### 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 in itially skeptical utility industry, conservation supply estimation techniques have pr imarily 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 can not 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 sta ndard estimation methods produce systematically low estimates of savings available over a 20 y ear period. We sought a method of forecasting savings that incorporates market and program innovations. We revi ewed the history of estimates

for several conservation measures and concluded th at aggressive program activities tend to help prompt development of consensus techniques to measure efficiency, and accelerate technology innovation and price declines. T hus, 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 progra ms, and then the affordability, measurability and volume of savings often impr oves progressively over several ye ars. As policy makers are asking how we can get more conservation fast er, the answer may depend on whether we arewilling 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 th e potential efficiency resource available at

various costs over a certai n period of time in the future. Typi cally, the supply curves provide an estimate of the technical potential available (a nnual therms, annual megawatt hours) at various prices by a certain date. A portion of that po tential is assumed to be “achievable.” In the Northwest, convention and experience has shown th at 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 wi zardry, to indicate ho w 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 co rrection to conventional forecasting methods, we discovered (as we feared) that the improvements to estimates of savings over time for different

8- 98 2008 ACEEE Summer Study on Energy Efficiency in Buildingsmeasures 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 exis ting 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 co mmercial 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 fo recasting savings. Data sources for savings include program evaluations and engineering calculations. The pr ocess 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 buildi ng or industry types to arrive at total potenti al. Care is taken not to count savings twice where measures overlap. Utilities and regulators use conservation supp ly 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 addi tion, supply curves have provided broad guidance regarding what

areas and sectors to examine in or der to expand or enhance effici ency programs. At the genesis of conservation supply curves (circa 1983) utility and regulatory audiences were unfamiliar with conservation and skeptical of the viability of cons ervation as a resource. As a result, analysts included only measures that were economically viable and alread y 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 u tilities, there were also lulls where activity decreased or was non-existent. Programs did not operate on a su stained enough basis to deplete the forecast available savings, so there was li ttle interest in exploring whet her the supply curves were too conservative. At the same time, experience sh owed 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 cl early a force in conservation acquisition, but was not relied upon to predic t potential savings. As the cost of fossil generation and natura l gas, and the interest in reducing carbon emissions have increased, questions from Northw est utilities, regulators, and other energy planning entities have focused more on whether th ere 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 th orny question “how else could you do it?” The most consistent and longest-running seri es of supply cu rve estimations are those of

the Northwest Power and Conservation Counc il (NPCC or The Council.) The Council has produced supply curves in support of a new Powe r Plan every few years since 1983. As time has passed, these estimations have b ecome more detailed, reliable, and purposeful. The analysis within this paper uses the series of cons ervation supply curves developed by the Council beginning with the First Power Plan in 1983.

8- 99 2008 ACEEE Summer Study on Energy Efficiency in BuildingsTable 1 illustrates the changes to 20-year c onservation supply curve estimates across the range of Power Plans from the NPCC. The nu mbers reflect the medium forecast achievable potential MWa savings for all cost-effective me asures for each sector, except where noted. 1 As can be seen from the forecast savings in the tabl e, there is hardly a linear trend on the amount of estimated efficiency in the supply curves at the sector level. These ch anges 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 evol ved 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, bot h by the Power Council and by efficiency programs. These complex factors mask the evolu tion 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 frac tion of the electric use comes from space heating due primarily to the climate and the highe r-than-typical saturation of electric heat. Since the Council’s First Power Plan, there has been gr eat attention given to th e potential savings from weatherization programs. Energy savings resulti ng 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 technol ogical change on resi dential replacement window efficiency is complicat ed by a number of factors in cluding: the variety of window designs, fuel source choice, climate variations, existing housing stock, engi neering approaches to

1 Achievable savings were more consiste ntly available from various forecasts th an technical potential. Achievable is

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

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

8- 100 2008 ACEEE Summer Study on Energy Efficiency in Buildingsenergy usage, changes in baseline, and product tes ting 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 type s. 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 marketin g, 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 pro cedure for rating window e fficiency did not exist

until the establishment of the National Fenestra tion Rating Council (NFRC) and the adoption of U-values as the testing and certif ication criteria in 1991. NFRC cer tified labels were eventually

made available on windows manufactured be ginning in 1993. Once NFRC testing and certification was in place, the penalties for alumi num frames and heat transfer through “spacing bars,” etc. became more apparent. Efficiency an alysts (and the manufactur ers) 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 effi cient windows in the Council’s first two plans were overestimated by ove r 13% due to the lack of a reliable testing procedure. The improved testing procedure de termines the heat loss of the entire window, including the frame and glass, whereas the pr eviously reported ASHRAE estimates reported savings based upon center of glas s heat loss. The implementa tion 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 Use 5

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 pl ans, based on a level of heating degree-days. Zone 1

corresponds to Portland/Seattle, Zone 2 is ty pified 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.

8- 101 2008 ACEEE Summer Study on Energy Efficiency in BuildingsThe consensus among manufacturers and anal ysts for enhanced testing methods (and therefore better savings estimates) led to pr oduct improvements such as vinyl frames and improved spacer material. In addition, the emerge nce 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 effi ciency levels that the old products were thought to meet. In addition to this compensation fo r 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 comp ared with the origin al baseline units from the Council’s plan in 1983, as shown in Table 3.

Table 3. Existing SF Window Efficiency Savings 6

Existing Homes U-Value Energy Use (kWh/yr) % Change from baseline 7

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 progr am activities clearly co-evolved and were interdependent. With the intr oduction of the U.S. DOE Ener gy 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 test ing program brought credence to the Energy Star window labels, laying the groundwork for further effi ciency enhancements. With an authorized, national efficiency benchmark in place, utilities a nd regional efficiency organizations such as the Northwest Energy Efficiency Alliance could begin program efforts to acqui re 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 essent ial to reliable supply curve estimates and to driving innovations in efficiency. With a tr ansparent 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, a nd to explore further efficiency gains. This collaborative standards developm ent 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.

8- 102 2008 ACEEE Summer Study on Energy Efficiency in Buildings

### 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 resident ial lighting between th e years 1983 and 2005. In the 22 years between the Council’s First and Fift h Power Plans, compact fluorescent light lamps became the largest available residential cons ervation resource, with an estimated 625 MWa available (NPCC 2005). The shift from scant men tion to the most valuab le resource happened suddenly, and recently. Table 4 below shows th e dramatic increase in savings over time attributable to CFLs. The variations in price reflect both changes in es timated 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 wa s modest. In 1983, CFLs were an exotic concept, barely on the market. Their inclusion in the plan (w ith other fluorescent technologies) was based on price and product development expect ations that were not met for many years.

The measure was included to indicate its future importance, and becaus e 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 in candescent with an 18 watt fluorescent (NPCC 1986). Ensuing plans concluded th at the technology was applicable only to a few fixtures per home, with appropriate wattages and geometry su ited 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 differe nce. The similarities included the same assumptions for yearly watts displaced (50), the number of installed bu lbs per existing home (3), the percentage of homes with installations (50%) and the elec tric space heat interaction effects (22%). 8 The main distinction in estimation methodology between th ese 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.

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

exterior fixtures while the Third Plan assumed onl y 2 hours for all fixtures. The effect of this assumption, in addition to any changes in housi ng 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 ma jor market transformations taking place. In fact, in both of these plans, the Council makes recommendati ons for a manufacturer buy-down approach by utility conservation programs in th e 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 a ttention to efficiency, conditions that meant there was only modest inte rest in upgrading conser vation supply estimates (NPCC 2007). These combined ef fects (which were common acros s much of the country), and the absence of a regionally estab lished conservation mechanism helped contribute to the lack of a technological breakthrough necessary for widesp read availability and adoption of CFLs.

Technological Improvements

Early compact fluorescent light bulbs were an immature, market-unfriendly product. In addition to the enormously high in itial cost in comparison to toda y’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 pr oducts for specialty fixtur es, power factor issues, limited available wattages, and flicker. At the ti me 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 ever y fixture, the increase in c onsumer acceptance, augmented considerably by lower prices, influe nced resource planners to re-evaluate the future potential of CFLs. Following the sales explosion, manufacturer s continued to develop a greater variety of products, and reduce product size. With initial funding from u tilities and N EEA, a testing protocol was set up for bulb re liability, and eventually fund ed by the CFL industry. EPA’s ENERGY STAR label gradually increased its qual ity and efficiency qualif ication requirements, prodding the industry to further im provements, including smaller size, less mercury content, a variety of color temperatures, mo re 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 resident ial sector. The confluence of technological breakthroughs and regionally coordinated market tran sformation 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 si gnificant methodological changes between the two

8- 104 2008 ACEEE Summer Study on Energy Efficiency in Buildingsplans. The average number of fi xtures, lamps, and on time was es timated separately for different rooms and areas of the house instead of just an a ggregate 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 estima ting 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 incandes cent bulbs, in 20 years, would enjoy only a 14% market share. In addition, the cost estim ate per bulb fell from $11.50 to $3 (NPCC 2005). To summarize, the emergence of lighting as the largest conservati on opportunity within

the Power Council’s residential su pply curves (41% of residentia l potential) is due mainly to technological changes that increased the app licability, affordability, reliability, life, and availability of CFLs. These changes were driv en 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 we re unable to predict the trajectory of this technology and its timing, or at least were unable to ma ke a convincing enough case.



### Residential Clothes Washers in Existing Homes

Background

The evolution of energy savings from clot hes washers in reside ntial applications illustrates how savings can increase with product a nd market evolution, but also illustrates many of the complications with analyzing and forecas ting 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 clothe s washers nearly doubled, even ignoring efficiency that was not in cluded 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 wash er energy use decreases from 3.02 to 0.44 kWh/cycle, reflecting an 85% reduction. Includi ng the effects of dryer energy use, the total

8- 105 2008 ACEEE Summer Study on Energy Efficiency in Buildingsenergy 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” im proved 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 E fficient Clothes Washer Energy Usage

Plan (Year) Efficiency Level Washer Use (kWh/Cycle) Dryer Use 9
(kWh/cycle) Total (kWh/cycle) Change (from 1983 baseline)

III (1991) EF

Baseline 0.86 10 3.02 2.32 5.34

Most efficient 3.19 11 0.81 2.32 3.13 2.21

IV (1996) EF

Most efficient 3.25 12 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 Pla n, the Council estimate d the potential yearly savings based on savings from a cold rinse option which saves hot water en ergy. 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 Counc il 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. Th e higher speed spin cycles of horizontal axis washers can greatly reduce the remaining moisture content (RMC) of the cl othes before they are placed into the dryer, resulting in much le ss energy required to dry the clothes.

Methodology

The Council’s Fourth Power Plan was the firs t to include the eff ects of reduced dryer usage into the overall savings attributed to clot hes 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 in cludes both washer and dr yer use per cycle to provide for reasonable comparison. The inclus ion of RMC into the conservation resource calculation provides a more robust estimation, a nd reflects the true overall energy savings.

9 The savings attributed from reduced dryer usage are not in cluded 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 construc ted as the “maximum technologically feasible” washer possible given ex isting technology at the time.

12 The EF 3.25 also assumes a remaining moisture content of 40%.

8- 106 2008 ACEEE Summer Study on Energy Efficiency in BuildingsPrograms

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 Laun dry Metering and Market Analysis (THELMA) helped establish measurement me thods and identify efficient feat ures. 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 us ing consistent specific ations. As markets accepted higher efficiencies, they worked their wa y 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 fo r rebates were harmonized with ENERGY STAR efficiency levels. The effect of this accord was reduced confusion among utilities, manufacturers, retailers, and consumers by provi ding consistent estimates of savings to all parties (CEE 2001). The primary lesson from this market is th at program efforts can drive improvements in technology, testing procedures, an d market branding of efficienc y. Consistent, reliable and purposeful estimates of savings he lps 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 id entify a systematic trend or mathematical function that helps forecast s upply curves beyond establishe d technology. While savings potential grows and prices moderate over time fo r many measures, this is neither universal nor consistent between measures. The results may sugge st that rather than de veloping a better point estimate of efficiency potential by a certain year, analysts s hould consider the asymmetric uncertainty around efficiency savings estimates. Integrated resource plans could establish a confidence interval around conventionally derive d supply curve-driven savings estimates that acknowledges that there is likelihood of more savi ngs than of less. For example, a conventional supply curv e does not forecast zero net energy new buildings and homes, but there are several entiti es in the US committed to developing them and demonstration homes and buildings under constructi on. Zero net energy initiatives generally set a goal of 60-70% energy reduction through effi ciency and meeting a dditional needs through renewable energy. While this is not now proven f easible on a mass scale, forecasters can use this technology to create a “high ca se” for savings with a mode st probability, and assign progressively more certainty to more conservative estimates. Another approach is to analyze potential savings from near-mature measures, without pretension of pr ecision 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 efficienc y, this helps drive measurement improvements and technical innovation, resulting in la rger and more reliable conser vation supply estimates. Thus,

8- 107 2008 ACEEE Summer Study on Energy Efficiency in Buildingsit appears that the best way to improve conserva tion 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 De partment of Energy. 1990. Technical Support Document: Energy Conservation Standards for Consumer Produc ts: 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: Mark et Progress Evaluation Report #5. Portland, Ore. http://www.nwalliance.org/research/reports/83.pdf

[NEEA, 2002] Northwest Ener gy 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/re search/documents/AR2005.pdf

[NFRC, 2003] National Fenest ration 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/docum ents/NFRC%20History.pdf

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

Heat Loss. Silver Spring, MD

. www.nfrc.org/documents/U-Factor.pdf

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[NPPC, 1986] Northwest Powe r Planning Council. 1986. 1986 Northwest Power Plan. Portland, Ore.

[NPPC, 1991] Northwest Powe r 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.

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

Power and Conservation Plan. Portland, Ore.

[NPCC, 2007a] Northwest Power a nd Conservation Council. 2007. Council Briefing Book. Portland, Ore.

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