Beyond Supply Curves 

Fred Gordon and Lakin Garth, Energy Trust of Oregon, Inc. 

Tom Eckman and Charles Grist, Northwest Power and Conservation Council 

ABSTRACT 

 Utility resource planners use estimates of the available energy efficiency resources to 

choose how much conservation to fund, and how much fossil generation and transmission and 

distribution construction can thus be deferred. To deal with an initially skeptical utility industry, 

conservation supply estimation techniques have primarily focused on efficiency measures that 

work and are commercially available. Estimates of savings, cost, and date of entry for individual 

“near-commercial” conservation have not been accurate and perhaps cannot be given the stopstart nature of product innovation. While conservation is now well established as a resource in 

many states, the authors wondered if the standard estimation methods produce systematically 

low estimates of savings available over a 20 year period. We sought a method of forecasting 

savings that incorporates market and program innovations. We reviewed the history of estimates 

for several conservation measures and concluded that aggressive program activities tend to help 

prompt development of consensus techniques to measure efficiency, and accelerate technology 

innovation and price declines. Thus, the idea that we forecast conservation economics and then 

make choices to fund cost-effective measures is only partly true. It appears that conservation 

program entities fund measures and develop programs, and then the affordability, measurability 

and volume of savings often improves progressively over several years. As policy makers are 

asking how we can get more conservation faster, the answer may depend on whether we are 

willing to make technology and program development investments with less certain and longerterm payoffs in energy savings. 

Introduction 

 

 Conservation Supply Curves have been developed since the advent of energy 

conservation as a power resource to determine the potential efficiency resource available at 

various costs over a certain period of time in the future. Typically, the supply curves provide an 

estimate of the technical potential available (annual therms, annual megawatt hours) at various 

prices by a certain date. A portion of that potential is assumed to be “achievable.” In the 

Northwest, convention and experience has shown that the achievable rate is 85% of technical 

potential for most measures. Then, a penetration function is created, based on experience and 

circumstances more than any mathematical wizardry, to indicate how quickly a measure’s 

achievable potential can be acquired (NPCC 2007b). 

Our analysis focuses on the first part of this process, concerning the methods for 

estimating the technical potential for efficiency. In order to understand how we might better 

forecast the potential for efficiency, the paper reviews how past estimates for selected 

technologies have evolved over the past 25 years. 

While we were hoping (against all reason) to find a pattern of prior conservation 

estimates that could lead to a new method or correction to conventional forecasting methods, we 

discovered (as we feared) that the improvements to estimates of savings over time for different 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-98
measures vary dramatically. After summarizing the evolution of technical potential for three 

residential efficiency measures, we propose not a new method of developing a point forecasting 

efficiency, but acknowledgement of the conservative bias of existing methods and greater focus 

on forecasting the up-side uncertainty in those estimates. 

Background 

Most credible studies use the same method to estimate technical potential. The forecaster 

estimates savings per home, square foot of commercial floor area, or per unit of industrial 

consumption, and then multiplies by the number of units projected to exist in each year of the 

forecast (e.g. savings per home x number of homes = total savings.) Utility load forecasts and 

demographic data provide the framework for forecasting savings. Data sources for savings 

include program evaluations and engineering calculations. The process is done separately for 

new and existing homes and businesses to reflect the influence of codes and building practices 

on new structures. The results are aggregated across measures and then building or industry 

types to arrive at total potential. Care is taken not to count savings twice where measures 

overlap. 

 Utilities and regulators use conservation supply curves not only to decide how much 

conservation to buy, but how much fossil generation not to buy because conservation can reduce 

load less expensively. In addition, supply curves have provided broad guidance regarding what 

areas and sectors to examine in order to expand or enhance efficiency programs. At the genesis 

of conservation supply curves (circa 1983) utility and regulatory audiences were unfamiliar with 

conservation and skeptical of the viability of conservation as a resource. As a result, analysts 

included only measures that were economically viable and already proven to be available and 

work. This methodology has continued through present-day estimates. Through much of the 

1990’s, these conservative estimates were quite adequate, because while there were bursts of 

interest in conservation among utilities, there were also lulls where activity decreased or was 

non-existent. Programs did not operate on a sustained enough basis to deplete the forecast 

available savings, so there was little interest in exploring whether the supply curves were too 

conservative. At the same time, experience showed that much of the acquired conservation 

resulted from measures that were either not in the supply curves only a few years before, or were 

forecast to be too expensive. Innovation was clearly a force in conservation acquisition, but was 

not relied upon to predict potential savings. 

 As the cost of fossil generation and natural gas, and the interest in reducing carbon 

emissions have increased, questions from Northwest utilities, regulators, and other energy 

planning entities have focused more on whether there is more efficiency available. With the 

hopes of answering these questions, the authors of this paper studied how supply curves have 

changed over the past 25 years, and the factors causing the changes. We hoped this would 

provide some information to address the thorny question “how else could you do it?” 

 The most consistent and longest-running series of supply curve estimations are those of 

the Northwest Power and Conservation Council (NPCC or The Council.) The Council has 

produced supply curves in support of a new Power Plan every few years since 1983. As time has 

passed, these estimations have become more detailed, reliable, and purposeful. The analysis 

within this paper uses the series of conservation supply curves developed by the Council 

beginning with the First Power Plan in 1983. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-99
Table 1 illustrates the changes to 20-year conservation supply curve estimates across the 

range of Power Plans from the NPCC. The numbers reflect the medium forecast achievable 

potential MWa savings for all cost-effective measures for each sector, except where noted.1

 As 

can be seen from the forecast savings in the table, there is hardly a linear trend on the amount of 

estimated efficiency in the supply curves at the sector level. These changes over time serve as 

the impetus for this project. 

 

Table 1. NPCC Conservation Forecast (MWa)2

Year of Plan: 1983 1986 1991 1996 2005 

Residential 2415 1895 1160 696 1275 

Commercial 1435 1162 1051 451 1100 

Industrial 550 500 490 565 350 

Total 4400 3557 2701 1712 2725 

Source: (NPCC 1983, 1986, 1991, 1996, 2005) 

 The forecasts do not follow a clear upward or downward trend. The primary causes are 

(1) the lesser detail and less evolved methods used in the first two plans, (2) the fact that 

achieved conservation was deducted progressively from each plan, and (3) lesser levels of effort 

put into identifying new resources in 1996, both by the Power Council and by efficiency 

programs. These complex factors mask the evolution of technical potential and led the authors to 

focus on individual measures to understand how the technical resource evolved. 

Residential Windows in Existing Homes

Background 

In Pacific Northwest homes, a significant fraction of the electric use comes from space 

heating due primarily to the climate and the higher-than-typical saturation of electric heat. Since 

the Council’s First Power Plan, there has been great attention given to the potential savings from 

weatherization programs. Energy savings resulting from the installation of efficient windows for 

existing houses have been included in each of the Council’s five plans and have constituted a 

significant component of the potential savings from each plan. 

Methodology 

 Understanding of the influence of technological change on residential replacement 

window efficiency is complicated by a number of factors including: the variety of window 

designs, fuel source choice, climate variations, existing housing stock, engineering approaches to 

 

1

 Achievable savings were more consistently available from various forecasts than technical potential. Achievable is 

forecast for most measures is technical potential x 85%. So the trends would also apply to technical potential. 2

 The estimates for the Power Plans in 1983 and 1986 correspond to the “high” forecast. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-100
energy usage, changes in baseline, and product testing methods. To address these factors simply, 

the analysis focuses on the electric efficiency per square foot of an “average” window in an 

existing 1350 square foot home, for a weighted-average Northwest climate.3

 This “average” 

window is designed to represent several windows types. It is combined with other housing sizes 

to provide total region-wide potential savings. The 1350 square foot house was chosen since it 

was included in each plan and its characteristics remained constant over time.4

 

Analysis 

 The Council’s first three plans relied on savings estimates based on the recommended 

calculations in the ASHRAE handbook (NPCC 1983, 1986, 1991). When these efficiency levels 

were translated into actual products for marketing, they meant something similar to “single pane 

with aluminum frames,” “double pane with wood frames” and “triple pane with thermally broken 

aluminum frames.” A standard scale and procedure for rating window efficiency did not exist 

until the establishment of the National Fenestration Rating Council (NFRC) and the adoption of 

U-values as the testing and certification criteria in 1991. NFRC certified labels were eventually 

made available on windows manufactured beginning in 1993. Once NFRC testing and 

certification was in place, the penalties for aluminum frames and heat transfer through “spacing 

bars,” etc. became more apparent. Efficiency analysts (and the manufacturers) realized what was 

previously thought to be equivalent to a U-30 product was in actuality only a U-50 product. 

Similarly, what was previously believed to be equivalent to a U-90 product was actually only a 

U-120 product (NFRC 2005). 

As Table 2 shows, the efficiency of both baseline windows and efficient windows in the 

Council’s first two plans were overestimated by over 13% due to the lack of a reliable testing 

procedure. The improved testing procedure determines the heat loss of the entire window, 

including the frame and glass, whereas the previously reported ASHRAE estimates reported 

savings based upon center of glass heat loss. The implementation of a consistent testing 

procedure and rating system resulted not only in more reliable efficiency estimates, but also 

allowed for additional technological advancements beyond what had been available at the time 

the NFRC adopted U-values. 

Table 2. Existing SF Window Savings and Use5

 U-Factor Annual Use and Savings 

Existing Homes 

Original 

Assumption Revised – Tested 

PNW 

Plan 1-3 

PNW 

Plans 4-5 

Single Pane Aluminum 0.90 1.20 

 

13,074 

 

14,511 

Dual Pane Wood/Alum 

Thermal break – Clear Glass 0.30 0.50 

 

10,051 

 

11,079 Difference 

Delta U-factor 0.60 0.70 

 

3,023 

 

3,431 13% 

Source: NPCC Internal Analysis 

 

3

 The Council defines three climate zones in each of its plans, based on a level of heating degree-days. Zone 1 

corresponds to Portland/Seattle, Zone 2 is typified by Spokane, and Zone 3 is Missoula. 4

 The other two existing single-family home sizes are 850 and 2100 sq. ft. 5

 The kWh/yr totals in Table 2 are regionally weighted for the typical 1350 sq. ft. home. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-101
 The consensus among manufacturers and analysts for enhanced testing methods (and 

therefore better savings estimates) led to product improvements such as vinyl frames and 

improved spacer material. In addition, the emergence of “low-e” coatings and “gas fills,” which 

reduced the heat transfer through the glass, allowed manufacturers to achieve the actual U-values 

that were previously reported. Thus, while rated efficiency levels were static through the first 

three plans, the new products could meet the efficiency levels that the old products were thought 

to meet. In addition to this compensation for incorrect early ratings, Class 25 (U-.25) windows 

were included in the Council’s Fourth Plan in 1996 and in the Fifth plan. This inclusion 

represents the highest level of savings for efficient windows in the supply curves, with a 33% 

improvement in the energy use compared with the original baseline units from the Council’s plan 

in 1983, as shown in Table 3. 

Table 3. Existing SF Window Efficiency Savings6

Existing Homes U-Value Energy Use (kWh/yr) % Change from baseline7

1983 Baseline (actual) U 1.2 14,511 - 

1983 Baseline (assumed) U .90 13,074 10% 

1983 Efficient Case (actual) U .50 11,079 24% 

1983 Efficient Case (assumed) U .30 10,051 31% 

Best Efficient Case in 2005 U .25 9,790 33% 

Source: NPCC internal analysis 

 Supply curve analysis improvements and program activities clearly co-evolved and were 

interdependent. With the introduction of the U.S. DOE Energy Star Windows Program in 1997, 

window manufacturers wishing to participate and receive Energy Star recognition first had to 

obtain performance values by implementing NFRC testing procedures (NFRC 2005). The 

establishment of a nationally recognized testing program brought credence to the Energy Star 

window labels, laying the groundwork for further efficiency enhancements. With an authorized, 

national efficiency benchmark in place, utilities and regional efficiency organizations such as the 

Northwest Energy Efficiency Alliance could begin program efforts to acquire additional electric 

savings, achieve market transformation, encourage better building energy codes, and work with 

manufacturers to develop further technologies to advance energy efficient windows (NEEA 

2002). 

 The major lesson from this experience is that standardized testing procedures, developed 

in collaboration with manufacturers, are essential to reliable supply curve estimates and to 

driving innovations in efficiency. With a transparent benchmark in place, U-35 and U-30 

windows have become available for most window types, and efforts are underway to make U-25 

and U-20 windows more affordable and available, and to explore further efficiency gains. This 

collaborative standards development process is more feasible for measures where efficiency 

programs are active. 

 

6

 The kWh/yr use figures are based on a regionally weighted 1350 sq. ft. house. 

7

 The percentage change is from the actual 1983 baseline of U-1.20. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-102
Compact Fluorescent Lamps for Homes 

Background 

It is difficult to overstate the magnitude of the shift in the conservation potential for 

compact fluorescent lamps (CFLs) for residential lighting between the years 1983 and 2005. In 

the 22 years between the Council’s First and Fifth Power Plans, compact fluorescent light lamps 

became the largest available residential conservation resource, with an estimated 625 MWa 

available (NPCC 2005). The shift from scant mention to the most valuable resource happened 

suddenly, and recently. Table 4 below shows the dramatic increase in savings over time 

attributable to CFLs. The variations in price reflect both changes in estimated bulb cost and the 

assumed number of bulbs installed per home. 

Table 4. Estimates of Residential CFL Savings for 20-Year Periods 

Plan/Year Achievable Savings (MWa) Assumed Cost per Bulb 

Plan I- 1983 105 __ 

Plan II- 1986 Not included __ 

Plan III- 1991 24 $12 

Plan IV-1996 44 $10 

Plan V- 2005 625 $3 

Source: (NPCC 1983, 1986, 1991, 1996, 2005) 

Early Estimates 

The initial estimate of savings for CFLs was modest. In 1983, CFLs were an exotic 

concept, barely on the market. Their inclusion in the plan (with other fluorescent technologies) 

was based on price and product development expectations that were not met for many years. 

The measure was included to indicate its future importance, and because with very little 

experience, the first plan criteria for inclusion of measures was not as exacting as for later plans. 

In the Second Power Plan in 1986, there was only a simple mention of the possibility that CFLs 

could replace incandescent light bulbs to achieve energy savings. In that year, the Council only 

examined the impact of replacing a 75 watt incandescent with an 18 watt fluorescent (NPCC 

1986). Ensuing plans concluded that the technology was applicable only to a few fixtures per 

home, with appropriate wattages and geometry suited to the limited range of available products, 

and enough hours of use to justify the high price. The Plan allowed that the estimation methods 

at that time were “rough” due to the lack of reliable data on run times and fit for the variety of 

fixtures and room use in a home (NPCC 1986). 

 The estimation procedure was nearly identical for the Council’s Third and Fourth Power 

Plans, with one significant difference. The similarities included the same assumptions for yearly 

watts displaced (50), the number of installed bulbs per existing home (3), the percentage of 

homes with installations (50%) and the electric space heat interaction effects (22%).8

 The main 

distinction in estimation methodology between these two plans was improved information about 

 

8

 This is the average interaction with electric heat and cooling, including gas heated homes. The interaction for an 

individual electric heated home is roughly double this number, due to high heating loads and small air conditioning 

loads in the Northwest. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-103
“on-time” of each lamp per day. The Fourth Plan assumes 3 hours for interior and 5 hours for 

exterior fixtures while the Third Plan assumed only 2 hours for all fixtures. The effect of this 

assumption, in addition to any changes in housing stock forecasts, explains the increase in 

potential savings (NPCC 1991, 1996). 

The similarity in estimations would seem to indicate that there were no major 

technological breakthroughs or major market transformations taking place. In fact, in both of 

these plans, the Council makes recommendations for a manufacturer buy-down approach by 

utility conservation programs in the interest of increasing actual efficiency savings. Also worth 

noting, the period during the decade when these two plans were released has been characterized 

as one where there was an energy surplus and a lapse in regulatory attention to efficiency, 

conditions that meant there was only modest interest in upgrading conservation supply estimates 

(NPCC 2007). These combined effects (which were common across much of the country), and 

the absence of a regionally established conservation mechanism helped contribute to the lack of a 

technological breakthrough necessary for widespread availability and adoption of CFLs. 

Technological Improvements 

Early compact fluorescent light bulbs were an immature, market-unfriendly product. In 

addition to the enormously high initial cost in comparison to today’s prices and the prices of 

incandescent bulbs, CFLs suffered from a variety of problems that limited their early adoption, 

including size, color, and reliability, lack of products for specialty fixtures, power factor issues, 

limited available wattages, and flicker. At the time of the Council’s Third Plan, there were no 

CFLs that could replace the full output of a 75-watt incandescent bulb (NPCC 1991). 

CFL program support grew slowly in the late 1990’s in parallel with development efforts 

by manufacturers. Manufacturers offered lower-cost, higher-quality CFLs to the market shortly 

before the 2000-2001 West Coast power crisis. During the years before this crisis, the Northwest 

Energy Efficiency Alliance (NEEA) had established a network of retail outlets that carried CFLs. 

NEEA also worked with manufacturers to improve product quality. The result, especially as 

utilities expanded CFL programs to meet the crisis, was a huge spike in CFL sales (an increase of 

over 3 million from 2003 to 2005 (NEEA 2006). While not every CFL worked perfectly and 

although CFLs did not fit every fixture, the increase in consumer acceptance, augmented 

considerably by lower prices, influenced resource planners to re-evaluate the future potential of 

CFLs. Following the sales explosion, manufacturers continued to develop a greater variety of 

products, and reduce product size. With initial funding from utilities and NEEA, a testing 

protocol was set up for bulb reliability, and eventually funded by the CFL industry. EPA’s 

ENERGY STAR label gradually increased its quality and efficiency qualification requirements, 

prodding the industry to further improvements, including smaller size, less mercury content, a 

variety of color temperatures, more reliability, and longer life. 

Recent Estimates 

By the release of the Council’s Fifth Power Plan in 2005, CFLs had become the largest 

efficiency option for the residential sector. The confluence of technological breakthroughs and 

regionally coordinated market transformation efforts led to nearly a fourteen-fold increase in 

savings between 1996 and 2005 (Table 4). With more market research and engineering 

information available, there were some significant methodological changes between the two 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-104
plans. The average number of fixtures, lamps, and on time was estimated separately for different 

rooms and areas of the house instead of just an aggregate total. The savings were then estimated 

based on the wattage displacement from replacing an incandescent bulb with an approximately 

equivalent CFL. Adjustments were also made for removal, take-back, and (as before) space 

conditioning interaction effects. With more reliable engineering models, the space heat 

interaction value is only 12% in the Fifth Plan compared with 22% in the prior plans (NPCC 

2005). 

As a larger variety of bulbs became available, this increased the number of fixtures per 

home where CFLs are suitable. The additional research conducted between the Fourth and Fifth 

Plans provided a much firmer basis for estimating the potential regional savings from this 

technology. In the Fifth Power Plan, the Council assumed a maximum market saturation level of 

86% (up from 50%), thus assuming that incandescent bulbs, in 20 years, would enjoy only a 14% 

market share. In addition, the cost estimate per bulb fell from $11.50 to $3 (NPCC 2005). 

To summarize, the emergence of lighting as the largest conservation opportunity within 

the Power Council’s residential supply curves (41% of residential potential) is due mainly to 

technological changes that increased the applicability, affordability, reliability, life, and 

availability of CFLs. These changes were driven by and also helped drive regional and national 

efforts to increase quality, awareness, and sales. The more than tenfold increase in potential 

from the previous plan shows that analysts were unable to predict the trajectory of this 

technology and its timing, or at least were unable to make a convincing enough case. 

Residential Clothes Washers in Existing Homes 

Background 

The evolution of energy savings from clothes washers in residential applications 

illustrates how savings can increase with product and market evolution, but also illustrates many 

of the complications with analyzing and forecasting innovation. In each plan beginning with the 

NPCC’s First Power Plan in 1983, continuing through the most recent Fifth Power Plan in 2005, 

residential clothes washers have been listed as a conservation resource. Between 1991 and 2005, 

the electric efficiency resource available from home clothes washers nearly doubled, even 

ignoring efficiency that was not included in later plans due to the increase in baseline efficiency 

(NPCC 1991, 2005). The driving forces behind the changes in potential energy savings 

estimates for residential clothes washers are (1) technological changes that increased the 

efficiency of residential clothes washers, (2) increases in baseline levels due to changes to 

minimum federal standards, resource acquisition programs, and market transformation programs, 

and (3) improved efficiency estimation methods. 

Changes in Savings Estimates 

 As discussed below, the “yardstick” used by most analysts was changed in the 1990s. To 

compare consistently the efficiency across plans in this paper we have chosen to analyze the 

savings per machine per cycle for this paper.

Comparing the baseline assumption in 1991 with the most efficient unit included in the 

Council’s Fifth Plan in 2005 (Table 5), the washer energy use decreases from 3.02 to 0.44 

kWh/cycle, reflecting an 85% reduction. Including the effects of dryer energy use, the total 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-105
energy use decreases from 5.34 to 1.36 kWh/cycle, an improvement of 74% in overall efficiency. 

Table 5 shows that the “most efficient unit” improved markedly between plans, from 59% of 

1991 baseline load in the 1991 plan to 25% of 1991 baseline load in the 2005 plan. 

Table 5. NPCC Plan III-V Baseline and Efficient Clothes Washer Energy Usage 

Plan (Year) Efficiency Level Washer Use 

(kWh/Cycle) 

Dryer Use9

(kWh/cycle) 

Total 

(kWh/cycle) 

Change (from 

1983 baseline) 

III (1991) EF 

Baseline 0.8610 3.02 2.32 5.34 

Most efficient 3.1911 0.81 2.32 3.13 2.21 

IV (1996) EF 

Most efficient 3.2512 0.64 1.37 2.01 3.33 

V (2005) MEF 

Most efficient 2.20 0.44 0.91 1.36 3.98 

Source: (DOE 1990), (NPCC 1991, 1996, 2005) 

Technology 

Prior to the Third Power Plan, the Council estimated the potential yearly savings based 

on savings from a cold rinse option which saves hot water energy. In the Third Plan, the 

possibility that a horizontal axis machine might be manufactured and marketed for residential 

use was considered. Prior to this time, horizontal axis machines were used widely in commercial 

applications, and by residential users in Europe and Asia (DOE 1990). However, without viable 

options domestically in the early 1990’s, the Council chose to leave out the potential savings of 

h-axis machines as the high levelized cost of the machine outweighed those savings. 

 The two predominant technological advancements in clothes washer efficiency with the 

horizontal axis machine were reduced water usage, leading to still lower hot water energy use, 

and the addition of high speed spin cycles. The higher speed spin cycles of horizontal axis 

washers can greatly reduce the remaining moisture content (RMC) of the clothes before they are 

placed into the dryer, resulting in much less energy required to dry the clothes. 

Methodology 

 

The Council’s Fourth Power Plan was the first to include the effects of reduced dryer 

usage into the overall savings attributed to clothes washers. Between the Fourth and Fifth Power 

Plan, the DOE adopted the MEF rating as the new testing procedure for clothes washers, thus 

incorporating water factor and RMC into the overall metric for clothes washers. As EF and MEF 

are not directly comparable, Table 5 above includes both washer and dryer use per cycle to 

provide for reasonable comparison. The inclusion of RMC into the conservation resource 

calculation provides a more robust estimation, and reflects the true overall energy savings. 

 

9

 The savings attributed from reduced dryer usage are not included in supply curve estimates until the Fourth Plan in 

1996. The kWh/cycle dryer usage numbers are provided for 1991 to be used for comparison purposes and were 

taken from DOE/CE 0299P. 

10 The baseline EF level for 1991 was taken from DOE/CE 0299P. 

11 The EF 3.19 unit was taken from DOE/CE 0299 and hypothetically constructed as the “maximum technologically 

feasible” washer possible given existing technology at the time. 12 The EF 3.25 also assumes a remaining moisture content of 40%. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-106
Programs 

A remarkable combination of local, regional, and national programs has helped improve 

the efficiency of clothes washers. The early efforts of the Western Utility Consortium (WUC) 

and implementation of the High Efficiency Laundry Metering and Market Analysis (THELMA) 

helped establish measurement methods and identify efficient features. These were brought to 

manufacturers and the DOE and helped establish standardized testing procedures and federal 

standards. The early programs also created and showed market interest in efficient washers 

(CEE 2001). Starting in 1997, a regional program coordinated by NEEA and including utilities 

helped spur the movement towards market transformation. The Consortium for Energy 

Efficiency followed with a national program using consistent specifications. As markets 

accepted higher efficiencies, they worked their way into federal standards, and CEE and utility 

specifications moved to higher efficiency levels (NEEA 2001). After the establishment of the 

Energy Star program, CEE efficiency tiers for rebates were harmonized with ENERGY STAR 

efficiency levels. The effect of this accord was reduced confusion among utilities, 

manufacturers, retailers, and consumers by providing consistent estimates of savings to all 

parties (CEE 2001). 

 The primary lesson from this market is that program efforts can drive improvements in 

technology, testing procedures, and market branding of efficiency. Consistent, reliable and 

purposeful estimates of savings helps retailers different efficient products and profit from them, 

which, as efficient units become commonplace and Federal standards increase, leads to 

accelerated technology development. 

Conclusion 

 Through this research, we were unable to identify a systematic trend or mathematical 

function that helps forecast supply curves beyond established technology. While savings 

potential grows and prices moderate over time for many measures, this is neither universal nor 

consistent between measures. The results may suggest that rather than developing a better point 

estimate of efficiency potential by a certain year, analysts should consider the asymmetric 

uncertainty around efficiency savings estimates. Integrated resource plans could establish a 

confidence interval around conventionally derived supply curve-driven savings estimates that 

acknowledges that there is likelihood of more savings than of less. 

 For example, a conventional supply curve does not forecast zero net energy new 

buildings and homes, but there are several entities in the US committed to developing them and 

demonstration homes and buildings under construction. Zero net energy initiatives generally set 

a goal of 60-70% energy reduction through efficiency and meeting additional needs through 

renewable energy. While this is not now proven feasible on a mass scale, forecasters can use this 

technology to create a “high case” for savings with a modest probability, and assign 

progressively more certainty to more conservative estimates. Another approach is to analyze 

potential savings from near-mature measures, without pretension of precision about costs or 

savings, and then assume that perhaps half of these technologies will mature in the ensuing five 

years. 

 How can the precision of these estimates be improved? Our research shows that when 

programs invest in higher levels of efficiency, this helps drive measurement improvements and 

technical innovation, resulting in larger and more reliable conservation supply estimates. Thus, 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-107
it appears that the best way to improve conservation forecasts is to make investments and take 

risks to prove and acquire additional resources. Given the low current cost of efficiency and 

high price and delivery reliability compared to fossil resources, a less risk-averse approach to 

forecasting and program funding than is currently employed by many program delivery entities 

may be both supportable and financially prudent. 

References 

[CEE, 2001] Consortium for Energy Efficiency. 2001. The Residential Clothes Washer 

Initiative: A Case Study on the Contributions of a Collaborative Effort to Transform a 

Market. http://www.cee1.org/eval/RCWI_eval.pdf 

[DOE, 1990] United States Department of Energy. 1990. Technical Support Document: Energy 

Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and 

Clothes Dryers, DOE/CE-0299P. Washington, D.C.: United States Department of 

Energy. 

[NEEA, 2001] Northwest Energy Efficiency Alliance, 2001. Energy Star Resource Efficient 

Clothes Washer Program: Market Progress Evaluation Report #5. Portland, Ore. 

http://www.nwalliance.org/research/reports/83.pdf

[NEEA, 2002] Northwest Energy Efficiency Alliance, 2002. Market Progress Evaluation Report 

for the Energy Star Windows Project. Portland, Ore. 

[NEEA, 2006] Northwest Energy Efficiency Alliance. 2006. 2005 Annual Report. Portland, 

Ore. http://www.nwalliance.org/research/documents/AR2005.pdf 

[NFRC, 2003] National Fenestration Resource Council. 2003. Behind the Glass: A History of 

NFRC as Told by the People Who Lived It. Silver Spring, Md. 

http://www.nfrc.org/documents/NFRC%20History.pdf 

[NFRC, 2005] National Fenestration Resource Council. 2005. The Facts About Windows and 

Heat Loss. Silver Spring, MD. www.nfrc.org/documents/U-Factor.pdf

[NPPC, 19833] Northwest Power Planning Council. 1983. Northwest Power Plan. Portland, 

Ore. 

[NPPC, 1986] Northwest Power Planning Council. 1986. 1986 Northwest Power Plan. 

Portland, Ore. 

[NPPC, 1991] Northwest Power Planning Council. 1991. 1991 Northwest Power Plan. 

Portland, Ore. 

[NPPC, 1996] Northwest Power Planning Council. 1996. The Fourth Northwest Conservation 

and Electric Power Plan. Portland, Ore. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-108
[NPCC, 2005] Northwest Power and Conservation Council. 2005. The Fifth Northwest Electric 

Power and Conservation Plan. Portland, Ore. 

[NPCC, 2007a] Northwest Power and Conservation Council. 2007. Council Briefing Book. 

Portland, Ore. 

[NPCC, 2007b] Northwest Power and Conservation Council. 2007. Achievable Savings: A 

Retrospective Look at the Northwest Power and Conservation Council’s Conservation 

Planning Assumptions. Portland, Ore. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-109