually in 2030) from existing buildings and 27% (1,150 GWh annually in 

2030) from new construction. The National Renewable Energy Laboratory (NREL) undertook 

new construction modeling, and this study represents Booz Allen Hamilton’s (BAH’s) analysis 

(with significant input from local stakeholders) of existing buildings.
2

Given the limited resources available to the Hawaii PBF and KIUC to devote to efficiency 

programming to meet the EEPS, a cost-effective distribution of resources that focuses on the 

building and technology types with the greatest potential electricity savings is essential. The 

purpose of this study is twofold: to identify the building types and current building technologies 

with the greatest opportunities for electricity savings, and to estimate potential electricity savings 

from all existing buildings in 2030. While past estimates of Hawaii efficiency potential exist, 

they are somewhat dated (HECO 1994, HECO 2004), and they were not conducted in the context 

of the EEPS. The ultimate goal of this study is to assist program managers in making informed 

decisions on the optimal building types and end use technologies on which to devote funds to 

maximize potential electricity savings.5

Methodology

See Appendix I for the full list of end use technologies 

evaluated.

In designing our study, BAH followed a six-step process (Figure 1). Using data provided from 

the state’s four electrical utilities, the Hawaii Department of Business, Economic Development 

and Tourism (DBEDT), and The Gas Company, we began by mapping electricity usage across 

the entire building stock, by building type (Step1). Next, given time and resource restrictions, we 

screened for the largest efficiency drivers and built electricity use profiles of “average” and 

“efficient” versions of these buildings and technology types (Steps 2 and 3). We compared these 

building models to estimate potential electricity savings (Step 4) and scaled up the savings to 

reflect the potential efficiency available from the entire building stock (Step 5). The goal of the 

analysis is to identify building types and efficiency measures that will be the primary drivers of 

electricity savings across the entire building stock and to compare them to the EEPS goal. Once 

the largest impact areas of focus were identified, we highlighted secondary areas of focus and 

any behavioral changes that may be necessary to facilitate energy savings to ensure a holistic 

approach to forecasting potential savings (Step 6).

 

5 It is essential to note that, unlike previous Hawaii efficiency studies, cost-effectiveness is not emphasized as an 

essential component of this building technology analysis. Instead, the emphasis is on attainment of the EEPS goal 

and estimating the amount of funding needed to attain the required level of savings. A quantitative cost-effectiveness 

screen is not applied because the basis of this study is to identify the building and technology types required to reach 

the 30% target. While we acknowledge that not all measures necessary to attain the target may currently be cost 

effective and that omitting these measures would leave the state well short of the necessary goal, we also realize that 

cost-effectiveness for individual measures varies especially widely on a per-building basis for existing building

retrofits. Therefore, we do not want to rule out technology types that may end up being cost effective in certain 

situations. Furthermore, given the long duration of the study period, we expect cost-effectiveness to change over 

time for various measures. Developing long-term technology cost curves was deemed outside of the scope of this 

study in its original conception. 
3

Figure 1. Analysis methodology

Steps 2, 3, and 4 require estimating energy savings potential for selected building types, end use 

technologies, and efficiency measures. We assembled models of individual buildings for two 

scenarios: a baseline building and an efficient building. For the commercial sector, we overcame 

limits on the availability of data by aggregating older, Hawaii-specific building efficiency studies 

with newer Hawaii building survey data and, where necessary, more recent non-Hawaii data. 

Assuming that equipment efficiencies, sizes, and saturations are normally distributed across the 

existing building stock, we used older data as representative of the left side of the efficiency 

curve and more recent data as representative of the right side of the curve. Thus, the average of 

these data represents our best estimate of the average building in the existing stock (Figure 2). 

For commercial buildings, we assumed that values from the 1994 HECO Commercial EnergyUse Survey (HECO 1994) and the HECO 2004 Integrated Resource Plan-3 Demand-Side 

Management Report (HECO 2004) represent the least efficient buildings, or the left side of the 

building stock efficiency curve. To construct an estimate of the most efficient end of the 

commercial building efficiency curve, Booz Allen teamed with HECO and KIUC to administer a 

limited appliance saturation survey (BAH 2009a), which supplied the team with data on the 

high-performing customers currently enrolled in HECO and KIUC’s building efficiency 

programs.

For residential buildings, appliance sizes and unit energy consumptions were derived from the 

2008 HECO Residential Appliance Survey (HECO 2009b). These values were not averaged,

since the 2008 appliance survey represents a more recent distribution of equipment in the current 

building stock, but they were compared to the HECO 2004 residential building profiles (HECO 

2004) and the 2005 KIUC energy efficiency study (KEMA 2005) for consistency.
4

Figure 2. Hawaii commercial existing building efficiency

curve and building profile methodology summary

Once baseline models were built for each building type, efficient building profiles were 

constructed to calculate electricity savings potential by technology and building type. Values for 

electricity savings by efficiency measure were taken from a combination of Federal Energy 

Management Program (FEMP) equipment requirements (FEMP 2010), the 2005 KIUC 

efficiency study (KEMA 2005), the 2008 study on water heater demand-side programs (KEMA 

2008), and the 2004 HECO IRP-3 modeling results (HECO 2004). The calculations and values 

for each individual baseline and efficient building model (including values for end use 

efficiencies, sizes, saturations, and efficiency measure savings) are detailed in Appendix I.

To avoid overreliance on future technology development in our forecast, we excluded

technologies that are not yet commercially viable from the initial building models. However, 

given that some future technology adoption is likely, we examined potential savings from 

second-generation technologies, such as seawater air-conditioning (SWAC) and Light-emitting 

diodes (LEDs), as an addendum to the initial analysis, to project the possible impact of future 

technologies. 

Once the per-building efficiency potential was calculated by building type, we scaled up from 

individual buildings to the entire existing building stock by multiplying electricity savings by the 

number of buildings for each building type. The number of buildings for each building type is 

calculated by dividing the total 2007 electricity use per building type (BAH 2009) by electricity 

use per baseline building profile developed in this analysis. To correct for building retirement, 

the model assumes a 1% per year building retirement rate (equivalent to that assumed by the 

• The area under the left side of the curve represents the saturation and energy usage 

of technologies for the most inefficient buildings in the state.

• The area under the right side of the curve represents the saturation and energy usage 

of technologies for the most efficient buildings in the state.

• The goal is to capture the most prevalent and efficient of the full range of 

technologies in the current building stock by averaging the most and least efficient 

technology saturations and energy usages for each individual technology type within 

a given building class.
5

EEPS6

Next, as the modeled building sectors represent only 62% of the electricity used by the existing 

building stock, we scaled up the aggregated results to estimate the potential efficiency savings 

available from the entire existing building stock. We assumed that the modeled buildings are 

representative of the entire building stock and that energy savings will be available at the same 

rate for the entire excluded building stock. The largest portion of the remaining 38% of the 

building stock electricity use consists of military residential and office buildings (12% of 2007 

electricity use), which is largely similar to the sectors included in this analysis. While we realize 

that there may be some deviation in savings across the remaining 26%, we believe that the six 

sectors evaluated in this report, plus the military sector, strongly correlate with the end results. 

While building-specific differences may alter the end numbers slightly, we do not believe the 

differences are significant to directionally alter the outcomes of this study. 

). As values for energy use are available by island, we also calculated aggregate savings 

by island and building type. 

Finally, we adjusted for existing building load growth from 2010 to 2030. As technology 

saturations change into the future (i.e., more buildings have cooling equipment) and some 

technologies become more energy intensive (e.g., some television models and added 

entertainment systems), the efficiency savings potential from existing buildings will increase. 

Because it is difficult to accurately estimate the increase in existing building load growth from 

the expected growth in overall energy usage, we estimated a range of potential savings (and 

potential contribution to the EEPS goal) in 2030 (Figure 3). The lower bound of the range 

represents zero existing building load growth and the upper bound of the range represents 

electrical load growth at a ratio of 30% from new construction and 70% from existing buildings.7

 

6 Based on 2000 U.S Census building age data, average lifespan of a building in the United States is 70 years; over 

20 years approximately two-sevenths of the building stock would turn over, or ~1% per year, [CENSUS 2000a].

7 Based on historical population growth figures (DBEDT 2008b), utility IRP forecasted energy demand (HECO 

2008, KIUC 2008, MECO 2007, HELCO 2007), and BAH-estimated building energy usage (Appendix I)
6 

Figure 3. Hawaii 2030 load growth scenarios 

Results 

Based on 2007 Electricity Usage Levels 

Figure 4 represents all of the electricity usage drawn from the Hawaii grid in 2007 (post-line 

loss). The residential sector comprises roughly 32% of this electricity use and includes single 

family housing, multifamily housing (less than 20 units) and high-rises (20 or more units). The 

remaining 68% is consumed by twelve commercial sector uses. The aggregate mapping results 

show that Hawaii electricity use in 2007 was approximately 9,900 GWh/yr and is forecast to 

grow up to roughly 14,300 GWh/yr by 2030 (BAH 2009b; HECO 2008, MECO 2007, HELCO 

2007, KIUC 2008).8 

 

 

8

 This 14,300 GWh figure reflects demand forecasts in the HECO IRP-4 and the HELCO, MECO, and KIUC IRP-3s 

to set a baseline for the 2010–2030 time frame. It does not make allowances for the potential increasing adoption of 

plug-in hybrid vehicles moving forward. Increased use of plug-in vehicles may elevate demand for both home and 

business electricity usage, as the vast majority of vehicle charging will take place at these locations. This did not 

impact our forecasts in this analysis, as the goal of 4,300 GWh was set independent of forecasts for PHEV demand, 

and no assumed efficiency gains were forecast to come from electric vehicle efficiency improvements over the 

2010–2030 time frame. GWh per year 

Hawaii energy 

use is 

expected to 

increase to 

14,300 GWh 

by 2030 

All load growth 

is due to New 

Construction

New 

Construction 

= 30% of 

load growth 

Existing 

Buildings = 

70% of load 

growth 

Upper Bound: Max potential 

existing building load growth 

Lower Bound: No existing 

building load growth 

New 

Construction 

Growth 

New 

Construction 

Growth 

Existing 

Buildings 

New Construction 

Replacing EB 

2010 2015 2020 2025 2030 

15,000 

13,000 

11,000 

9,000 

7,000 

5,000 

Existing 

Buildings 

New Construction 

Replacing EB 

2010 2015 2020 2025 2030 

15,000

13,000 

11,000 

9,000 

7,000 

5,000 

Existing 

Building load 

growth 
7

Figure 4. 2007 Electricity use in the state of Hawaii (MWh)

Based on the various magnitudes of energy usage indicated by the mapping effort, BAH selected 

the six highlighted building types that, combined, use 62% of Hawaii’s electricity profile 

(Figure 4).9

Similar to screening for large building types, we limited our analysis to large energy usage 

drivers within each building type. Cooling, lighting, water heating, fans and motors, building 

controls, building envelope and computers were modeled for commercial buildings. Cooling, 

lighting, water heating, building envelope, refrigeration and other major appliances were 

modeled for residential buildings.

For this analysis, the military sector, although large, was excluded from our detailed 

review, as it is not under state jurisdiction. All sectors were not evaluated due to time and 

budgetary constraints, but given the significant footprint of the selected sectors in combination 

with that of the military, Booz Allen, in consultation with the stakeholders, determined that they 

represented a reasonable proxy for the entire Hawaii building stock and should be considered 

first. 

 

9 By sector: single family homes (attached + detached): 2.0 million MWh, or 20.5%; multifamily homes (less than 

20 units): 5.5 million MWh, or 5.6%; high-rise: 6 million MWh, or 6.1%; retail: 1.2 million MWh, or 12.1%; office: 

1 million MWh, or 10.1%; hospitality: 7.6 million, or 7.7% 
8

Figure 5. Electricity savings as a percent of 2007 Hawaii electricity usage = 13.5%

Should the advised retrofits be adopted across 80% of Hawaii single family, multifamily, office, 

retail and hospitality existing buildings, the aggregate savings potential is approximately 1,300 

GWh, or 13.5% of the total 2007 Hawaii electricity use. By building type (See Figure 5), single 

family homes represent the largest amount of savings potential at 6.6% of 2007 electricity use. 

The remaining potential savings is represented by retail ( 2.5% of 2007 electricity use), highrises (1.2%), the hospitality sector (1.1%), multifamily homes (1.1%), and offices (1%).

Figure 6. Electricity savings by end use as a percent of 2007 Hawaii electricity usage

By end use (Figure 6), lighting is the technology with the greatest energy savings potential, at 

3.6% of 2007 electricity use. The remaining potential savings is represented by water heating 

(2.8% of 2007 electricity use); cooling (2.7%); appliances, including refrigeration (2.1%); 

building envelope improvements (1.1%); lighting and building temperature controls (1%); fans 

and motors (0.05%); and computers and data centers (0.05%). 
9 

By island (Figure 7), Oahu has the greatest potential for savings over 2007 electricity use at 9%, 

followed by Hawaii (1.9%), Maui (1.8%) , and Kauai (0.8%).

Figure 7. Electricity savings by island as a percent of 2007 Hawaii electricity use 

Results are also tabulated on a per-building basis for each model building type (Figure 8–Figure 

14). For the residential sector, the average high-rise can save 23% of total energy use, the 

average single family home can save 38%, and the average multifamily home can save 24%. In 

the commercial sector, large offices can save 12%, small offices can save 20%, retail buildings 

can save 26%, and hospitality buildings can save 18%. 

Figure 8. High-rise profile savings 
10 

Figure 9. Single family profile savings 

Figure 10. Multifamily profile savings 

Figure 11. Large office profile savings 
11 

Figure 12.Small office profile savings 

Figure 13. Retail profile savings 

Figure 14. Hospitality profile savings 

 
12

Combining building types and end-use technologies (See Table 1), single family water heating 

presents the greatest opportunity for efficiency improvements, with a potential savings of 250 

GWh. 

Table 1. Top 5 Individual Efficiency Measure Savings

Building Type and End Use GWh Savings Potential % of 2007 Electricity Use10

Single Family Water Heating 250 GWh 2.5%

Single Family Lighting 194 GWh 2%

Retail Lighting 85 GWh 1%

Office Cooling 72 GWh 1%

Single Family Refrigeration 69 GWh 1%

Other primary electricity efficiency drivers are single family lighting (194 GWh), retail lighting 

(85 GWh), office cooling (72 GWh), and single family refrigeration (69 GWh). A full list of 

aggregate efficiency savings by building type and end use is available in Appendix I.

Finally, once the potential electricity savings from these six building type is calculated, we 

adjusted to incorporate those buildings in the additional 38% of the building stock not accounted 

for in these six categories. Taking the average savings across all six building types (22%

electricity savings per building) and applying it to the energy usage for the remaining building 

types results in an additional potential savings of 800 GWh, bringing the potential savings for the 

entire existing building stock up to 2,100 GWh overall, or roughly 22% of Hawaii 2007 

electricity use.

Results Adjusted for Load Growth (2008–2030)

It is important to note that the EEPS is 30% of Hawaii electricity use in 2030, so to make a true 

longer-term projection, we must compare potential savings to the projected energy use in our end 

scenario, the year 2030, as opposed to the static 2007 electricity use context provided in the 

preceding section. The projected annual increase in electricity usage above 2007 levels in the 

year 2030 is 4,500 GWh,

11 including increased usage from both new construction and existing 

buildings (the energy intensity of an average building is forecast to increase over time with the 

adoption of more extensive air-conditioning units and more energy-intensive appliances).

 

10 Due to the uncertain nature of how load growth and efficiency by category type will fluctuate, projections of what 

each efficiency measure savings will be as a fraction of 2030 energy usage is outlined here. 

11 Projected growth is calculated by subtracting 14,333 GWh (HECO 2005) minus 9,859 GWh (BAH 2009b; HECO 

2005).
13

Since a detailed breakdown of these components of expected growth is not available, we have 

determined instead a likely range for potential electricity savings relative to projected 2030 

electricity use, based on “minimum growth in existing buildings electricity demand” (Lower 

Bound) and “maximum potential growth in existing buildings electricity demand” (Upper 

Bound) scenarios (Also illustrated in Figure 3): 

• Lower Bound: If there is no growth in existing building load, potential savings will not 

grow over time, capping the existing buildings portion of the final savings figure at 2,100 

GWh (15% of 2030 electricity use), or approximately 50% of the 4,300 GWh EEPS goal. 

• Upper Bound: If new construction grows to match historical population growth fully 

(0.7% per year [DBEDT 2008b]), equivalent to 30% of all new energy usage coming 

from new construction (based on utility 2007–2008 IRPs and BAH-estimated perbuilding energy usage [Appendix I]), but the entire remaining electricity growth forecast 

comes from existing buildings, potential existing building savings will equal 3,100 GWh, 

or 22% of 2030 Hawaii electricity use, maintaining existing buildings’ approximate 70%

share of the state EEPS goal. 

This 50%–70% range for the existing buildings’ contribution to the efficiency goal indicates that 

the targeted 70% contribution, estimated when the EEPS was enacted (Hawaii 2009), is a 

possibility (albeit a lower probability contingent on the balance of load growth between new 

construction and existing buildings moving forward). 

Despite the variability in expected savings from existing building load growth, these added 

savings are not expected to change the modeling results of the primary building types on which 

policy should be focused. Cooling savings will likely increase with increased cooling equipment 

saturation, but cooling is already at the top of the list for efficiency savings potential. Any 

assumption about appliance growth, particularly the increase in home entertainment equipment 

saturation, is difficult to accurately include in the model. This added growth is not likely to be 

significant enough to become a primary efficiency driver, as the average entertainment 

equipment electricity consumption reflects a small percentage of Hawaii electricity use. For 

example, a plasma television is the most energy-intensive home entertainment appliance, using 

441 kilowatt-hours (kWh) per year on average. This end use is only roughly 5% of the electricity 

use of an average single family building. 

Conclusions

Once we established the level of savings needed, a general cost analysis was conducted, and 

conclusions were drawn on a number of key points essential to the attainment of the state goals. 

To be implemented effectively, the following recommendations rely heavily on collaboration 

between the public sector (state agencies, the PBF administrator) and the private sector (utility 

companies, private businesses, building owners) across a wide range of issues, including the 

identification and testing of technologies, the raising and investment of capital, the education of 

the public, and the refinement of existing programs. 
14

•

Private investment in energy efficiency is critical to Hawaii’s meeting its efficiency 

target, and it is apparent that much will be necessary above and beyond what is already 

provided by the public sector via utility programs and contributions to the state’s PBF. 

Based on a levelized cost of energy efficiency of $83 per megawatt-hour (MWh) (Rogers, 

Messenger, and Bender 2005) and linear projections from 2010 to 2030, the total cost to 

meet the EEPS target (regardless of source) would be $4.1 billion, or $196 million per 

year. Assuming existing building efficiency savings will contribute 50-70% to the EEPS 

target (See “Results Adjusted for Load Growth” section for the calculation of this range), 

then the funding needed for existing buildings would be $98 million to $137million per 

year ($196 × 50%70%). Currently, KIUC annual program funding is $1 million (KIUC 

2010), and the Hawaii PBF funding for efficiency savings is roughly $20 million per year 

(HCEI 2009).

Additional investment, on the order of $50 million to $100 million per year, is necessary 

to meet the Hawaii EEPS targets.

12 Additionally, according to our analysis, the military accounts for 12% of 

Hawaii electricity use. Assuming the military matches the 30% efficiency goal with its 

own pool of funding separate from that of the state as a whole, we assume that the costs 

for those improvements can be subtracted from the total costs to achieve the EEPS (12%

× $196 million = $24 million). Thus, total additional investment (either private or public) 

needed to meet the existing building portion of the state’s efficiency goal would be in the 

$53 million to $92 million range per year ($98 million to $137 million minus $45 

million.) Given the ratio of existing building energy use between the residential and 

commercial sectors,

13 the residential sector will need an additional $24 million to $41 

million per year ($53 million to $92 million × 45%), and the commercial sector will need 

an additional $29 million to $51 million per year ($53 million to $92 million × 55%), 

with much of this being contributed by the private sector. Thus, finding ways for public 

money to leverage high levels of private capital becomes essential to the attainment of the 

EEPS goal.

 

12 $19.6 million per year is equivalent to roughly 0.6% of the total expected revenues for HECO, HELCO, and 

MECO. As revenue is expected to increase over time, PBF funds generated will increase to a predicted $60 million 

per year by 2014 (HCEI 2009).

13 As the cost for energy efficiency retrofits is estimated based on “percentage improvement reached” (see footnote 2 

in Executive Summary for overview), the $83/MWh cost assumed in this study reflects an average of commercial 

and residential retrofit projects. Therefore, we do not make a differentiation in cost between the two sectors here.
15

•

The model assumes a 1% rate of building turnover per year and a total building turnover 

rate of 20% of the existing building stock by 2030. To maximize the amount of electricity 

savings from retrofits, the least viable candidates for retrofit must be identified and 

targeted for replacement with more efficient new buildings, while retrofit efforts are 

targeted at the buildings that are capable of being cost-effectively retrofitted. This is due 

to the fact that most buildings in the lower 20% of the energy efficiency curve are too 

costly to retrofit, so if they are not replaced, they will continue to act as a drag on the 

state’s energy reduction efforts. Therefore, those buildings that can be retrofitted cost

effectively should be upgraded, while those that will never be cost effective to retrofit 

should be replaced entirely. This will generate the maximum efficiency savings from 

both existing buildings (more retrofits will happen), as well as from new construction 

(highly efficient new construction replaces the worst of the energy users). 

Given our assumption of cumulative 20% building turnover from 2010 to 2030, 

successfully identifying and retiring these buildings to maximize cost effectiveness would 

allow Hawaii to optimize efficiency gains.

•

Given the 20% overall building retirement assumption, an estimated 80% of Hawaii’s 

buildings must participate in efficiency efforts for the state to meet the EEPS target. It 

was assumed that 20% of building owners enroll voluntarily in retrofit programs, which 

is a large portion of the overall population to enroll in any single public program. This 

leaves 60% of the building stock currently unaccounted for. Given policy initiatives that 

correctly target building and technology areas, additional outreach and education must be 

designed to achieve the retrofitting of as much of this 60% of the building stock as 

possible. It is also quite likely that our hypothetical 20% assumption is too optimistic, 

which would make the importance of outreach and education programming even greater.

Full participation in retrofit and efficiency programs is essential to meeting the EEPS 

target.

•

An important caveat to our calculation of available savings is that some of the energy 

efficiency measures that are considered will not prove cost effective for all buildings 

types.

Advanced technologies, not yet deployable, must play a role in creating efficiency savings 

to offset shortfalls in savings from non-cost-effective current technology.

14

 

14 The purpose of this study is to identify technologies that will be required to meet the 30% EEPS goal. To chart 

these technologies, we make the initial assumption that not all of them will be cost effective. Deployment of 

technologies that are not yet commercially viable can help offset these costs.

For example, building envelope retrofits to insulation are an expensive energy 

efficiency measure, and unlikely to be adopted in many cases, even when applicable. 

Where possible, Hawaii should seek to increase per building efficiency savings through 

the use of next-generation technologies. One possibility is LED lighting. If all 
16

incandescent and CFL lighting is replaced with LED lighting, the modeled existing 

buildings could obtain an additional 134 GWh of savings (DOE 2010), or about 1.4% of 

2007 state electricity use. SWAC is another example of a technology option under 

development. HECO estimates that a proposed Waikiki SWAC site could offset 140,000 

MWh of cooling energy, equal to another 1.4% of 2007 electricity use (HECO 2010). As 

such, the development of pilot programs for new technologies to identify promising ones 

and to verify their performance becomes of key importance to the long-term attainment 

of any lofty efficiency goal such as the one in the EEPS.

•

Efficiency savings estimates are based on manufacturer data and may not represent realtime results because of improper installation, calibration, and maintenance. Proper 

building commissioning and O&M are essential to achieving the full savings potential of 

retrofits, as building operators may be unfamiliar with new technologies. The proper 

operation of building controls, particularly, should be a focus of this type of policy 

because this equipment can have a large impact on building energy use for minimal cost 

as long as it is installed and operated correctly. A recent Lawrence Berkeley National 

Laboratory metadata study estimates average electricity savings of approximately 9%

from the commissioning/retrocommissioning of a wide range of building types (Mills and 

Mathew 2009). Thus, the building commissioning will be a significant source of ongoing 

savings that is essential to the real-time reduction of electricity usage statewide. 

In order to increase the effectiveness of efficiency policy, retro/recommissioning and 

O&M training should be incorporated into technology policy.
17

References

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Home Energy Savings: Condensed Online Version; Water Heating.” Accessed April 2010. 

http://www.aceee.org/consumerguide/waterheating.htm.

[BAH] Booz Allen Hamilton. (2009a). “Commercial Efficiency Survey.” Booz Allen Hamilton, 

HECO companies and KIUC. October 2009.

BAH. (2009b). “Hawaii Building Stock Mapping and the Way Forward.” Booz Allen Hamilton. 

April 22, 2009.

[Census 2000a] U.S. Census Bureau. Census 2000 Summary File 3, Matrix H34. Accessed April 

2010. http://factfinder.census.gov/servlet/

DTSubjectShowTablesServlet?_lang=en&_ts=290530765812.

[Census 2000b] U.S. Census Bureau. Census 2000 Summary File 3, Matrices P3, P4, H3, H4. 

Accessed April 2010. http://factfinder.census.gov/servlet/

DTSubjectShowTablesServlet?_lang=en&_ts=290530765812.

[DBEDT] State of Hawaii Department of Business, Economic Development & Tourism.

(2008a). “Annual Visitor Research Report - 2008.” Accessed February 2010. 

http://hawaii.gov/dbedt/info/visitor-stats/visitor-research/.

DBEDT. ( 2008b). State of Hawaii Databook. Accessed May, 2010. 

http://hawaii.gov/dbedt/info/economic/databook/db2008/.

[DOE] U.S. Department of Energy. (2010). “DOE to Award Nearly $37 Million to 17 SolidState Lighting Projects.” U.S. Department of Energy Energy Efficiency & Renewable Energy.

EERE Network News. January 20, 2010.

[ESH] EnergySmart Hospitals Training Manual. (2008). “Energy Management Training Session 

Two: Energy Efficient Opportunities for Hospitals.” U.S. Department of Energy Energy 

Efficiency and Renewable Energy. March 4, 2008.

[FEMP] Federal Energy Management Program. (2010). “Purchasing Specifications for EnergyEfficient Products.” U.S. Department of Energy, Energy Efficiency & Renewable Energy, 

Federal Energy Management Program. Accessed February 2010. 

http://www1.eere.energy.gov/femp/technologies/eep_purchasingspecs.html.

[EPA] U.S. Environmental Protection Agency. 2004. “Reducing Urban Heat Islands: 

Compendium of Strategies; Cool Roofs.” Accessed February 2010. 

http://www.epa.gov/hiri/mitigation/coolroofs.htm.

Hawaii. (2009). “House Bill 1464 of the 2009 Legislative Session.” Hawaii House of 

Representatives. Accessed November 2009. 

http://www.capitol.hawaii.gov/session2009/Bills/HB1464_SD2_.HTM.
18

[HCEI] Hawaii Clean Energy Initiative. (2009). “Hawaii Clean Energy Initiative Working Group 

Policy Recommendations for the 2010 Hawaii State Legislative Session.” Accessed March 2010. 

http://www.hawaiicleanenergyinitiative.org/pdfs/policy_report_ly2010_part2.pdf, p.19.

[HECO] Hawaiian Electric Company. (1994). “1994 Commercial Energy-Use Survey.” Prepared 

for HECO by Regional Economic Research, Inc. August 1995.

HECO. (2004). “Integrated Resource Plan – 3, Appendix L: Demand-Side Management Report, 

Assessment of Energy Efficiency and Demand Response Potential.” Prepared for HECO by 

Global Energy Partners, LLC. Product ID # 1125-01. February 2004. Accessed May 2010 

http//www.heco.com/portal/site/heco/menuitem.508576f78baa14340b4c0610c510b1ca/?vgnexto

id=d1ee5e658e0fc010VgnVCM1000008119fea9RCRD&vgnextfmt=default&cpsextcurrchannel

=1.

HECO. (2008). “Integrated Resource Plan – 4.”Accessed February 2010. 

http://heco.com/portal/site/heco/menuitem.508576f78baa14340b4c0610c510b1ca/?vgnextoid=d1

ee5e658e0fc010VgnVCM1000008119fea9RCRD&vgnextfmt=default&cpsextcurrchannel=1.

HECO. (2009a). “2008 Appliance Age Distributions.” Personal Communication – George 

Willoughby. November, 24 2009.

HECO. (2009b). “UEC and Saturations Summary.” Personal Communication – George 

Willoughby. Based on 2006 CDA, 2004 RASS data and 2008 RASS. October 29, 2009.

HECO. (2010). “Seawater Air Conditioning.” Accessed February 2010. 

http://www.heco.com/portal/site/heco/menuitem.508576f78baa14340b4c0610c510b1ca/?vgnext

oid=a7eb202fe559b110VgnVCM1000005c011bacRCRD&vgnextchannel=deeaf2b154da9010V

gnVCM10000053011bacRCRD&vgnextfmt=default%26amp%3Bamp%3Bamp%3Bamp%3Ba

mp%3Bamp%3Bamp%3Bamp%3Bquot%3B&vgnextrefresh=1&level=0&ct=article.

[HELCO] Hawaii Electric Light Company. (2007). “Integrated Resource Plan – 3.” Accessed

February 2010. 

http://www.heco.com/portal/site/helco/menuitem.b136fe8120a5c28884276c10c510b1ca/?vgnext

oid=b41d9a796a4e2110VgnVCM1000005c011bacRCRD&vgnextfmt=default&cpsextcurrchann

el=1

[KEMA] KEMA, Inc. (2005). “Energy Efficiency Potential Study.” Prepared for Kauai Island 

Utility Cooperative. April 26, 2005.

KEMA. (2008). “Energy and Peak Demand Impact Evaluation Report of the 2005-2007 Demand 

Side Management Programs.” Prepared for Hawaiian Electric Company, Maui Electric 

Company, and Hawaii Electric Light Company. December 2008.

[KIUC] Kauai Island Utility Cooperative. (2010). “Personal Communication to Patrick Finch at 

Booz Allen Hamilton.” February 2010.

KIUC. (2008).“Integrated Resource Plan – 3.” Accessed February 2010. 

http://www.kiuc.coop/IRP/Tariff/IRP2008_2008%20KIUC%20IRP.PDF.
19

[MECO] Maui Electric Company. (2007). “Integrated Resource Plan – 3.” Accessed February 

2010. 

http://heco.com/portal/site/meco/menuitem.ed4aed221358a44973b5c410c510b1ca/?vgnextoid=e

b282e1f9d380110VgnVCM1000005c011bacRCRD&vgnextfmt=default&cpsextcurrchannel=1.

Mills, E.; Mathew, P. (2009). “Monitoring-Based Commissioning: Benchmarking Analysis of 24 

UC/CSU/IOU Projects.” Lawrence Berkeley National Laboratory. June 2009. Accessed April 

2010. http://cx.lbl.gov/MBCx.html.

[RMI] Rocky Mountain Institute. (2008). “Systems Thinking for Radically Efficient and 

Profitable Data Centers.” Newman, S., Palmintier, B. http://www.rmi.org/rmi/Library/E08-

06_SystemsThinkingDataCenters.

Rogers, C., Messenger, M., Bender, S. (2005). “Funding and Savings for Energy Efficiency 

Programs for Program Years 2000 Through 2004.” California Energy Commission. July 2005. 

Accessed February 2010. http://www.fypower.org/pdf/CEC%20_Trends2000-04.pdf.

Wigg, Howard. DBEDT Institutional Energy Analyst. (2009). E-mail Correspondence with 

Patrick Finch. December 30, 2009.
20

Appendix I: Study Assumptions and Calculations

The full list of aggregated potential energy savings by sector and end use is included in Table 2

(below).

Table 2. Total Aggregate Savings by Building Type and Technology (State-wide)

Potential Electricity Savings (GWh/year)

Offices

Refrigeration Did Not Estimate

Cooling 72

Lighting 6

Water Heating 3

Controls 10

Fans and Motors 1

Building Envelope 4

Appliances Did Not Estimate

Computers and Data Centers 1

Retail

Refrigeration Did Not Estimate

Cooling 64

Lighting 85

Water Heating 5

Controls 22

Fans and Motors 2

Building Envelope 63

Appliances Did Not Estimate

Computers and Data Centers 4

Hospitality

Refrigeration Did Not Estimate

Cooling 43

Lighting 17

Water Heating 3

Controls 41

Fans and Motors 3

Building Envelope 3

Appliances Did Not Estimate

Computers and Data Centers 1

High-Rises

Refrigeration 22

Cooling 31

Lighting 8

Water Heating 6

Controls 31

Fans and Motors 1

Building Envelope 13
21

Appliances 33

Computers and Data Centers 1

Single Family

Refrigeration 70

Cooling 46

Lighting 194

Water Heating 250

Controls Did Not Estimate

Fans and Motors Did Not Estimate

Building Envelope 18

Appliances 66

Computers and Data Centers Did Not Estimate

Multifamily 

Refrigeration 15

Cooling 9

Lighting 43

Water Heating 10

Controls Did Not Estimate

Fans and Motors Did Not Estimate

Building Envelope 0

Appliances 25

Computers and Data Centers Did not estimate

These figures represent the difference in energy usage from the efficient case to the baseline case 

for each building type, aggregated across the full number of buildings in each category for the 

state. 

While the total number of housing units in the state is known, due to a lack of detailed 

information on the number of commercial buildings, the total number of commercial buildings 

assumed for each category is back-calculated from their total electricity usage. Thus, our model 

profiles may represent in certain commercial building cases an average building that is the 

equivalent of multiple smaller buildings, all with the same baseline characteristics and efficiency 

options. While this represents an accurate picture of statewide potential savings, as we account 

for the full electricity usage in each sector, it may mean that for certain building types we are 

assuming a smaller number of buildings than exist in the current building stock. This 

correspondingly reduces the number of retrofits needed but is compensated for by an increase in 

the size of each individual retrofit in absolute terms (although not in percentages). An example: 

three small buildings retrofitted at a 20% savings level, if added together, form the equivalent 

one larger building retrofitted at a 20% savings level, provided the same combination of energy 

conservation measures is applied to each. Therefore, the accuracy of the total energy savings is 

not compromised, but it would not be correct to assume energy savings per building applies to 

any one given building in the state of Hawaii on the commercial side. 
22

Commercial Sector Modeling

In many of our data sources, high-rise (multifamily, 20 units or greater) building profiles are 

grouped into the commercial sector. Therefore, a majority of the high-rise data points used in this 

study are estimated using the methodology for the commercial sector (i.e., averaging data 

collected in the 2009 building survey [BAH 2009a] with the 1994 HECO study [HECO 1994]). 

However, as high-rise buildings share more key components with residential buildings than with 

their commercial counterparts in terms of appliance saturation and mix, we have classified them 

as residential overall, and aggregated them using residential data in the post-profile modeling 

stage of this analysis. 

On the office building side of things, one of our major data points, the 2004 HECO DSM study,

contains data for large and small offices. With this to build upon, we have developed subbuilding 

profiles for large and small offices within the “Office” category, but to maintain continuity with 

our building stock map (Figure 4. 2007 electricity use in the state of Hawaii [MWh]), we reaggregate these values in the final projections analysis. We do this because the building map 

results do not distinguish between large and small offices, therefore making it impossible to 

derive the number of large and small offices while maintaining consistency in the methodology 

for scaling up across building types.

Commercial Cooling

Baseline Building

We estimate baseline cooling load for commercial buildings, by building type, from three 

variables: average efficiency (kW/ton), average size (tons) and average cooling operating hours 

(Table 3).

• Average cooling operating hours, by building type, are equal to the average values from 

the 1994 study (HECO 1994) and the 2009 survey (BAH 2009a). For hospitality, average 

cooling hours are reduced to 70.4% of their total to reflect the average occupancy rate in 

2008 (DBEDT 2008), thus adjusting for reduced usage in unoccupied rooms.

• Average efficiencies, by building type, are equal to the average of values from the HECO 

2004 study baseline building profiles (HECO 2004) and the 2009 survey (BAH 2009a). 

Where values are reported as energy efficiency ratio (EER), they are converted to kW/ton 

by dividing them by 12.

• Average system size is calculated by dividing average building size by average square 

footage per ton of cooling.

• Average building sizes are equal to the 2004 study’s (HECO 2004) assumptions for 

average building size.

• Average square footage per ton of cooling is derived from averaging 1994 (HECO 1994) 

values with 2009 survey results (BAH 2009a). 1994 values are back-calculated from total 

building type square footage, total building type cooling electricity sales, and average 

operating hours per year.

Since the 2004 study (HECO 2004) does not include model results for high-rises, we make a 

number of assumptions for high-rises where the calculations require 2004 values. 
23

• Building size: Assuming the maximum number of floors is 47 and the minimum number 

is 2, the average floors per building is 24.5. Average area per floor is derived from the 

2009 survey results and is equal to 18,727 square feet (SF) per floor (BAH 2009a). This 

value is scaled up to equate to 458,823 SF per building. 

• Average operating hours per year: This value is assumed to be equivalent to the 

hospitality building type. 

• Average efficiency: This value is assumed to equal the average of the 2004 value for 

multifamily units (HECO 2004) and the 2009 survey result for hospitality (BAH 2009a).

Table 3. Baseline Commercial Cooling Assumptions

Large Office Small Office Retail Hospitality High-Rises

Average Building Area 

(SF) 330,00 10,000 50,000 404,700 458,823

Average Cooling 

Operating Hours Per Year 3,159 3,159 4,088 6,150 8,736

Average Efficiency 

(kW/Ton) 0.75 1.34 1.3 1.33 1.33

Average Cooling Size 

(Tons) 921.8 19.7 76.5 300.6 189.2

Average Cooling 

Consumption (kWh/year) 2,169,352 83,723 406,532 2,464,331 2,202,990

Efficient Building

The efficient building case predicts average cooling load when average cooling efficiency is 

improved, given assumptions from the baseline building profiles for average operating hours, 

average cooling system size, and average building area. By building type, efficiency values for 

kW/ton are derived from an average of FEMP values (FEMP 2010). The baseline building 

efficiency values represent average efficiencies from several different system types; thus the 

efficient building cooling efficiency values are represented by an average of different system 

types: commercial unitary air-conditioners, air-cooled chillers, packaged units and room AC 

units. For the large office building type, we assume the efficient system is a water-cooled chiller, 

which is an upgrade over the mix of centrifugal and less efficient water-cooled chillers prevalent 

in the base case (Table 4).

Table 4. Efficient Commercial Cooling Assumptions

Large Office Small Office Retail Hospitality High-Rises

Average Efficiency 

(kW/Ton) 0.52 1.08 1.08 1.08 1.08

Commercial Lighting

Baseline Building

Average lighting load per building, by building type, is equal to the average of existing and new 

building lighting load profiles from the 2004 HECO building modeling results (HECO 2004). 
24

Number of lamps per building, by lamp type, is derived by averaging 1994 lamp numbers 

(HECO 1994) and 2009 survey results (BAH 2009a). The 1994 lamp numbers are not reported 

on a per-building basis. Thus, we calculate 1994 lamps per building by dividing by the total 

number of lamps estimated in the study by the estimated number of buildings in 1994. Number 

of buildings in 1994 is back-calculated from the 1994 values for total building area (by building 

type) and the 2004 study values for average building size (by building type) (HECO 2004). This 

calculation results in 1994 lamps per building by lamp type and by building type. Next, by 

building type, 1994 lamp numbers per building by lamp type must be averaged with 2009 lamp 

numbers per building by lamp type. Since the 1994 and 2009 lamp types are reported in different 

subcategories, we roll these subcategories into larger categories to take the average (Table 5).

Table 5. Baseline Commercial Lighting Assumptions

Large Office Small Office Retail Hospitality High-Rises

Average Lighting 

Consumption 

(kWh/Year)

1,664,365 55,285 415,617 1,343,157 1,522,787

Lamps Per Building

(Average Wattage)

T12 Fluorescent

(82 W) 645 15 479 869 455

T8 Fluorescent

(57 W) 511 12 37 99 97

Incandescent (60 W) 33 12 235 2,959 1,859

CFL (17 W) 50 18 6 3,189 751

Efficient Building

To estimate the efficient building lighting scenario, we calculate the expected energy savings 

from retrofitting all T12 lamps with T8 lamps and all incandescent lamps with CFLs. Energy use 

per lamp type is calculated for each lamp type, based on average light power (watts) and building 

type operating hours per year (FEMP 2010). Then, for each building type, the differences in 

energy use for each replacement (T8s, CFLs, and LED exit signs) are multiplied by the number 

of retrofits (number of T12s, incandescent lamps, and incandescent exit signs).

Commercial Water Heating

Baseline Building

The methodology for water heating is similar to the methodology for lighting. Baseline water 

heating electricity use is the average of 2004 water heating electricity values for new and 

existing buildings in the 2004 HECO energy model (HECO 2004). Number of water heaters by 

building type and by water heater type are derived from an average of 2009 survey responses 

(BAH 2009a) and 1994 water heater numbers (HECO 1994). Similar to lighting, some models of 

water heaters are not classified in the same way across studies, so they must be combined. For 

small offices, there are no 1994 or 2009 values for number of water heaters. These values are 

derived from the number of water heaters in large offices, adjusted by the ratio of average large 

office size to average small office size (Table 6).
25

Once the average number of water heaters is calculated (per building, by building type) we 

derive a weighted average energy factor, by building type, as a measure of baseline water heating 

efficiency. The weighted average is based on figures from the American Council for an EnergyEfficient Economy’s (ACEEE’s) “Consumer Guide to Home Energy Savings: Condensed Online 

Version; Water Heating,” (ACEEE 2010), in combination with DOE’s EnergySmart Hospitals 

Training Manual (ESH 2008), minimum efficiency water heating energy factors, and the number 

of water heaters per building, by building type. For the purpose of comparing with the efficient 

water heating case, we multiply the water heating energy loads by the energy factors to obtain 

measures of the heat energy in the water, net of efficiency losses (Table 7).

Table 6. Baseline Commercial Water Heating Assumptions

Large Office Small Office Retail Hospitality High-Rises

Average Water Heating 

Electricity 

Consumption

(kWh/Year)

84,435 2,559 17,119 714,480 714,480

Water Heaters Per 

Building

Solar Water Heater 0 0 0 0 0

High-Efficiency 

Electric or Tankless 10.9 0.3 0.2 0.4 0

Electric Individual 

Tank Heaters

6.0 0.2 3.4 2.8 60.2

Gas Boilers 0 0 0.83 2.9 1.2

Heat Pumps 0.1 0.002 0.2 1.34 3.1

Fuel Oil Heaters 0 0 0 0.3 0

Average Electric Water 

Heater Energy Factor 0.87 0.87 0.82 0.96 0.86

Average Water Heating 

Electricity 

Consumption Adjusted 

for Losses (kWh/Year)

73,091 2,215 14,116 684,160 611,834
26

Table 7. Commercial Water Heater Efficiency Values

Water Heater Type Average Energy Factor

Tankless/Electric High-Efficiency 0.9

Electric Tank 0.79

Gas Storage 0.6

Heat Pump 2.2

Fuel Oil 0.55

Solar Thermal 1.2

Efficient Building

To calculate the efficient water heater energy use per building scenario by building type, we 

derive energy factors if all existing water heaters are replaced with tankless or high-efficiency 

water heaters for hospitality and high-rises and with solar water heaters for offices and retail. We 

assume that solar hot water heaters are not feasible for hospitality and high-rise buildings 

because the ratio of roof space to building area is too small to support this technology. The 

energy factor for the efficient building is retabulated with these water heater replacements using 

the same methodology as for the baseline case. Last, we divide the average water heating 

electricity load adjusted for losses by the efficient building energy factor to estimate the average

efficient building water heating electricity load (Table 8).

Table 8. Efficient Commercial Building Water Heating Electricity Load

Large Office Small Office Retail Hospitality High-Rises

Average Water 

Heating Electricity 

Consumption

(kWh/Year)

60,727 1,840 11,440 685,921 639,062

Commercial Controls

Baseline Building

Data for the percentage of buildings in Hawaii with EMS and programmable thermostats by 

building type are available from both the 1994 survey (HECO 1994) and the 2009 survey (BAH 

2009a). We average these values to approximate the average percentage of buildings with these 

systems in the baseline scenario. Data were not available separately for small offices, so we 

assume that the saturation of controls in this building type is approximately the same as that of 

the large office building type (Table 9).
27

Table 9. Saturation of Building Controls, Baseline Case

Large Office Small Office Retail Hospitality High-Rises

Buildings with EMS 49.7% 49.7% 16.4% 52.2% 57.9%

Programmable 

Thermostats 57.5% 57.5% 32.5% 24.3% 17.6%

Adjusted Savings as 

a Percent of Total 

Building Electricity 

Use

3.9% 4.3% 2.3% 6.7% 6.2%

Efficient Building

For the efficient building scenarios, we assume that all buildings will have an EMS and 

programmable thermostats. Gross electricity savings from installing this equipment are derived 

from savings per square foot values (Table 10) given in the 2004 study building modeling results 

(HECO 2004). For each building type, the savings values are multiplied by 1 minus the baseline 

equipment saturations and average square footage per building. Since we are not installing the 

EMS and programmable thermostats in isolation of other measures, we must reduce the amount 

of savings from this equipment to avoid double counting savings from lighting and cooling. To 

avoid double counting savings for each building type, control savings as a percentage of total 

building energy use (HECO 2004) are reduced by the sum of cooling savings as a percentage of 

cooling electricity use and lighting savings as a percentage of lighting electricity use (see 

adjusted values in Table 10).

Table 10. Control Savings

Gross Electricity Savings Per SF (kWh/SF)15

EMS 1.44

Programmable Thermostat 0.68

Commercial Fans and Motors

Baseline Building

In this section, we calculate the number of standard and efficient fans and motors in each 

baseline building. These numbers are averages of values from the 1994 HECO survey (HECO 

1994) and the 2009 survey (BAH 2009a). While results for number of fans and motors per 

building, by fan and motor type, are available from the 2009 survey, the 1994 survey reports the 

number of standard-efficiency fans and motors per building and the percentage of buildings with 

variable-speed fans and efficient motors. To calculate the number of efficient fans and motors 

per building in 1994, the number of 1994 fans and motors is multiplied by the percentage of 

buildings with variable-speed fans and efficient motors. Due to missing 1994 fan and motor 

values for offices, the number of fans and motors in offices is based entirely on 2009 survey 

 

15 A range of control savings values is available from the 2004 HECO study depending on building type and on 

whether the building is new construction or existing. We choose conservative values to avoid overestimating 

savings.
28

results. Small office fans and motors are scaled down based on the ratio of small office to large 

office per building areas (Table 11).

Table 11. Baseline Fan and Motor Assumptions (Number of Fans and Motors Per building)

Large Office Small Office Retail Hospitality High-Rises

Standard Fans 15 0.5 2.3 41.7 13.5

Variable-Speed 

Drive Fans 29 0.9 0 6 0

Standard-Efficiency 

Motor 15 0.5 5.1 6.7 1.2

Premium-Efficiency 

Motor 53.5 1.6 1 9.8 10.5

Efficient Building

We assume that efficient buildings will replace all standard-efficiency fans and motors with 

variable-speed fans and premium-efficiency motors. Electricity savings from this retrofit are 

calculated based on a value of electricity savings per fan from the 2004 HECO study (HECO 

2004) and on a value of electricity savings per premium-efficiency motor from a 2008 KEMA 

study (KEMA 2008) (Table 12).

Table 12. Efficient Fan and Motor Assumption

Electricity Savings Per Unit (kWh/Unit)

Variable-Speed Fan 769.8

Premium-Efficiency Motor 54.8

Commercial Building Envelope

Baseline Building

There are four components to the building envelope efficiency measures in the model: 

percentage of buildings with roof insulation (R-19), percentage of buildings with wall insulation 

(R-13), percentage of buildings with high-reflectivity roofs, and percentage of buildings with 

efficient windows.16

For high-reflectivity roofs and high-efficiency window saturations, we assume that no buildings 

on the low end of the efficiency curve will have high-reflectivity roofs or high-efficiency 

The percentages of buildings with roof insulation, by building type, are 

averages of 1994 survey results (HECO 1994) and 100% (we assume that all buildings on the 

upper end of the building efficiency curve will have roof insulation). Since we do not have data 

on wall insulation saturation, we assume that the percentage of buildings with roof insulation is 

approximately the same as the percentage of buildings with wall insulation.

 

16 Hawaii building codes specify at least R-19 building insulation, and we assume virtually no buildings have R-25 

insulation (Wigg 2009). 
29

windows and that the upper end of the efficiency curve is represented by responses to the 2009 

survey (BAH 2009a) (Table 13). 

Table 13. Saturation of Insulation Types for Building Envelope, Baseline Case

Large Office Small Office Retail Hospitality High-Rises

Percentage of 

Buildings with Roof 

Insulation

62.1% 62.1% 61% 60.6% 66.5%

Percentage of 

Buildings with Wall 

Insulation

62.1% 62.1% 61% 60.6% 66.5%

Percentage of 

Buildings with HighReflectivity Roofs30% 30% 40% 0% 50%

Percentage of 

Buildings with HighEfficiency Windows0% 0% 0% 0% 50%

Efficient Building

Building envelope electricity savings are based on retrofitting the buildings with no ceiling 

insulation to R-19 ceiling insulation (we assume no buildings will upgrade to higher than R-19 

insulation, as R-19 is the current Hawaii building code level), R-13 to R-19 wall insulation, highreflectivity roofs, and high-efficiency windows (Table 14). We assume buildings with R-13 wall 

insulation will upgrade to R-19 wall insulation, and buildings without wall insulation will not 

install wall insulation (we assume that most of the buildings without wall insulation are not 

cooled, so no electricity savings would result from increasing insulation).

• Ceiling insulation savings

•

—These values are based on kWh savings per SF of roof area 

for small offices retrofitting from no insulation to R-19 (HECO 2004). These savings are 

multiplied by the percentage of buildings without insulation by building type (HECO 

2004) and by average floor space per story (assuming this is equivalent to roof area). 

Wall insulation savings—The electricity savings due to upgrading from R-13 to R-19 

insulation are based on kWh savings per SF of exterior wall area for small offices (HECO 

2004).17 This value is multiplied by estimated exterior wall area for each building type 

and by the percentage of buildings with R-13 wall insulation. 

 

17 We assume the average wall is 9' in height for the calculation of exterior wall area per building.
30

• High-reflectivity savings

•

—High-reflectivity roofs save 18.6% of a building’s cooling 

energy on average (EPA 2004). We apply this percentage to the baseline percentage of 

buildings without high-reflectivity roofs. To adjust for the effect of a building’s ratio of 

roof to building area, we multiply savings by the ratio of roof to total building area. 

Percentage savings from roof upgrades will be less for taller buildings, with the roof as 

less of a percentage of the building envelope.

High-efficiency windows savings—By building type, we multiply savings per square foot 

of window (from upgrading to double-pane windows (HECO 2004)18

• All of the building envelope electricity savings are summed, and then we subtract cooling 

savings as a percentage of total building energy use to prevent double counting as we 

upgrade both building systems in the efficient building profile.

) by the average 

window square footage per building. We use a window-to-wall ratio from the 2004 study 

to derive window square footage based on our previous calculation of exterior wall area. 

We also assume that the average high-rise window-to-wall ratio is similar to that of an 

average hospitality building, since the window-to-wall ratio is not available in the 2004 

study.

Table 14. Efficient Commercial Building Envelope Assumptions

Electricity Savings Assumption

Installing Ceiling Insulation (No Insulation to R-19) 2.24 kWh Per SF of Roof

Installing Wall Insulation (R-13 to R-19) 0.038 kWh Per SF of Wall

Installing a High-Reflectivity Roof 18.6% Cooling Energy Savings

Installing High-Efficiency Windows 4 kWh Per SF of Window

Commercial Computers and Data Centers

Baseline Building

For computers and data centers, we estimate the number of standard efficiency computers, 

ENERGY STAR computers, standard data centers, and efficient data centers. We average values 

from the 1994 HECO study (HECO 1994) and a 2009 commercial sector survey (BAH 2009a) 

for all of these estimates. We assume that the number of ENERGY STAR computers at the low 

end of the efficiency curve is zero. All data centers reported in the studies are also assumed to be

standard efficiency 1-U servers (Table 15).19

 

18 We understand that additional U-value improvements could be made through the adoption of window film as 

opposed to double-paned glass in this case. However, given our data at hand, and the fact that main improvement in 

this area would be in reduced cost, rather than reduced savings and that cost is to be examined more closely at a 

programmatic level, we have opted to use double-paned glass as a proxy for window improvement for the purposes 

of this study.

19 The 1994 study reports number of “mainframes” and we assume this is roughly equivalent to today’s data center 

for the purposes of this study.
31

Table 15. Baseline Commercial Computer and Data Center Assumptions

Large Office Small Office Retail Hospitality High-Rises

Standard Computers 

Per Building 37 1.1 15.2 54 15.2

ENERGY STAR 

Computers Per 

Building

1.5 0.1 0.2 100 3

Data Centers Per 

Building 1.1 0.1 0.3 0.5 0.5

Efficient Building

Savings for upgrading to ENERGY STAR computers and monitors are based on savings 

estimates in the 2004 HECO modeling results (HECO 2004). Savings from data centers are 

based on an estimate by Rocky Mountain Institute (RMI 2008) (Table 16).

Table 16. Efficient Commercial Computer and Data Center Assumptions

Electricity Savings Per Unit (kWh/Unit)

ENERGY STAR Computer 84

ENERGY STAR Monitor 197

Efficient Data Center 534

Residential Sector Modeling

For most single family and multifamily building technology types in the model, baseline energy 

use and saturations are based on the 2008 HECO Residential Appliance Survey (HECO 2009b).

Appliance saturations are listed by utility (HECO, MECO, or HELCO), so we combine these 

values by weighting them according to the percentage of the utility’s contribution to total state 

electricity use. Energy use per appliance/system type is multiplied by its saturation to derive 

average energy use by end use and building type. Multifamily cooling and water heating 

appliance energy uses are reduced, relative to the values for single family buildings, by the 

percentage difference between the 2004 study’s modeling results for each respective end use 

(HECO 2004). Below, we describe these assumptions in more detail and note adjustments and 

exceptions.

Since multifamily energy use is calculated on a per-housing-unit level, we multiply this value by 

the average housing units per building to derive the average energy use per building. To estimate 

average housing units per building, we calculate the weighted average units per building from 

the distribution of energy use per housing type (BAH 2009b). In the distribution, energy use is 

broken down by housing type, and these housing types are categorized by number of units per 

building (2, 3, or 4; 5 to 9; and 10 to 19).
32

Residential Refrigeration

Baseline Building

The baseline building refrigeration assumptions are estimated by multiplying appliance 

saturations with unit energy use, as described above. Table 17, below, outlines the base 

assumptions used in calculating the baseline residential refrigeration use. 

Table 17. Baseline Residential Refrigeration Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

First Refrigerator Saturation 100% 100%

First Refrigerator Average 

Energy Use (kWh/Year) 661 661

Second Refrigerator 

Saturation 50% 13%

Second Refrigerator Average 

Energy Use (kWh/Year) 1,979 1,979

Freezer Saturation 31% 14%

Freezer Average Energy Use 

(kWh) 563 563

Efficient Building

For both single family and multifamily efficient building profiles, energy savings per refrigerator 

and freezer are subtracted from the standard energy use values. These energy savings per 

efficient refrigerator values are estimated using modeling results from the 2004 HECO study for 

upgrading from a minimum NAECA efficiency refrigerator to an ENERGY STAR refrigerator 

(HECO 2004). Energy savings per efficient freezer is derived from FEMP efficient freezer 

values (FEMP 2010) (Table 18).

Table 18. Efficient Residential Building Refrigeration Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

First Refrigerator Saturation 100% 100%

First Refrigerator Average 

Energy Use (kWh/Year) 558 558

Second Refrigerator 

Saturation 50% 13%

Second Refrigerator Average 

Energy Use (kWh/Year) 1,666 1,666

Freezer Saturation 31% 14%

Freezer Average Energy Use 

(kWh/Year) 350 350
33

Residential Cooling

Baseline Building

Appliance saturations and energy use values are estimated as described above. The data only list 

energy use values for central air-conditioning (AC), so we assume that packaged central AC and 

split central AC systems use a similar amount of electricity per year (Table 19). The efficiency 

values for each system type are not used in calculating energy use, as energy use per efficient 

unit is given. The efficiency value for room AC is an estimate used in the 2004 HECO study 

(HECO 2004). For central AC units, we derive efficiency from a FEMP example central AC unit 

(FEMP 2010). We scale the efficiency of our model central AC unit according to the energy use 

and efficiency of this example central AC unit.

Table 19. Baseline Residential Building Cooling Assumptions

Single Family Multifamily (<20 Units per 

Building, Per Unit Assumptions)

Room AC Saturation 29% 35%

Room AC Average Efficiency 

(EER) 8.6 8.6

Room AC Average Energy 

Use (kWh/Year) 1,397 652

Packaged Central AC 

Saturation 11% 9%

Packaged Central AC Average 

Efficiency (Seasonal Energy 

Efficiency Rating [SEER])

13 13

Packaged Central AC Average 

Energy Use (kWh/Year) 3,750 2,394

Split AC Saturation 19% 6%

Split AC Average Efficiency 

(SEER) 13 13

Split AC Average Energy Use 

(kWh/Year) 3,750 2,394

Efficient Building

Energy efficiency estimates for the efficient building profile cooling systems are based on 

minimum FEMP purchasing requirements (FEMP 2010). We adjust these efficiencies to 

correspond to energy saving values from the 2004 HECO modeling results (HECO 2004). For 

example, the minimum FEMP purchasing requirement for residential room AC units is 10.7 

EER, but we only have energy savings values for improving efficiency from 8.6 to 10.2. 

Therefore, we set the efficient building profile cooling efficiency at 10.2 (Table 20).
34

Table 20. Efficient Residential Building Cooling Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Room AC Saturation 29% 35%

Room AC Average Efficiency 

(EER) 10.2 10.2

Room AC Average Energy 

Use (kWh/Year) 1,001 443

Packaged Central AC 

Saturation 11% 9%

Packaged Central AC Average 

Efficiency (SEER) 18 18

Packaged Central AC Average 

Energy Use (kWh/Year) 3,361 2,247

Split AC Saturation 19% 6%

Split AC Average Efficiency 

(SEER) 18 18

Split AC Average Energy Use 

(kWh/Year) 3,361 2,247

Residential Lighting

Baseline Building

Baseline residential lighting energy use is calculated using a sample distribution of the number of 

lights per building by lamp type (HECO 2009a). Average lamp number estimates are weighted 

averages from the distribution. Lighting electricity use per building is calculated by multiplying 

average lamp numbers by their average power and an estimate of average residential lighting 

operating hours (1,200 per year) (FEMP 2010) (Table 21).

Table 21. Baseline Residential Lighting Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Average Number of Lamps Per 

Building (Average Wattage)

Incandescent (40 W) 16.4 10.8

CFL (17 W) 9.0 5.2

T12 Tube Fluorescent (47 W) 5.9 2.9

Spot Light (100 W) 2.3 1.0

Outdoor Light (100 W) 3.4 1.3

Average Operating Hours 1,200 1,200

Efficient Building

For the efficient building profiles, all incandescent lights are replaced with CFLs, all T12 tube 

fluorescent lights are replaced with T8 fluorescent lights, and both spot and outdoor lights are 
35 

replaced with CFLs of the appropriate wattage. Average total lighting energy use is estimated 

using the same methodology as for the baseline profile (Table 22).

Table 22. Efficient Residential Lighting Assumptions (Average Number of Lamps Per Building)

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Incandescent (40 W) 0 0

CFL (17 W) 25.4 15.9

T8 Tube Fluorescent (45.5 W) 5.9 2.9

Efficient Spot Light (27 W) 2.3 1.0

Efficient Outdoor Light (27 W) 3.4 1.3

Residential Water Heating

Baseline Building

To estimate average per-building water heating energy use, we use the 2008 survey’s electric 

water heater saturation and energy use (HECO 2009b). The 2008 survey does not specify the 

type of electric water heater corresponding to the saturation or the efficient water heaters in the 

baseline. We assume that the electric water heater in the 2008 study is a standard efficiency 

electric storage water heater (Table 23). 

Table 23. Baseline Residential Water Heating Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Standard Electric Storage WH 

Saturation 57% 61%

Standard20

2,719 

Electric Storage 

WH Average Energy Use 

(kWh/Year)

1,941 

Solar WH Saturation 28% 0%

Solar WH Average Energy 

Use (kWh/Year) 644 460 

High-Efficiency18

0% Electric 

Resistance WH Saturation 10%

High-Efficiency Electric 

Resistance WH Energy Use 

(kWh/Year)

2,462 1,758 

Efficient Building

For the efficient case water heaters, we assume efficient water heater types based on those 

offered by the HECO Residential Water Heating Program and Residential New Construction 

Program (KEMA 2008). In the model, single family buildings with water heating upgrade to 

 

20 Our calculations do not use water heater efficiency values to calculate energy savings, only energy use. We 

compare the annual energy use of an average Hawaii water heater to the energy use of solar water heaters in the 

efficient case to reduce the need to forecast future water usage patterns per person.
36

solar water heaters, and multifamily buildings with water heating upgrade to high-efficiency 

electric water heaters (Table 24).21

Table 24. Efficient Residential Water Heating Assumptions

We assume no multifamily buildings will use solar water 

heaters due to feasibility issues for buildings with multiple stories, multiple units, and limited 

roof space. Energy use for the efficient technologies is calculated based on the average per unit 

impact of the technologies (KEMA 2008).

Single Family Multi Family (<20 Units Per 

Building, Per Unit Assumptions)

Standard Electric Storage WH 

Saturation 0% 0%

Solar WH Saturation 84% 0%

High-Efficiency Electric 

Resistance WH Saturation 0% 71%

Residential Building Envelope

Baseline Building

The percentage of single family and multifamily buildings with wall insulation and ceiling 

insulation are derived from data collected by HECO (HECO 2009b). We assume that the 

baseline wall insulation is R-13 and the baseline and ceiling insulation is R-19 (Table 25). These 

levels of insulation are the current Hawaii building code (Wigg 2009).

Table 25. Baseline Residential Building Envelope Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Percentage of Buildings with 

R-13 Wall Insulation 20.4% 14.1%

Percentage of Buildings with 

R-19 Ceiling Insulation 21.1% 13.1%

Efficient Building

In the model, we calculate electricity savings from buildings with the baseline level of wall 

insulation that will upgrade to R-19 insulation and from buildings without the baseline R-19 

ceiling insulation that will upgrade to this baseline level. We do not calculate savings from 

upgrading wall insulation to multifamily homes because this efficiency measure is likely too 

costly for existing multifamily buildings (Table 26).

• For ceiling insulation upgrades, we calculate electricity savings from only those buildings 

without insulation and with cooling. There will be no electricity savings for buildings 

 

21 The study does not derive the efficiency of “high-efficiency electric water heaters.” Average per-unit impact, as 

defined in the KEMA 2008 DSM report, is used to derive the average energy use of this technology in the efficient 

case.
37 

without cooling that install insulation. To calculate this percentage, we subtract the 

percentage of buildings with ceiling insulation from the total percentage of buildings with 

cooling.22

• To estimate the electricity savings from the percentage of buildings that will upgrade 

from R-13 to R-19 wall insulation, we multiply the percentage of buildings with 

insulation by average exterior wall area per building and by electricity savings per square 

foot of exterior wall area.

This percentage is multiplied by an estimate of roof area and an estimate for 

electricity savings per square foot of R-19 insulation installed. 

• All of the building envelope electricity savings are summed and then we subtract cooling 

savings as a percentage of total building energy use to prevent double counting as we 

upgrade both building systems in the efficient building profile.

Table 26. Efficient Residential Building Envelope Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Average Exterior Wall Area 

Per Building (SF) 1,704 6,814 

Average Roof Area Per 

Building (SF) 995 1,184 

Electricity Savings Per Square 

Foot of Installed R-19 Wall 

Insulation (kWh/Year)

0.012 0 

Electricity Savings Per Square 

Foot of Installed R-19 Ceiling 

Insulation (kWh/Year)

0.44 1.1 

Residential Appliances

Baseline Building

To calculate baseline energy use and saturation of dishwashers, clothes washers, clothes dryers 

and ranges/ovens, we use values from the 2008 saturation study (HECO 2009b) with some 

adjustments. First, the 2008 saturation study value for dishwasher energy use is higher than the 

2004 HECO study value. We assume that the higher dishwasher values include the energy 

needed to heat water. Since we are counting this electricity in the water heater section, we use 

the lower 2004 HECO study value as the amount of electricity used by the dishwasher. Second, 

the energy use value for clothes washers is omitted from the 2008 data. Again, we use a 2004 

HECO study value for the energy used by the average clothes washer motor (Table 27).

 

22 Total % buildings with insulation (TI) = % buildings with cooling, with insulation (CI) + % buildings without 

cooling with insulation (NCI); Total % buildings with cooling (TC) = % buildings with cooling without insulation 

(CNI) + % buildings with cooling with insulation (CI).

To derive CNI: assume NCI = 0; CI = TI; CNI = TC – TI (substituting TI for CI). This methodology is slightly 

different from that used for commercial buildings, as we account for commercial buildings without cooling using 

average tons of cooling per SF, not saturation of buildings with cooling.
38

Table 27. Baseline Residential Appliance Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Dishwasher Saturation 40% 39%

Dishwasher Average Energy 

Use (kWh/Year) 179 179

Electric Cooking Saturation 87% 92%

Electric Cooking Energy Use 

(kWh/Year) 663 663

Clothes Washer Saturation 97% 71%

Clothes Washer Energy Use 

(kWh/Year) 103 103

Clothes Dryer Saturation 74% 59%

Clothes Dryer Energy Use 

kWh/Year) 354 354
39

Efficient Building

Energy savings values for each appliance are derived from either the HECO 2004 (HECO 2004) 

study’s modeling results or FEMP minimum appliance efficiency requirements (FEMP 2010). 

Dishwasher savings are equal to the savings from going from a standard dishwasher to an 

NAECA minimum required efficiency dishwasher. Standard efficiency ovens are replaced by 

ENERGY STAR ovens. For clothes washers, we estimate that the electricity used by the motor is 

10% of total energy use (the value for total energy use, including energy to heat water, is listed in 

FEMP’s purchasing guidelines). The FEMP required minimum efficiency clothes washer model 

uses 750 kWh per year, so we assume that its motor will use 75 kWh per year. Dryer savings are 

values from the 2004 HECO modeling results (Table 28).

Table 28. Efficient Residential Appliance Assumptions

Single Family Multifamily (<20 Units Per 

Building, Per Unit Assumptions)

Dishwasher Average Energy 

Use (kWh/Year) 20 20

Electric Cooking Energy Use 

(kWh/Year) 546 546

Clothes Washer Energy Use 

(kWh/Year) 75 75

Clothes Dryer Energy Use 

(kWh/Year)

188 188
40 

Appendix II: “Hawaii Building Stock Mapping and the Way 

Forward” (Booz Allen Hamilton, April 22, 2009) 

In April of 2009, Booz Allen Hamilton (BAH) began the process of evaluating the energy 

efficiency potential of the Hawaii existing building stock by creating a roadmap of the energy 

demand in the state. This process involved several different data sources for both the residential 

and commercial sectors, which will be outlined in this appendix. Primary data sources on the 

residential side include the 2000 U.S. Census and the U.S. DOE’s Energy Information 

Administration (EIA), while on the commercial side sources included data provided by the 

Hawaii state utilities: Hawaiian Electric Company (HECO), Hawaii Electric Light Company 

(HELCO), Maui Electric Company (MECO), and Kauai Island Utility Cooperative (KIUC). This 

analysis was presented to the Hawaii Clean Energy Initiative (HCEI) Energy Efficiency working 

group at its April, 2009 meetings. 

Residential

On the residential side of the analysis, BAH began by gathering all the information available on 

the number and types of housing units in the state (Census, 2000b). This data was combined with 

the unit energy consumption (UEC data from HECO 2009b; where data was missing, it was 

supplemented with values from HECO 2004) for each housing type, by island, to create the table 

of demand for the year 2000, when the census data was collected (Table 29). 

Table 29. Residential Electricity Demand, by Island (2000)

Residential Elect 

Demand (2000), MWh Oahu Hawaii Maui Kauai Total

Housing: 1-Unit, 

Detached 902,314 306,749 200,931 106,956 1,516,951 

Housing: 1-Unit, 

Attached 198,043 13,140 22,241 9,378 242,802 

Housing: 2 Units 45,583 9,661 6,589 5,460 67,293

Housing: 3 or 4 Units 91,190 9,196 10,218 4,866 115,470

Housing: 5 to 9 Units 127,976 12,216 23,532 6,765 170,488

Housing: 10 to 19 Units 94,022 11,040 16,163 4,907 126,132

Housing: 20 or More 

Units 432,862 25,348 61,040 9,071 528,321 
41

Once the relative energy demand was known, a table of factors was derived outlining the ratio of 

electricity usage for the subsectors within residential (Table 30). These factors were then applied 

to the EIA 2007 Hawaii residential electricity demand to generate the end usage numbers for the 

residential sector, by subsector, adjusted to 2007 demand levels (Table 31). 

Table 30. EIA Electricity Demand, by Sector (2007)

Sector EIA Demand (2007), 

MWh

Commercial & Industrial 6,677,905

Residential 3,182,000

Total 9,859,905

Table 31. Residential Energy Demand Allocation (Base Year) and 2007 Demand Levels

% of Total Residential 

Demand, Base Year 

(2000)

2007 Subsector 

Demand 

(MWh)

Housing: 1-Unit, Detached 55% 1,744,178

Housing: 1-Unit, Attached 9% 279,172

Housing: 2 Units 2% 77,373

Housing: 3 or 4 Units 4% 132,767

Housing: 5 to 9 Units 6% 196,026

Housing: 10 to 19 Units 5% 145,025

Housing: 20 or More Units 19% 607,459

Total 100% 3,182,000

Commercial

On the commercial side, BAH began by collecting the last full year of recorded commercial 

electricity demand data (by sector) from the four major utility companies in Hawaii: HECO 

(2007), HELCO (2005), MECO (2005) and KIUC (2008). HECO and KIUC provided their 

billed MWh figures directly to BAH, while HELCO’s and MECO’s numbers were drawn from 

their most recent Integrated Resource Plans (IRPs) (HELCO 2007, MECO 2007). As this data 

tended to span a range of years from 2005 through 2008 (due to the cyclical nature of the IRP 

process), BAH harmonized it by converting it to a common year’s value. This was done by 

utilizing the relative allocations of electricity demand provided by the utilities, by island, and 

applying them to the total electricity demand for the year 2007 as recorded by the EIA (Table 

30, above) This allowed BAH to maintain a common year across all utilities, while at the same 

time reflecting island-specific variances in electricity demand. The demand factors identified by 

the utilities are provided in Table 32, while the EIA total and the relative distributions for the 

year 2007 calculated from these factors are provided in (Table 33). 
42

Table 32. Commercial Electricity Demand Allocation by Sector 

and Island (% of Total Commercial Demand, Base Year)

Commercial Oahu 

(2007)

Hawaii 

(2004)

Maui 

(2003)

Kauai 

(2008)

Office/Business Services 16% 6% 8% 25%

Hotel 8% 17% 24% 26%

Health 5% 3% 3% 4%

Education 8% 10% 4% 0%

Air Facility/Transport 2% 2% 2% 4%

Manufacturing/Food Processing/ 

Farming 4% 5% 1% 2%

Services/Recreational/Amusement 8% 12% 9% 9%

Restaurant 5% 5% 6% 5%

Retail/Warehouse 16% 21% 24% 18%

Water Pumping 4% 17% 11% 0%

Military 23% 1% 1% 5%

Other 0% 1% 7% 2%

Table 33. Commercial Electricity Demand by Sector and Island (2007)

Commercial (MWh) Oahu Hawaii Maui Kauai Total

Office/Business 

Services 820,000 39,095 60,979 73,231 993,305

Hotel 400,000 113,934 174,806 74,894 763,634

Health 231,000 22,340 22,133 10,214 285,687

Education 402,000 63,669 29,247 791 495,708

Air Facility/Transport 122,000 10,053 12,760 11,139 155,953

Manufacturing/Food 

Processing/Farming 193,000 35,744 4,630 5,075 238,449

Services/Recreational/ 

Amusement 382,000 80,424 67,641 24,529 554,594

Restaurant 257,000 34,627 46,863 14,546 353,036

Retail/Warehouse 814,000 139,625 172,434 51,705 1,177,764

Water Pumping 210,000 111,700 81,192 - 402,892

Military 1,167,000 4,468 5,646 15,374 1,192,488

Other - 5,585 52,735 6,076 64,397

Total 4,998,000 661,264 731,066 287,574 6,677,905
43

Combined

Once the data for the commercial and residential sectors was harmonized to 2007 levels, it was 

aggregated to form Figure 15, below (same as Figure 4 in the main body of the report). This 

data was used to prioritize the key sectors of existing demand for Hawaii to focus on in its 

attempt to reduce its electricity usage by 4,300 GWh in the year 2030 (noncumulative). This also 

forms the basis for the six existing building profiles developed in this report, as the top six 

sectors by demand are where BAH focused its modeling efforts to begin. 

Figure 15. 2007 Electricity use in the state of Hawaii (MWh)
F1146-E(10/2008)

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1. REPORT DATE (DD-MM-YYYY)

June 2010

2. REPORT TYPE

Subcontractor Report

3. DATES COVERED (From - To)

11/17/09-6/30/10

4. TITLE AND SUBTITLE

Hawaii Clean Energy Initiative Existing Building Energy Efficiency 

Analysis: November 17, 2009 – June 30, 2010

5a. CONTRACT NUMBER

DE-AC36-08GO28308

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6. AUTHOR(S)

P. Finch and A. Potes

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NREL/SR-7A2-48318

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IDHW9120

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Booz Allen Hamilton

8283 Greensboro Drive

McLean, VA 22102

8. PERFORMING ORGANIZATION

REPORT NUMBER

Task Order# KLDJ-6-66282-07

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National Renewable Energy Laboratory

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NREL

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13. SUPPLEMENTARY NOTES

NREL Technical Monitor: Sarah Busche/James Galvin

14. ABSTRACT (Maximum 200 Words)

In June 2009, the State of Hawaii enacted an Energy Efficiency Portfolio Standard (EEPS) with a target of 4,300 

gigawatt hours (GWh) by 2030 (Hawaii 2009). Upon setting this goal, the Hawaii Clean Energy Initiative, Booz Allen 

Hamilton (BAH), and the National Renewable Energy Laboratory (NREL), working with select local stakeholders, 

partnered to execute the first key step toward attaining the EEPS goal: the creation of a high-resolution roadmap 

outlining key areas of potential electricity savings. This roadmap was divided into two core elements: savings from 

new construction and savings from existing buildings. BAH focused primarily on the existing building analysis, while 

NREL focused on new construction forecasting. This report presents the results of the Booz Allen Hamilton study on 

the existing building stock of Hawaii, along with conclusions on the key drivers of potential energy efficiency savings 

and on the steps necessary to attain them.

15. SUBJECT TERMS

Hawaii; Hawaii Clean Energy Initiative; HCEI; Energy Efficiency Portfolio Standard; EEPS; energy efficiency; energy 

savings; new construction; existing buildings; Building Efficiency

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