TECHNICAL MEMORANDUM

TO: Carla Frisch, U.S. Department of Energy, Office of Energy Policy and Systems Analysis

(DOE-EPSA)

FROM: Ian M. Hoffman and Ling Jin, Lawrence Berkeley National Laboratory (LBNL)

DATE: May 15, 2015

CC: Lisa C. Schwartz and Charles A. Goldman

SUBJECT: Energy Efficiency Portfolio and Program Lifetimes: Temporal Distribution of Electricity 

Savings

Summary

In a peer-reviewed brief funded by DOE-EPSA (Hoffman et al. 2015a), we describe and report energy 

savings lifetimes1 obtained from regulatory reports filed by administrators of electricity efficiency 

programs for customers of investor-owned utilities. DOE-EPSA requested additional information on

the lifetime and magnitude of electricity savings from these programs. We employed a statistical 

cluster analysis to identify logical cohorts of program savings lifetimes. We then quantified the energy 

saved within each lifetime cohort or tier for the period 2009 to 2013. The results thus represent the 

magnitude and timing of aggregated energy savings for program administrators in 25 states2 for the 

most recent period for which data are available.

We find that efficiency programs can be segregated by savings lifetimes into reasonably discrete ranges 

or tiers (based on the types of measures or other efficiency actions). Reported gross annual savings3 and 

lifetime savings can be summed for each of these discrete tiers as an estimate of the magnitude of 

savings over time. These tiers and their associated savings thus show the temporal distribution of the 

reported energy impacts of efficiency programs in more than half of states where substantial efficiency 

programs are funded by customers of investor-owned utilities.

 

1 Savings lifetime is the number of years of annualized savings that a given energy efficiency measure, program or portfolio (a

collection of programs targeting multiple sectors) is expected to deliver savings. A measure lifetime is defined as the mean 

number of years that the measure is in place, operable and delivering energy savings. Program, market sector and portfolio 

lifetimes are averages of the lifetimes of the measures installed for that program, market sector or portfolio.

2 States from which savings lifetimes could be obtained include AR, AZ, CA, FL, HI, IA, ID, IL, IN, MA, MD, ME, MN, NC, 

NM, NY, OH, OK, OR, PA, RI, SC, UT, VT, and WA.

3 Annual savings, also known as annual incremental savings, are the energy savings resulting from a given measure, program 

or portfolio in the first year after installation. First-year savings are typically annualized in most states—i.e., assumed to 

constitute a full year of energy savings regardless of when during the year the efficiency action was taken. Lifetime savings are 

annual savings summed over each year of the estimated lifetime of a given measure, program, market sector or program 

portfolio. 
Temporal Distribution of Electricity Savings

Carla Frisch, DOE EPSA

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Background 

In 2012, LBNL began collecting detailed data on energy efficiency programs funded by utility 

customers and reported in regulatory filings by the administrators of those programs. The LBNL 

Demand-Side Management (DSM) Program Database includes program-level spending, annual and 

lifetime net and gross energy savings by fuel, customer participation rates, total costs, cost breakdowns,

and, where available, average savings lifetimes of the measures installed or actions elicited by the 

programs. We characterize programs on multiple levels—by market sector, program implementation 

type and technology—according to a typology of more than 60 program types. LBNL developed the 

program typology through our review of hundreds of regulatory filings in 34 states (Hoffman et al. 

2013). Characterizing programs in a detailed fashion, with specified applications of reasonably defined 

sets of measures, allows better definition of savings lifetimes. Sorting by program type enables 

aggregation of program data across states and benchmarking of program results for individual 

program administrators and states.

LBNL researchers draw upon the database to calculate the cost of saving electricity through various 

types of efficiency programs (Billingsley et al. 2014, Hoffman et al. 2015b), among other uses. The 

lifetime of efficiency measure4 savings for each program—how many years, on average, the savings 

last—is an essential value in quantifying energy savings and avoided emissions, as well as assessing 

the resource potential and cost-effectiveness of saving energy through efficiency programs.

LBNL distributed a draft technical brief for external technical peer review in December 2014 on the 

state of knowledge of energy savings lifetimes (Hoffman et al. 2015a). In the brief, we reported ranges, 

medians and averages of program savings lifetimes from the LBNL DSM program database during the 

period 2009 to 2013.

DOE-EPSA requested more detail on the distribution of energy savings over the lifetimes of energy 

efficiency programs. Following is a description of our analysis.

Approach

LBNL collects program savings lifetimes reported directly by the energy efficiency program 

administrator, where available. As of March 2015, the LBNL Program Impacts database included ~6,000 

program years of data.5 However, program savings lifetimes are obtainable6 for only ~1,600 program 

 

4 Measures in the LBNL DSM program database include devices, equipment, processes and behaviors or actions promoted, 

incentivized or installed as part of utility customer-funded efficiency programs. Efficiency measures through other activities, 

such as building energy codes and end-use equipment standards, are not included.

5 LBNL counts the data points in its DSM program database in terms of program years. For example, a program administered 

and operated over three years would generate three program years of data.

6 Either directly reported by the program administrator or derived from lifetime savings reported by the program 

administrator.
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Carla Frisch, DOE EPSA

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years (27% of the total sample). The analysis described in this brief is based upon this subset of 1,600 

program years.7 More than 50 utilities and other energy efficiency program administrators in 25 states 

contributed to those data through their regulatory filings, statewide databases and other sources.

For the current analysis, LBNL calculated the savings-weighted average lifetime for each of the 

program types identified in our data collection. We then performed a cluster analysis to help determine 

logical divisions into cohorts or tiers of savings lifetimes within the program data. Cluster analysis is a 

commonly used statistical technique for grouping objects or values based on their similarity to one 

another versus other objects or values. 

A number of algorithms can be used to define clusters. The nature of the data and the interrelationships 

among the data points guide the selection of the algorithm. The defining quality for our clusters is the 

relationship between: 1) the lifetime for each program year and 2) the averages for each program type.

In essence, we grouped the 1,600 program-year data points by their proximity to the average for each 

type of energy efficiency program.

For our cluster analysis, we chose one of the most common methods, known as k-means (Anderberg 

1973). The variable k is the number of clusters. The method starts with assignment of each data point to 

a cluster so as to minimize the distance of cluster members from the center of the cluster, which is 

designated randomly from the start. The algorithm then readjusts the centers and cluster membership 

to achieve the least squared Euclidean distance within each cluster and summed across all clusters. In 

essence, the method seeks to minimize differences within each cluster and maximize differences among 

the clusters. In our case, programs within each cluster would therefore have similar savings lifetimes 

and program types.

The algorithm iterates until it converges on a local optimum. Because the local optimum may not result 

in a global optimum or produce stable, repeatable clusters, we randomly seeded the selection of the 

initial cluster centers, resulting in 50 random starts of the search for a global minimum in the distances 

of the members within each cluster from its center. We specified a number of clusters ranging from 

three to seven. 

We reviewed the cluster results for coherence in the vertical dimension—i.e., the degree to which each 

cluster appeared well defined across a certain range of years, with minimal overlap in lifetimes. Based 

upon this criterion, the program savings lifetimes grouped most cleanly into four clusters.

 

7 We elected not to use the entire dataset of 6,000 program years because savings lifetimes for about 70% of the data are 

developed based upon the average savings lifetimes for each type of program, as reported or derived for the subset of 1,600+ 

program years. Using the larger dataset, with lifetime savings based upon averages, would have skewed the range of savings 

lifetimes in the cluster analysis toward the average for each program type and thus also biased the estimated annual and 

lifetime savings for each tier of program savings lifetimes.
Temporal Distribution of Electricity Savings

Carla Frisch, DOE EPSA

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We summed the annual and lifetime energy savings for each program year in each cluster and 

calculated each cluster’s share of savings relative to total annual and lifetime savings for all program 

years with reported or calculated savings lifetimes.

Results

With the ~1,600 program years sorted among four clusters or tiers of savings lifetimes, the energy 

savings for each tier of program lifetimes can be depicted as a share of the total savings for the dataset

(Figures 1 and 2).

Figure 1. Annual electricity savings for each tier of program savings lifetimes, as a percentage of the total annual savings 

of all programs in the dataset

For annual energy savings across the 1,600 efficiency program years for which savings lifetimes can be 

obtained, about 67% of the annual savings comes from programs defined by measures lasting 11 years 

or less (Figure 1). This is consistent with the savings-weighted average lifetime of 10.2 years across all 

1,600 program years. Programs defined by measures lasting 19 years or more account for about 2% of 

overall annual energy savings in our dataset. If we consider the lifetime savings of programs in our 

portfolio, then about 53% of the savings comes from programs defined by measures lasting 11 years or 

less (Figure 2).

35%

32% 31%

2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

1 to 8 years, n=270 9 to 11 years, n=547 12 to 18 years, n=660 19+ years, n=130

Percent of Total Portfolio Annual 

Savings

Program Savings Lifetimes (years)
Temporal Distribution of Electricity Savings

Carla Frisch, DOE EPSA

Page 5 of 7

Figure 2. Percentage of total lifetime savings in the dataset for each tier of program savings lifetimes

The duration of the annual and lifetime savings in our national portfolio of programs can be depicted

in stepwise fashion, ratcheting down as the energy savings from each tier of programs comes to an end 

(Figure 3).

Figure 3. Temporal distribution of energy savings and savings-weighted average lifetime for each tier of program savings 

lifetimes

22%

31%

43%

4%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 to 8 years, n=270 9 to 11 years, n=547 12 to 18 years, n=660 19+ years, n=130

Percent of Total Portfolio Lifetime 

Savings
Temporal Distribution of Electricity Savings

Carla Frisch, DOE EPSA

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For the 25-state portfolio, 100% of the savings from the initial investment in the programs last from one 

to eight years (with a savings-weighted average lifetime of 6.5 years). About 65% of the savings

continue at least for the next one to three years (average lifetime of 9.6 years from the initial investment 

in the programs). Some 31% of the savings continue for another one to six years (average lifetime of 

14.2 years). A small fraction of the savings (2%) continues for as much as another 27 years for the 

longest-lived measures and programs (average lifetime of 21.2 years).

Distinct types of programs can be identified within each tier of program lifetimes and portfolio savings 

(Table 1). 

Table 1. Prevalent types of energy efficiency programs associated with each tier of program savings lifetimes

Tier of Program Savings Lifetime Prevalent Program Types 

Tier I

<= 8 years

Savings-weighted average lifetime: 6.5 years

• Residential/Commercial Lighting

• Residential Appliance Recycling

• Residential Behavioral Feedback

• Consumer Electronics

• Residential/Commercial HVAC TuneUps

• Commercial Retro-Commissioning

Tier II

9 to 11 years

Savings-weighted average lifetime: 9.6 years

• Agricultural Pumps

• Residential/Commercial Appliances

• Commercial Lighting

• Small Commercial Prescriptive 

• Residential Direct-Install Retrofits

• Multi-Family Retrofits

• Pool Pumps

• Street Lighting

• Voltage Optimization and Transformer 

Upgrades

Tier III

12 to 18 years

Savings-weighted average lifetime: 14.2 years

• Data Centers

• Refrigerated Storage

• Industrial Motors

• Industrial and Agricultural Process

• Commercial HVAC

• Small Commercial Retrofits

• MUSH8 Retrofits

 

8 Municipalities (and other government buildings), universities, k-12 schools and hospitals
Temporal Distribution of Electricity Savings

Carla Frisch, DOE EPSA

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• Residential Water Heaters

• Whole-Home Retrofits

• Low Income (Shell/System)

Tier IV

19+ years

Savings-weighted average lifetime: 21.2 years

• Residential New Construction

• Commercial New Construction

• Residential HVAC

• Residential Insulation

• Cool Roofs

This work demonstrates that efficiency programs for electric utility customers can be segregated into 

logical tiers based on similar program savings lifetimes. An estimate of the magnitude of energy 

savings over time can be developed from the gross annual and lifetime savings for each of these tiers, 

as reported by program administrators. These tiers and their associated savings thus reflect the

temporal distribution of the reported impacts of energy efficiency programs in 25 states. Programs 

defined primarily by measures with savings lifetimes of 11 years or less account for 67% of annual 

savings and 53% of lifetime savings across the 25-state sample. 

References

Anderberg, M.R. 1973. “Cluster Analysis for Applications. Monographs and Textbooks on Probability 

and Mathematical Statistics” Academic Press Inc., New York.

Billingsley, M.A., I.M. Hoffman, E. Stuart, S.R. Schiller, C.A. Goldman and K. Hamachi LaCommare

2014. “The Program Administrator Cost of Saved Energy for Utility Customer-Funded Energy 

Efficiency Programs.” Lawrence Berkeley National Laboratory. LBNL-6595E. April.

http://emp.lbl.gov/cost-saved-energy

Hoffman, I.M., M.A. Billingsley, S.R. Schiller, C.A. Goldman and E. Stuart 2013. “Energy Efficiency 

Program Typology and Data Metrics: Enabling Multi-State Analyses Through the Use of Common 

Terminology” LBNL-6370E. August. http://emp.lbl.gov/sites/all/files/lbnl-6370e.pdf 

Hoffman, I.M., S.R. Schiller, A. Todd, C.A. Goldman, L.C. Schwartz 2015a. “Energy Savings Lifetimes 

and Persistence: Practices, Issues and Data” LBNL. May. http://emp.lbl.gov/publications/energysavings-lifetimes

Hoffman, I.M., G. Rybka, G. Leventis, C.A. Goldman, L. Schwartz, M.A. Billingsley and S.R. Schiller 

2015b. “The Total Cost of Saving Electricity Through Utility Customer-Funded Energy Efficiency 

Programs: Estimates at the National, State, Sector and Program Level” LBNL. April. 

http://emp.lbl.gov/sites/all/files/total-cost-of-saved-energy.pdf