Measure Cost Study (WO17) –
Task 5 Report: Recommended
Data Collection Approaches
(Deemed Measures)
Final
Itron, Inc.
1111 Broadway, Suite 1800
Oakland, CA 94607
(510) 844-2800
August 23, 2012
Table of Contents
1 Introduction ........................................................................................................ 1-1
2 Recommended Data Collection and Development Approaches by Measure
Group ..................................................................................................................... 2-1
2.1 Retail Unit Prices ........................................................................................ 2-2
2.1.1 Large Sample of Actual Retail Price Observations .................................................... 2-2
2.1.2 Retail Price Build-up from Wholesale Prices ............................................................ 2-10
2.2 Installation Labor....................................................................................... 2-15
2.3 Other Measure Cost Data ......................................................................... 2-20
2.3.1 O&M Labor and Materials ........................................................................................ 2-20
2.3.2 RUL for ER Measures .............................................................................................. 2-22
2.3.3 Lifecycle Setup ......................................................................................................... 2-22
3 Appendix A – Attributes of NPD POS Data ...................................................... 3-1
4 Appendix B - Sample Extract of C4A Invoice Data .......................................... 4-1
5 Appendix C - Final Disposition of DNV KEMA’s Fall 2011 Shelf Surveys ..... 5-1
List of Figures
Figure 2-1: Number of Rebates Issued under California’s Cash for Appliances
Program (source: CEC) .................................................................................... 2-8
List of Tables
Table 2-1: Recommended Data Sources and Primary Data Collection Activities
to Support Development of Large Samples of Actual Retail Price
Observations by Measure Group ..................................................................... 2-4
Table 2-2: Recommended Data Sources and Primary Data Collection Activities
to Support Development of Built-up Retail Prices by Measure Group ............ 2-13
Table 2-3: Recommended Data Sources and Primary Data Collection Activities
to Support Development of Installation Labor Hours by Measure Group ....... 2-17
Table 2-4: Deemed Measures with Incremental O&M Costs................................ 2-21
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data ........... 3-2
Table 3-2: Equipment Attributes Available for NPD’s Electronics POS Data.......... 3-7
Itron, Inc.
i
Table of Contents
Table 4-1: Sample Extract of California Cash for Appliances Program Invoice
Database .......................................................................................................... 4-2
Table 5-1: Distribution of Completed Store Visits by Chain/Independent,
Participating/Non-Participating Stores, Channel, and IOU (source: DNV
KEMA) .............................................................................................................. 5-2
Table 5-2: Number of Total Advanced and Non-Advanced Lamps by Channel
and Detailed Lamp Type (source: DNV KEMA) ............................................... 5-3
Itron, Inc.
ii
Table of Contents
1
Introduction
This memorandum represents the primary deliverable for Task 5 of the Measure Cost Study
(Work Order ITRON017). As presented in the approved research plan, the primary objective of
Task 5 is to develop detailed data collection strategies and plans for each in-scope deemed
measure group. As described in the research plan, the primary deliverables associated with Task
5 are as follows:

A comprehensive memorandum presenting the preferred data collection strategies for all
in-scope measure groups, including but not limited to explicit identification of data to be
collected from subcontractors, data to be collected by Itron and/or KEMA staff, data to be
purchased, and data to be collected from secondary sources. The limitations and key
analysis issues associated with each preferred data collection strategy will be delineated
and previewed (draft and final)

Task orders for all data collection to be conducted by subcontractors, including written
scopes of work, budgets, and schedules (draft and final)
This memorandum is structured to satisfy the first of these two requirements. The second
requirement will be fulfilled by having the subcontractors prepare detailed budget estimates for
completing the recommended data collection approaches described in this memo. Each
subcontractor’s draft budget and scope will then be optimized to be consistent with the final
priority rankings established in Task 4. Revised subcontractor budgets and scopes will form the
basis of each final task order, which will be structured as phased budget authorizations linked to
a series of interim deliverables.
Itron, Inc.
1-1
Introduction
2
Recommended Data Collection and Development
Approaches by Measure Group
In this section, we present and describe the specific data collection and development approaches
for each in-scope deemed measure group that the study team believes will allow robust
estimation of ex ante incremental measure costs. These data collection and development
approaches are based on the study team’s assessment of the quality and applicability of costrelated data that can be readily and cost-effectively acquired by Itron and/or its subcontractors.
These data collection and development approaches were also designed to meet the data
collection and analysis objectives delineated previously in the Task 4 memorandum, which
themselves are based on a detailed review and assessment of the data sources and methods
underlying the current set of incremental cost estimates in DEER and the IOU workpapers.
These objectives are:

Use substantially larger sample sizes from highly representative sample frames

Increase use and improve specification of regression-based cost models

Use systematic, independent validation of results

Incorporate anticipated interactions with future codes, standards, and labeling programs

Develop additional lifecycle cost data

Streamline data acquisition and development for future updates
In the remainder of this section, we present and describe each of the recommended data
collection activities and sources in detail. We describe the strengths, limitations, and key analysis
issues associated with each preferred data collection strategy and identify the activities to be
conducted by specialized subcontractors and those to be conducted by Itron.
It is important to note that the recommended data collection activities described below will be
designed to allow estimation of both full measure cost and incremental measure cost relative to
either a code-defined baseline or a market average baseline. The proposed data collection
activities for each measure group have been designed around the types of analysis we believe
will lead to the most robust cost estimates based on factors such as delivery channel and the
various cost influencing factors identified in Task 4. The primary analysis methods will be
described at a high level in the sections that follow. Analysis methodologies specific to each
Itron, Inc.
2-1
Recommended Data Collection
Measure Cost Study - Task 5 Report
measure group will be developed and tested in parallel to data collection activities.
Recommended cost cases and incremental cost analysis approaches for each measure group will
be defined in the deliverable for Task 7 in close coordination with the DEER team.
2.1 Retail Unit Prices
To develop estimates of the average retail unit price for in-scope deemed measures, we
recommend two general approaches to data collection and development. For mass market
measures that are primarily sold directly to final consumers through retail channels, we
recommend collecting and developing large samples of actual retail price observations at the
point of sale. For measures that are procured and sold to consumers primarily or exclusively via
contractors, we recommend using a “retail price build-up” approach where unit price data is
collected at the wholesale level and supplemented by explicit estimation of bulk purchase
discounts, contractor mark-ups, warranties, and other factors that determine the average retail
price faced by final consumers.
Within these two general approaches to data collection and development, we identify and
describe the specific data sources and data collection approaches that the study team
recommends leveraging for each in-scope deemed measure group in the subsections below. 1
2.1.1 Large Sample of Actual Retail Price Observations
For mass market measures that are primarily sold directly to final consumers through retail
channels, we recommend collecting and developing large samples of actual retail price
observations at the point of sale. 2 Collecting and developing such samples allows incremental
costs due to efficiency to be estimated using regression-based cost modeling (also known as
hedonic price modeling). As discussed previously in the Task 4 memorandum, regression-based
cost modeling has many attributes that make it highly appealing for incremental cost estimation.
First and foremost, it allows incremental cost estimates to be explicitly controlled for costinfluencing factors that are not related to efficiency performance. Second, these models allow
incremental costs to be estimated across a continuum of technology specifications and are thus
1
Note that there are a few measures which are strictly services, and as such their incremental costs are entirely the
labor costs associated with the service and do not include any retail technologies or materials. Specifically, the
in-scope deemed measures that are strictly services are: refrigerator recycling, freezer recycling, room air
conditioner recycling, AC coil cleaning, delamping, and BMS programming.
2
In support federal appliance standards rulemakings, the USDOE uses a “manufacturing cost” approach to
estimate incremental costs due to efficiency improvements for mass market technologies. This approach involves
isolating the specific equipment components that determine efficiency performance and using engineering
analysis to estimate the incremental production costs of those components. This engineering analysis is then
followed by a retail markup analysis to arrive at average retail price. This approach is well-developed and has
proven to be acceptable to the USDOE’s stakeholders but is also time and cost-intensive. In this respect, it is
largely impractical and well beyond the resources of this study.
Itron, Inc.
2-2
Recommended Data Collection
Measure Cost Study - Task 5 Report
inherently flexible and applicable to both program-level and measure-level planning activities.
Third, and perhaps most importantly, regression-based cost models allow for the explicit
quantification of the uncertainty associated with the result for each independent variable, which
is strictly not possible when using weighted or simple averages. In addition to the advantages
provided by applying regression-based cost modeling to large samples of actual retail price
observations, large samples of “point of sales” (POS) data for some measures can be readily
purchased from third party marketing firms (e.g. ACNeilsen, Activant, NPD) which makes it
possible to conduct regular, targeted updates for those measures. Indeed, large POS samples have
been used as the primary datasets in nearly all of the Residential Market Share Tracking (RMST)
studies sponsored by the CPUC and the IOUs since 2000.
Conversely, there are several challenges associated with the approach of using large samples of
actual retail price observations. The first of these challenges is that the price data must be recent
enough to be relevant to the analysis, i.e. data collection is somewhat constrained to prices paid
in the most recent 1-2 years. This is especially true for technologies that change rapidly, such as
TVs and computer displays. Second, the use of large samples of actual retail prices often needs
to be complemented by sales volume (or relative sale volume) data in order to properly account
for any significant price differences observed across different retail channels. Finally, such data
sets often need to be corrected for seasonal pricing (e.g. holiday sales), inflation, and changes in
producer prices (e.g. for labor and commodities). The first two challenges described above are
essentially additional data collection requirements. The last challenge described is primarily an
analytic challenge for which econometric methods have already been developed and applied in a
wide variety of contexts.
The study team has identified several viable data sources that can be readily leveraged to develop
large samples of actual retail price observations for specific measures, as well as a set of primary
data collection activities that can be used to develop such samples and/or validate results. Table
2-1 shows the specific measure groups for which the study team recommends developing large
samples of actual retail price observations and identifies the specific data sources and primary
data collection activities recommended for each such measure group.
Itron, Inc.
2-3
Recommended Data Collection
Measure Cost Study - Task 5 Report
Table 2-1: Recommended Data Sources and Primary Data Collection Activities to Support Development of Large Samples
of Actual Retail Price Observations by Measure Group
Sector
End Use
Tech Group
Measure Group
Residential
Residential
Residential
Lighting
Lighting
Lighting
Interior lighting
Interior lighting
Exterior lighting
CFL lamps
CFL fixtures
CFL lamps
Residential
Lighting
Exterior lighting
CFL fixtures
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
C&I
C&I
C&I
Residential
Residential
Appliances
Appliances
Appliances
Appliances
Appliances
Electronics
Electronics
Electronics
Electronics
Lighting
Lighting
Lighting
Water Heating
Water Heating
Water Heating
Water Heating
Water Heating
Electronics
Electronics
Laundry
Kitchen
Cold storage
Cold storage
Laundry
Office
Office
Office
Other plug load
Interior lighting
Controls
Controls
HW distribution
HW distribution
Liquid circulation
Liquid circulation
Liquid circulation
Office
Other plug load
Clothes washers
Dishwashers
Refrigerators
Freezers
Clothes dryers
Copier
Desktop computers
Monitors
Televisions
LED
Photocell
Occupancy sensors
Pipe insulation
Faucet aerator
Pipe insulation
Faucet aerators
Low-flow showerheads
PC power management
Plug load sensors
Itron, Inc.
2-4
Data Sources
Data Validation
Approach
IOU program data,
POS data
(ACNeilsen/Activant),
Retail shelf surveys,
Supplier interviews
Web price search
CA SEEARP invoice data
POS data (NPD)
Retail shelf surveys
Retail shelf surveys
Web price search
Web price search
none
Recommended Data Collection
Measure Cost Study - Task 5 Report
In the subsections below, we describe each of the recommended data sources and primary data
collection activities shown in Table 2-1 in more detail and the rationale for the study team’s
recommendations.
Note that the first three data sources described below are specific to interior and exterior CFLs.
In these particular cases, there are multiple sources of large volumes of actual retail price
observations. However, from Itron’s previous Residential Market Share Tracking Studies
(RMST), it is well established that retail prices for CFLs vary significantly across retail channels
for identical products. In order to develop robust estimates of the average incremental cost of
CFLs, it is therefore necessary to also acquire comprehensive data on relative sales volumes
across all retail channels. Unfortunately, there is no single data source that has comprehensive
sales data for CFLs from each retail channel. 3 In this sense, developing retail channel sales
weights for CFLs requires piecing together best available information from several sources. The
recommended data sources and data collection activities for CFLs described below were
developed by DNV KEMA on behalf of the Itron study team in order to leverage their related
data collection activities and analysis currently being conducted for WO28, as well as their
previous data collection and development activities conducted for their evaluations of IOU
residential lighting programs in the 2004-2005 and 2006-2008 program cycles.
IOU program data
The IOUs provide information on all products discounted through the current upstream lighting
program. This information includes model number, manufacturer, retailer, product style, wattage
and lumens, and rebate and pricing information. The study team (via DNV KEMA) has direct
access to program tracking data from as far back as 2004. These data will support the
development of retail channel weights for interior and exterior CFLs.
POS data (ACNeilsen, Activant)
Since the late 1990s, Itron has evaluated, procured, and analyzed POS data from various third
party marketing firms as the primary datasets to support near annual updates to the RMST
studies for lighting and appliances. For the current portfolio of EM&V studies, Itron plans to
purchase similar datasets for 2010 and 2011 as part of the current update to the RMST studies
under WO23 (which will also be leveraged for analyses being conducted in WO28). Specifically,
Itron plans to purchase a POS dataset for CFLs from ACNeilsen that covers the food store, drug
store, and mass merchandiser retail channels, and an analogous POS dataset from Activant that
covers hardware stores – which collectively account for all of the major retail channels for CFLs
with the exception of national chain home improvement centers (such as Home Depot and
Lowe’s) and small “mom and pop” grocery stores.
3
As noted in Itron’s 2007 RMST Lamp Report, national chain home improvement stores stopped providing their
POS data to third party marketing firms starting in 2003.
Itron, Inc.
2-5
Recommended Data Collection
Measure Cost Study - Task 5 Report
In past RMST efforts, Itron has attempted to identify products discounted through the IOU
upstream lighting programs. However, there is no flag in the ACNeilsen and Activant POS
databases that isolates within-program versus outside-program sales. In the context of the MCS
study, the ACNeilsen and Activant POS data will contribute to the development of retail channel
weights for interior and exterior CFLs.
Supplier interviews
DNV KEMA has conducted hundreds of in-depth interviews with lighting suppliers, i.e.
manufacturers, lighting buyers, sales representatives from national chain retailers, and retail store
managers. The results from these interviews have been used since 2004 to estimate total CFL
sales by retail channel, including estimates from within and outside of IOU programs. The
supplier interview data provide a good high-level understanding of retail channel weights for
interior and exterior CFLs and will supplement the IOU program and POS datasets.
POS data (NPD)
The NPD Group collects POS data for a large panel of major appliance and electronics retailers
that cover roughly 80% of those respective retail markets. 4 Included in these POS datasets are
nearly all of the appliance and consumer electronics measures in the scope of this MCS update.
Specifically, the NPD POS data include clothes washers, dish washers, refrigerators, freezers,
room air conditioners, clothes dryers, televisions, desktop computers, monitors, and copiers. In
addition to brand, model number, unit price, and relative unit sales for all models sold by their
partner retailers, the NPD POS data also include fields for an extensive set of variables that are
critical to developing robust cost models and isolating price differences due strictly to efficiency
performance. For example, in the case of refrigerators, each NPD POS record includes fields for
color, depth, width, height, capacity (volume), door dispenser options, icemaker options,
ENERGY STAR qualification, rated annual kWh, number of doors, and door type and
configuration. A full list of the product attribute variables available in NPD’s POS data for
appliances and electronics measures are provided in Appendix A.
Discussions with NPD revealed that, due to NPD’s confidentiality agreements with their
appliance retail partners, the unit price and relative sales volume data for major appliances that
can be delivered to Itron would be national averages, not California-specific averages. However,
NPD’s confidentiality constraints do not restrict it from weighting the national averages by the
4
NPD’s POS data for appliances includes the following retailers: Abt TV & Appliances, amazon.com, American
TV, Bernies, Best Buy, BJ’s Wholesale Club, Bloomingdale’s, Boscov’s, Dillard’s, Fred Meyer, HH Gregg,
JCPenney, Kmart, Kohl’s, Lowe’s, Macy’s, Meijer, Nebraska Furniture Mart, PC Richard & Sons, Pamida, RC
Wiley, Sears, Shopko, Target, Ultimate Electronics. NPD’s POS data for electronics includes the following
retailers: AAFES, ABT TV & Appliances, amazon.com, Apple Store, Best Buy, BJ’s Wholesale Club, Dell,
NewEgg.com, Office Max, Office Depot, PC Richards, Staples, RadioShack, Ritz Camera, Sears, Target
Itron, Inc.
2-6
Recommended Data Collection
Measure Cost Study - Task 5 Report
actual CA-specific mix of retailers. It is also possible to apply a CA-specific CPI or other
appropriate index to further weight the national averages to account for CA-specific inflation and
other macroeconomic factors that influence consumer prices for appliances in California. NPD
has indicated that they have not observed CA-specific unit price differences for appliances and
electronics that cannot be explained by CA macroeconomic factors. This approach is analogous
to the procedure used in previous RMST efforts where the original POS dataset was composed of
national or regional averages and then adjusted using CA-specific corrections for retailer mix
(and their relative sales volumes).
For the electronics POS data, NPD does not face the same confidentiality constraints and can
provide POS to Itron specifically for California retailers. The electronics POS data is also
available for each of the eight individual Designated Market Areas (DMAs) that NPD tracks in
California. It should be noted that, in coordination with WO34 (BCE), KEMA has already
acquired NPD’s complete POS dataset for televisions, so the remaining potential data purchase
would only need to include desktop computers, monitors, and copiers.
CA SEEARP data
As part of the USDOE’s State Energy Efficiency Appliance Rebate Program (SEEARP), the
CEC established the California Cash for Appliances (C4A) program that launched on April 22,
2010 and closed on December 31, 2011. Under this program, roughly 178,000 rebates were
offered to customers to replace existing, inefficient equipment with new ENERGY STAR
qualified units, including a host of technologies in the scope of the MCS update, including
clothes washers, dishwashers, refrigerators, freezers, room air conditioners, gas furnaces, central
heat pumps, central air conditioners, gas storage water heaters, solar thermal water heaters, and
heat pump water heaters. The CEC recently released the final number of rebates issued under the
C4A program, which are summarized by technology in Figure 2-1 below.
Itron, Inc.
2-7
Recommended Data Collection
Measure Cost Study - Task 5 Report
Figure 2-1: Number of Rebates Issued under California’s Cash for Appliances Program
(source: CEC)
Through the RFQ process for this study (Task 2), D&R International informed the Itron study
team of the forthcoming availability of the invoice data from the C4A program as well as other
SEEARP programs (for which D&R is one of several firms evaluating the program impacts at a
national level). The Itron team then inquired directly with the CEC’s program manager and
consequently acquired the entire C4A invoice dataset from the CEC. Each invoice record
includes brand, model number, purchase price, rebate amount, date of purchase, zip code of
purchase/installation, as well as information on the replaced equipment. A sample extract of the
C4A invoice database is provided in Appendix B. As Figure 2-1 shows, this data set contains
roughly 178,000 records and thus represents a very large sample of actual retail price
observations. Additionally, since the dataset contains model numbers for each line item, it can be
easily expanded to include the equipment characteristics (e.g. capacity and efficiency rating)
required to conduct robust hedonic price modeling.
However, due to the C4A program design, this dataset does not contain any retail price data for
baseline efficiency units. Therefore, the C4A invoice dataset cannot be used as the exclusive
primary data source on which to develop incremental cost estimates. That said, the C4A data still
have significant value as a potential supplemental primary data source or, at a minimum, an
important data source for validating NPD’s POS data for appliances. 5
5
Unfortunately, the some of C4A data for HVAC and water heating equipment include installation labor and
delivery charges (aggregated with retail unit prices), and these records cannot be parsed out from those that do
not. Therefore, the C4A data for HVAC and water heating equipment cannot be used to validate the study team’s
estimates of full or incremental measure cost. This is not the case with the C4A data for appliances, where the
invoices only include retail unit prices.
Itron, Inc.
2-8
Recommended Data Collection
Measure Cost Study - Task 5 Report
Retail shelf surveys
One of the most direct ways to collect and develop large samples of retail price observations for
mass market goods is to conduct in-store retail shelf surveys, i.e. visits to a statistically
significant sample of retail stores to collect information on the specific products stocked in those
stores. This type of data collection has been a central part of previous MCS efforts in California
as well as other CPUC-funded market characterization and baseline studies. Indeed, in the
specific case of upstream lighting measures, DNV KEMA has conducted four different waves of
retail shelf surveys for lighting products since 2008 and recently completed another large shelf
survey of retail lighting stores in November 2011 as part of WO13 and WO28. The final
disposition of stores surveyed and unit prices observed is provided in Appendix C. The Itron
study team intends to integrate DNV KEMA’s most recent shelf survey data as a key supplement
to the Activant/Nelisen POS data for CFLs and the IOU program tracking databases.
One of the main challenges associated with using this data collection approach, however, is that
it does not provide information on relative sales volumes across competing brands and
manufacturers. This can be problematic when observed retail prices vary significantly within a
certain product class, as it leads to large standard deviations for the estimated variable
(incremental cost). In this sense, it is often not ideal to rely exclusively on retail shelf survey data
for incremental measure cost analysis. Conversely, retail shelf surveys can be an effective and
appropriate primary data collection approach when positioned as a way to strategically
supplement other primary data collection activities or to validate measure costs estimated using
other data sources.
For this MCS update, we recommend developing and implementing retail shelf surveys primarily
as a means to validate NPD’s POS data for appliances and electronics. Validation through shelf
surveys would both verify that the use of national averages (with California-specific retailer
weights and CPI corrections) produces robust estimates of California-specific retail prices and
ensure that NPD’s POS data is representative of the prices at retailers known to be outside their
sample. 6 We also recommend using retail shelf surveys as the primary data collection approach
for a few specific residential measures, namely residential outdoor lighting controls (photocells
and motion sensors), faucet aerators, low-flow showerheads, and hot water pipe insulation. For
these specific measures, initial investigations suggest either limited variance in retail prices
across brands (faucet aerators, pipe insulation) and/or a relatively limited number of competing
products in the market (photocells and motion sensors) – situations where the main challenges
associated with relying on retail shelf surveys as the primary data source are largely mitigated.
6
NPD claims that their POS data covers > 85 percent of all appliance/electronics retailers, but the sample does not
include a limited number major retailers such as Wal-mart and Home Depot.
Itron, Inc.
2-9
Recommended Data Collection
Measure Cost Study - Task 5 Report
Web price search
Another direct and low-cost way to collect and develop large samples of retail price observations
for mass market goods is to conduct online price searches, either using “web crawler” routines or
leveraging the many publically-available search engines. As with retail shelf surveys, one of the
main challenges associated with using this data collection approach is that it does not provide
information on relative sales volumes across competing brands and manufacturers. In this sense,
it is often not ideal to rely exclusively on web-based price searches for incremental measure cost
analysis. Conversely, web price searches can be an effective and appropriate primary data
collection approach when positioned as a way to validate measure costs estimated using other
data sources or for measures where the primary retail channel is in fact online retailers (as
opposed to “brick and mortar” stores) and competing product offerings are limited.
For this MCS update, we recommend using web price searches to validate the final incremental
cost estimates for CFLs and the retail shelf survey data and resulting cost estimates for
residential outdoor lighting controls (photocells and motion sensors), faucet aerators, low-flow
showerheads, and hot water pipe insulation. 7 We also recommend using web price searches as
the primary data collection approach for two specific nonresidential measures – plug load
sensors and network power management software. For plug load sensors (sometimes called
smart plug strips), the commercial product offerings are currently very limited, and it is unclear
whether a retail shelf survey approach would successfully capture all products currently available
in the market. 8 For network power management, the number of commercial product offerings is
also very limited and distribution appears to be either direct from the software provider or via
online retailers. 9 Web-based data collection will be most effective for these measures if the
IOUs can provide detailed information on the specific products being incentivized.
2.1.2 Retail Price Build-up from Wholesale Prices
For measures that are procured and sold to consumers primarily or exclusively via contractors,
we recommend using a “retail price build-up” approach where unit price data is collected at the
wholesale level and supplemented by explicit estimation of bulk purchase discounts, contractor
mark-ups, warranties, and other factors that determine the average retail price faced by final
consumers. This approach closely mirrors the equipment and project pricing practices used by
7
Note that this validation exercise is not meant to be a large-scale endeavor but a low-cost, systematic means by
which to independently verify that the cost models are calibrated accurately.
8
See the most recent market assessment for plug load sensors conducted by Ecova (formerly Ecos) for a list of
known products commercially available in the U.S.: http://efficientproducts.org/reports/smartplugstrip/EcosSmart-Plug-Strips-DRAFT-Jul2009-v2x.pdf
9
The EPA maintains a list of commercially available network power management software packages in addition
to its own free EZ GPO software tool in support of its ENERGY STAR Low Carbon IT campaign:
http://www.energystar.gov/index.cfm?c=power_mgt.pr_power_mgt_comm_packages
Itron, Inc.
2-10
Recommended Data Collection
Measure Cost Study - Task 5 Report
contractors, energy service companies, and program implementers who procure and install
energy efficiency measures on behalf of customers.
There are several important advantages to using a “retail price build-up” approach. First, because
the population of wholesale equipment distributors is relatively small, the sample sizes required
to form a representative share of the total market are also small, especially compared to the
sample sizes required when sampling the population of final customers. Second, for measures
typically procured and installed by third parties, the wholesale markets are highly competitive
(e.g. packaged DX) and unit wholesale prices tend not to vary significantly across manufacturers
for similar products, thus reducing and sometimes eliminating the need to acquire data on
relative sales volumes in order to develop weighted average wholesale prices. Similarly, some
wholesale markets have a very limited number of manufacturers (e.g. remote refrigeration),
which also serves to reduce the complexity and data required to develop weighted average
wholesale prices. Finally, and perhaps most importantly, this approach allows retail price
estimates to be explicitly controlled for high volume vs. low volume purchasing practices,
variation in contractor markup percentages, warranties, and other pricing factors which can
influence variations in final retail unit prices as much or more than variations in wholesale unit
prices.
Of course, there are also several challenges associated with the approach of building up retail
unit price estimates from wholesale prices. The first of these challenges is that this approach
requires access to multiple distributor price lists, which are not often readily available and shared
with the general public. Second, actual bulk purchase discounts and contractor markup
percentages are highly variable and depend on distributor inventories and the specific
relationship between a given distributor and contractor. Addressing this challenge requires
leveraging a large volume of actual project records to develop robust averages. A third challenge
is trying to account for both recent and historical changes in wholesale equipment prices. While
there have recently been large fluctuations in many commodity prices (e.g. copper), initial
discussions with the subcontractors have indicated that wholesale unit prices for equipment
change much more slowly than other project material costs like those for piping, ducting, and
wiring. Because these types of material costs cancel in an incremental cost analysis, the largest
potential source of price fluctuation is obviated.
The study team has identified five firms that regularly specify, procure, and install energy
efficiency technologies on behalf of customers and have ready access to multiple distributor
price lists for the measures of interest. These firms also have archives of a large number of actual
project cost records. Additionally, the study team has identified publically-available raw data and
a set of primary data collection activities that can be used to supplement the wholesale unit price
data and/or validate results. Table 2-2 shows the specific measure groups for which the study
Itron, Inc.
2-11
Recommended Data Collection
Measure Cost Study - Task 5 Report
team recommends using a “retail price build-up” approach and identifies the specific data
sources and primary data collection activities recommended for each such measure group. 10
10
While we are aware that the IOUs have extensive archives of rebate invoices for many of these measure groups,
those invoices only provide unit price data for high-efficiency technologies, not baseline-efficiency technologies.
With the exception of add-on measures like HVAC QM, we feel it is important to obtain both baseline and
efficient case measure costs from a single source in order to establish the most robust incremental cost models.
That said, IOU data may be appropriately used for data validation purposes.
Itron, Inc.
2-12
Recommended Data Collection
Measure Cost Study - Task 5 Report
Table 2-2: Recommended Data Sources and Primary Data Collection Activities to Support Development of Built-up Retail
Prices by Measure Group
Sector
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
Residential
C&I
C&I
C&I
C&I
End Use
HVAC
HVAC
HVAC
HVAC
Water Heating
HVAC
HVAC
HVAC
HVAC
HVAC
Building Shell
Building Shell
Building Shell
Building Shell
Water Heating
Water Heating
Water Heating
Pool
Lighting
HVAC
Pool
Building Shell
Tech Group
DX
DX
Space heating
Space heating
Water heaters
Air distribution
Air distribution
Air distribution
Air distribution
Controls
Insulation
Insulation
Roof
Windows
Water heaters
Water heaters
Controls
Pump
All
All
All
All
C&I
Water Heating
All
C&I
C&I
C&I
Process
Food Service
Refrigeration
All
All
All
Itron, Inc.
Measure Group
Split CACs
Split HPs
Furnaces
Gas boiler
Heat Pump WHs
Whole house fans
Fan VSDs
Fan motors
Air filter alarm controls
Programmable thermostat
Batt insulation
Blow-in insulation
Cool roof
Windows
Storage WHs
Tankless WHs
Water heating controller
Pool pump
All but CFL lamps, fixtures
All but QM
All
All
All but pipe insulation, faucet
aerators, low-flow showerheads
All
All
All
2-13
Data Sources
Data Validation Approach
IOU invoice data
DEG (via AEP affiliate)
Actual project records, IOU invoice
data, and/or representative bids
EMCOR, QuEST
ERS
VACOM
Recommended Data Collection
Measure Cost Study - Task 5 Report
As Table 2-2 shows, the study team recommends leveraging the market position and expertise of
five specific firms – Davis Energy Group (DEG), EMCOR Energy Services (EMCOR),
Quantum Energy Services & Technologies (QuEST), Energy & Resource Solutions (ERS), and
VaCom Technologies (VACOM). These five firms were selected as a result of a Request for
Qualifications (RFQ) that was developed and released by the study team under Task 2 of the
approved research plan. 11 In all, 18 firms responded to the RFQ, and the study team vetted each
firms’ stated qualifications and conducted multiple follow-up calls to determine each firm’s
specific areas of expertise, including but not limited to: established in-house cost estimation
resources, processes, and expertise; access to distributor price lists; accessible archives of actual
project records involving measures in the scope of this MCS update; and established
relationships with a large network of contractors.
Each firm’s qualifications were then matched to the areas of greatest need within the specific
measure scope and objectives of the MCS update. This process yielded the following specific
recommendations: using DEG to support residential HVAC, water heating, and building shell
measures; using EMCOR and QuEST to support nonresidential lighting, HVAC, water heating,
and building shell measures; using ERS to support commercial food service and industrial
process measures; and using VACOM to support commercial refrigeration measures. Each of
these firms has extensive knowledge of the respective energy technology markets and established
relationships with equipment manufacturers and vendors that can be leveraged to acquire
multiple distributor price lists and develop a representative sample of wholesale unit prices for
those respective measure groups. Note that due to the breadth and diversity of the nonresidential
lighting, HVAC, water heating, and building shell measures in the scope of this MCS update, the
study team recommends leveraging both EMCOR and QuEST’s relationships with distributors
and equipment vendors in order to ensure that an adequate number of distributor price lists can
be acquired for those specific measure groups to support robust estimates of average wholesale
unit prices.
To develop estimates of average contractor markups, the study team recommends leveraging
each firm’s relationships with contractors and builders to solicit a representative sample of
itemized project bids (i.e. with equipment separate from labor) for clearly defined prototypical
retrofit projects involving the specific measures of interest. By using the wholesale unit price
estimates derived from the distributor price lists, the study team will then be able to explicitly
back out the contractor markups for each type of equipment included in each bid. 12
11
The final RFQ is available at: http://eega.cpuc.ca.gov/Docs/REQUEST%20FOR%20QUALIFICATIONS%20%20Final_9.19.11.docx
12
Under this approach, warranties and other markups besides contractor profit markups would be included in the
estimate.
Itron, Inc.
2-14
Recommended Data Collection
Measure Cost Study - Task 5 Report
To validate the final built-up retail unit price estimates, the study team recommends leveraging a
combination of the C4A invoice data (for central air conditioners, air-source heat pumps,
furnaces, and heat pump water heaters), each firm’s records of completed projects, and/or IOU
current project records involving the specific measures of interest. In cases where the available
project records are not sufficient or comprehensive enough to support validation for a particular
measure, we recommend leveraging the appropriate firm’s relationships with contractors and
builders to solicit a sample of representative project bids (separate from the exercise described
previously) as a means of validating the built-up retail unit price estimates.
2.2 Installation Labor
For deemed measures that are installed directly by the final customer, estimating full measure
costs only requires estimating average retail unit prices. Examples of such measures include
residential lighting, appliances, and electronics, as well as some residential water heating
measures such as faucet aerators and low-flow showerheads. However, for deemed measures that
are installed by third parties (e.g. contractors) on behalf of final customers, estimating full
measure costs requires estimating average installation labor costs in addition to average retail
unit prices.
For this MCS update, the study team recommends using RS Means Cost Data publications as the
primary source for average installation labor rates ($/hr) associated with different types of
retrofit projects. The RS Means publications are a recognized source for developing construction
cost estimates and a common benchmark used by contractors, developers, and builders. In the
context of this study, the primary advantages of using the labor rates published by RS Means are
that the labor rates are internally consistent (thereby reducing systematic bias in labor cost
estimates), easily customizable to specific regions and locations via application of RS Means’
city cost indices and location cost factors, and consistent with the labor cost estimation
procedures used by many contractors and implementers. IOU invoice histories will be used as a
means of validating RS Means labor rate information.
However, the RS Means estimates of the installation labor hours required for construction and
retrofit projects are often too generic to be reasonably representative of energy efficiency
interventions. For example, while RS Means provides labor hour estimates for the installation of
various types of cooling systems, it does not provide labor hour estimates for several types of
HVAC control measures (e.g. demand-control ventilation) and indeed all of the lighting and
HVAC maintenance measures in the scope of this MCS update.
The study team therefore proposes developing original estimates of average installation labor
hours for each deemed measure in the scope of the MCS update that is typically installed by a
Itron, Inc.
2-15
Recommended Data Collection
Measure Cost Study - Task 5 Report
third party. Table 2-3 provides an overview of the data sources and data collection and validation
approaches proposed by the study team for each in-scope deemed measure group.
Itron, Inc.
2-16
Recommended Data Collection
Measure Cost Study - Task 5 Report
Table 2-3: Recommended Data Sources and Primary Data Collection Activities to Support Development of Installation
Labor Hours by Measure Group
Sector
End Use
Tech Group
Measure Group
Residential
Lighting
All
All
Residential
Appliances
All
All but recycling
Residential
Electronics
All
All
Residential
Water Heating
HW distribution
All
C&I
C&I
Residential
Water Heating
Water Heating
HVAC
Liquid circulation
Liquid circulation
All
Faucet aerators
Low-flow showerheads
All
Residential
Water Heating
Water heaters
All
Residential
Water Heating
Controls
All
Residential
Building Shell
All
All
Residential
Pool
Pump
Pool pump
C&I
Lighting
All
All but CFL lamps, fixtures
C&I
HVAC
All
All
C&I
Pool
All
All
C&I
Building Shell
All
All
C&I
Water Heating
All
All but pipe insulation, faucet
aerators, low-flow
showerheads
C&I
Process
All
All
C&I
Food Service
All
All
C&I
Refrigeration
All
All
Itron, Inc.
Data Sources
Data Validation
Approach
N/A
N/A
CATI-based survey of IOUapproved contractors
DEG project records
and/or representative
bids; IOU invoices
EMCOR/QuEST project
records and/or
representative bids;
IOU invoices
In-depth interviews
w/contractors
ERS project records
and/or representative
bids; IOU invoices
VACOM project records
and/or representative
bids; IOU invoices
2-17
Recommended Data Collection
Measure Cost Study - Task 5 Report
Below we describe each of the recommended data sources and primary data collection activities
shown in Table 2-3 in more detail and the rationale for the study team’s recommendations.
For residential HVAC, water heating, pool pumps, and building shell measures, as well as
nonresidential lighting measures, the study team recommends conducting telephone surveys with
large samples of the respective contractor populations in California using Itron’s ComputerAided Telephone Interview (CATI) survey center. Due to the relative homogeneity of new
HVAC, water heating, pool pumps, and building shell equipment and materials installed in the
residential sector (especially compared to those installed in the commercial and industrial
sectors), the study team believes that it is possible to construct a representative set of site
conditions and projects that can be effectively communicated in a CATI survey format in order
to solicit installation labor hour estimates. For most nonresidential lighting measures (e.g.
general service linear fluorescent lamp/ballast upgrades and fixture replacements), the study
team also believes that it is possible to construct a representative set of site conditions and
projects that can be effectively communicated in a CATI survey format. We acknowledge,
however, that the site conditions for certain types of nonresidential lighting projects (e.g. HID
fixtures and high-bay lighting applications) are too heterogeneous to be reasonably represented
and communicated in a CATI survey format. For those types of nonresidential lighting measures,
the study team recommends using in-depth interviews with contractors (described further below).
The sample frames for the CATI surveys will be developed using a combination of the approved
contractor lists maintained by each of the IOUs and the list of contractors that have submitted
relevant incentive applications for the current program cycle (through Q4 2011) as shown in the
SPT. The study team will leverage the expertise and experience of DEG (for residential HVAC,
water heating, pool pumps, and building shell measures) and QuEST and EMCOR (for
nonresidential lighting measures) to ensure that the prototypical site conditions and project types
are defined in terms and metrics appropriate for each respective contractor population and
consistent with the manner in which actual projects are described and defined when contractors
produce cost estimates for competitive bids.
For all other nonresidential measures, the study team recommends conducting in-depth
interviews with contractors. Due to the diversity of equipment specifications and site conditions
involved in these types of efficiency projects, it is difficult if not impossible to construct a
representative set of site conditions and projects that can be effectively communicated in a CATI
survey format. However, the study team believes that it is possible to leverage our
subcontractors’ existing relationships with contractors to solicit reasonable installation labor
estimates in an in-depth interview format, where a variety of site conditions and other sitespecific factors could be explored. To be successful, the study team recommends that these indepth interviews be conducted by qualified members of each subcontractor’s staff who have
direct experience in both the project types of interest and the contractors being interviewed.
Itron, Inc.
2-18
Recommended Data Collection
Measure Cost Study - Task 5 Report
As with the CATI surveys, the sample frames for the in-depth interviews will be developed using
a combination of the approved contractor lists maintained by each of the IOUs and the list of
contractors that have submitted relevant incentive applications for the current program cycle
(through Q4 2011) as shown in the SPT. The study team will leverage the expertise and
experience of QuEST/EMCOR (nonresidential lighting, HVAC, water heating, and shell),
VACOM (commercial refrigeration), and ERS (commercial food service and industrial process)
to ensure that the prototypical site conditions and project types are defined in terms and metrics
appropriate for each respective contractor population and consistent with the manner in which
actual projects are described and defined when contractors produce cost estimates for
competitive bids.
To validate the final installation labor hour estimates, the study team recommends leveraging
each subcontractor’s records of completed projects and/or IOU project invoice histories
involving the specific measures of interest. In cases where the available project records are not
sufficient or comprehensive enough to support validation for a particular measure, we
recommend leveraging the appropriate firm’s relationships with contractors to solicit a sample of
representative project bids (separate from the in-depth interview exercise described previously)
as a means of validating the installation labor hour estimates.
Appliance Recycling and Direct Installation Measures – Use of Program Data
It should be noted here that Table 2-3 does not show a recommended data collection plan for
appliance recycling measures. For these measures, the incremental measure cost is, at first blush,
represented simply by the labor costs associated with removing old appliances from residences,
and then decommissioning and disassembling the removed units. Conceptually, these quantities
could be estimated in a fairly straightforward manner by conducting a large sample telephone
survey of appliance recyclers in California. However, the IOUs’ appliance recycling programs
are administered by turnkey contractors – one for each electric utility, respectively. In this
context, estimating incremental measure cost is not simply a question of estimating the labor
hours associated with removing and decommissioning old units, since each turnkey contractor is
also earning a profit (which is embedded in the turnkey arrangement) and potentially getting
some return from the value of the recovered raw materials.
From this perspective, the incremental cost of current recycling programs – from a societal
perspective – could be estimated as simply the total value of each turnkey contract divided by the
number of units removed under the program. A similar situation occurs for direct installation
programs, where the IOUs engage turnkey contractors for program delivery (albeit in larger
numbers than for appliance recycling). A more robust empirical assessment of these issues would
require a detailed audit of each of the turnkey contracts and their performance to date. Such types
of financial auditing activities are outside the scope of this MCS update.
Itron, Inc.
2-19
Recommended Data Collection
Measure Cost Study - Task 5 Report
However, it is possible and feasible within the scope of this MCS update to leverage the program
audits being conducted under WO18 (Portfolio Strategy and Management Assessment) as a
means to acquire, at a minimum, the total value of each turnkey contract for recycling and direct
installation providers, along with the associated number of installations and removals by measure
type. It is also likely that equipment price sheets used by direct installation contractors can also
be obtained through data requests under WO18. We recommend organizing a dedicated
discussion between the responsible parties from ED and the WO18 and WO17 (Measure Cost
Study) teams to discuss the feasibility and relative priority of using the WO18 program audit
activities to feed relevant cost information to the WO17 team and establish specific timelines and
expectations for any activities determined to be feasible and high priority for both work order
efforts.
2.3 Other Measure Cost Data
As discussed in the Task 4 memo, the incremental measure costs produced in previous MCS
studies and IOU workpapers have been technically incremental installed costs, i.e. first costs. In
this sense, incremental lifecycle costs (i.e. taking into account differences in operating costs,
maintenance costs, disposal costs, and/or salvage values between baseline and high-efficiency
technologies) have not been in the scope of previous MCS efforts or the IOU workpapers and
therefore remain relatively understudied in California compared to incremental installed costs.
This data gap is relevant to the extent that the policy rules governing the cost-effectiveness
calculations for early replacement measures (e.g. dual baselines) require the use of lifecycle and
present-value approach and associated data. Moreover, the Total Resource Cost Test is itself a
lifecycle framework for assessing cost-effectiveness but only one element of incremental
measure lifecycle costs (installed costs) is currently well understood.
For this deliverable, the study team had identified the lifecycle cost data that would be feasible to
collect within the scope of this MCS update and the specific measures for which these lifecycle
costs are clearly incremental and not incurred under baseline technologies and conditions.
2.3.1 O&M Labor and Materials
The most significant lifecycle cost variable that appears to be feasible to collect and develop
within the scope of this MCS update is incremental operations and maintenance (O&M) cost. In
order to balance this additional objective with the overall scope, priorities, and resources of the
study, the study team identified the subset of in-scope deemed measures that have clear and wellestablished incremental O&M requirements. Within that subset, the study team identified the
smaller subset of deemed measures whose incremental O&M requirements are typically
performed using specialized labor and materials, either via third party service providers or paid
internal staff. In this sense, we excluded measures where incremental O&M activities are
typically performed by customers themselves (e.g. regular filter cleaning heat pump water
Itron, Inc.
2-20
Recommended Data Collection
Measure Cost Study - Task 5 Report
heaters). Note that we also included in-scope deemed measures that are maintenance practices
themselves – specifically duct testing and sealing for residential HVAC systems, refrigerant
charging and airflow (RCA) for residential and commercial DX systems, and condenser coil
cleaning in commercial DX systems.
Table 2-4 shows the final list of deemed measures for which the study team believes it is feasible
to collect and develop estimates of incremental O&M costs. This table of identified, applicable
O&M will be commented upon and updated by the subcontractors for each of their assigned
specialty areas before being finalized for their scopes of work. As the table shows, most of these
measures involve sensors and controls that need to be periodically recalibrated and
reprogrammed to maintain optimal energy and peak demand savings performance. The
exceptions are evaporative coolers which have the unique requirement of regular replacement of
the cooler pad to maintain cooling performance and cool roofs which have the unique
requirement of regular cleaning to maintain the reflective properties of the cool roof surface.
Table 2-4: Deemed Measures with Incremental O&M Costs
Sector
End Use
Tech Group
Measure Group
O&M Activity
Residential
HVAC
DX
RCA
refrigerant charging and airflow
Residential
HVAC
Evaporative cooling
Evaporative coolers
pad replacement
Residential
HVAC
Air distribution
Duct test & seal
duct testing & sealing
C&I
Lighting
Controls
Occupancy sensors
recalibration and reprogramming
C&I
Lighting
Controls
Timeclocks
reprogramming
C&I
Lighting
Controls
Daylighting controls
recalibration and reprogramming
C&I
HVAC
DX
RCA
refrigerant charging and airflow
C&I
HVAC
DX
Coil cleaning
condenser coil cleaning
C&I
HVAC
Evaporative cooling
Indirect evaporative coolers
pad replacement
C&I
HVAC
Heat rejection
Economizers
maintenance
C&I
HVAC
Air distribution
DCV
recalibration and reprogramming
C&I
HVAC
Other controls
BMS programming
reprogramming
C&I
HVAC
Other controls
Timeclocks
reprogramming
C&I
HVAC
Other controls
HVAC controller
recalibration and reprogramming
C&I
Building Shell
Roof
Cool roof
roof cleaning
C&I
Water Heating
Liquid circulation
Other controls
reprogramming
C&I
Refrigeration
Controls
Evaporator fan controls
reprogramming
C&I
Refrigeration
Controls
Remote refrigeration system
controls
reprogramming
C&I
Refrigeration
Controls
Vending machine controls
recalibration and reprogramming
C&I
Refrigeration
Controls
Anti-sweat heater controls
reprogramming
C&I
Food Service
Controls
Exhaust hood controls
recalibration and reprogramming
Itron, Inc.
2-21
Recommended Data Collection
Measure Cost Study - Task 5 Report
For the measures shown in Table 2-4, we recommend including a battery of questions soliciting
O&M labor and materials cost estimates (as well as the average periodicity of those O&M costs)
into the proposed CATI survey and in-depth interview activities with the respective contractor
populations described in Section 2.2 above.
2.3.2 RUL for ER Measures
For early replacement (ER) measures, a critical part of the dual baseline accounting is the
remaining useful life (RUL) of the replaced equipment. Under current policy rules, the current
default value for RULs is one third of effective useful life (EUL) of a new version of the same
equipment.
The MCS study team initially envisioned including RUL investigations and data collection
within the scope of WO17, but after discussions and follow-up with the impact evaluation teams
for early replacement measures (primarily WO29 and WO33), it became clear that the
development of RUL values for ER measures was already included in the scope those
evaluations studies. Additionally, it also became clear that the only measure group where we are
proposing to use data collection methods that would be appropriate to contribute to the
development of meaningful RULs is nonresidential lighting, for which the WO29 team has
already developed a battery of questions in its net-to-gross survey of participants. We therefore
do not recommend investigating or developing RUL estimates for ER measure within the scope
of this MCS update.
2.3.3 Lifecycle Setup
For those measures with that involve early replacement or differences over time in O&M or
other costs between baseline and high efficiency equipment, we will work with the ED cost
effectiveness team to ensure that the data is developed and organized in a structure and format
that makes clear which cost elements occur at which points in time to enable accurate lifecycle
cost calculations with the CPUC’s cost effectiveness models and framework.
Itron, Inc.
2-22
Recommended Data Collection
3
Appendix A – Attributes of NPD POS Data
Itron, Inc.
3-1
Appendix A
Measure Cost Study - Task 5 Report
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data
Dishwashers
Clothes Dryers
Clothes Washers
Refrigerators
Freezers
Room Air Conditioners
Color
Color
Color
Built-In
Color
Air Direction Control
Black
Black
Black
Not Specified
Black
2-Way
White
White
White
Not Built-In
White
4-way
Bisque
Bisque
Bisque
Built-In
Bisque
6-way
Stainless
Stainless
Stainless
Stainless
8-way
Stainless Look
Stainless Look
Stainless Look
Black
Stainless Look
Fixed
Other
Red
Red
White
Other
Infinite
Controls
Color
Blue
Blue
Bisque
Pushbutton
Green
Green
Stainless
Frost-Free
5,000-5,999 BTU
Knob
Other
Other
Stainless Look
Manual Defrost
6,000-6,999 BTU
Other
Defrost Drain
7,000-7,999 BTU
Cubic Capacity
8,000-8,999 BTU
Less than 20 inches
< 6.0 Cu. Ft.
9,000-9,999 BTU
24 inches
6.5-7.4 Cu. Ft.
10,000-10,999 BTU
Top Loading
25-29 inches
7.5-8.4 Cu. Ft.
11,000-12,999 BTU
Digital
Combination
Width
16-19 inches
Delicate Cycle
Energy Star
Delicate Cycle
Not Qualified
No Delicate Cycle
Qualified
Door Opening
Depth
Loading
Defrost Type
BTUs
20-23 inches
Drop Down
24 inches
Swing Out
Front Loading
30 inches
8.5-9.4 Cu. Ft.
13,000-14,999 BTU
25+ inches
Top Load
Number of Cycles
31 inches
9.5-10.4 Cu. Ft.
15,000-16,999 BTU
1 Cycle
32 inches
10.5-11.4 Cu. Ft.
17,000-19,999 BTU
Energy Star
Cubic Capacity
Qualified
1.0-1.4 Cu. Ft.
2 Cycles
33 inches
11.5-12.4 Cu. Ft.
20,000-22,999 BTU
Not Qualified
1.5-1.9 Cu. Ft.
3 Cycles
34 inches
12.5-13.4 Cu. Ft.
23,000-25,999 BTU
Interior Material
2.0-2.4 Cu. Ft.
4 Cycles
35 inches
13.5-14.4 Cu. Ft.
26,000-28,999 BTU
Plastic
2.5-2.9 Cu. Ft.
5 Cycles
Other Inch
14.5-15.4 Cu. Ft.
29,000 & over BTU
Stainless Steel
3.0-3.4 Cu. Ft.
6 Cycles
15.5-16.4 Cu. Ft.
Efficiency Rating (ER)
Other
Door Dispenser Options
3.5-3.9 Cu. Ft.
7 Cycles
Water Dispenser
16.5-17.4 Cu. Ft.
ER under 8.0
4.0-4.4 Cu. Ft.
8 Cycles
Ice Dispenser
17.5-18.4 Cu. Ft.
8.0-8.4 ER
1 cycle
4.5-4.9 Cu. Ft.
9 Cycles
No Door Dispenser
18.5-19.4 Cu. Ft.
8.5-8.9 ER
2 cycles
5.0-5.4 Cu. Ft.
10 Cycles
Ice & Water Dispenser
20.5-21.4 Cu. Ft.
9.0-9.4 ER
Number of Cycles
Itron, Inc.
3-2
Appendix A
Measure Cost Study - Task 5 Report
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data
Dishwashers
Clothes Dryers
Clothes Washers
Refrigerators
3 cycles
5.5-5.9 Cu. Ft.
11-15 Cycles
4 cycles
6.0-6.4 Cu. Ft.
16-19 Cycles
5 cycles
6.5-6.9 Cu. Ft.
20 + Cycles
6 cycles
7.0-7.4 Cu. Ft.
7 cycles
7.5-7.9 Cu. Ft.
300-599 RPMs
8 cycles
8.0-8.4 Cu. Ft.
600-649 RPMs
Not Qualified
9 cycles
8.5-8.9 Cu. Ft.
650-699 RPMs
Qualified
RPMs
Freezers
9.5-9.9 ER
Meat & Crisper Drawers
22.5-23.4 Cu. Ft.
10.0-10.4 ER
No Drawers
23.5-24.4 Cu. Ft.
ER 10.5 & above
Crisper Drawer Only
24.5-25.4 Cu. Ft.
Energy Star
25.5 + Cu. Ft.
Icemaker Options
Built In Icemaker
10 cycles
9.0-9.4 Cu, Ft.
700-999 RPMs
11 cycles
9.5-9.9 Cu. Ft.
1000-1199 RPMs
No Icemaker
12 cycles
10.0+ Cu. Ft.
1200-1299 RPMs
Icemaker Optional
Interior Light
1300-1599 RPMs
Icemaker Standard
Included
13 cycles
14+ cycles
Sound Control Option
Included
No Sound Control
Type
Dryer Rack Included
Yes
No
Optional
Width
<18”
1600+ RPMs
Room Air Conditioners
21.5-22.4 Cu. Ft.
Drawer Type
Icemaker Option
Cubic Capacity
Icemaker Optional
No Icemaker
No Interior Light
Electronic/Remote Control
Electronic w/o Remote
Electronic w/Remote
No Electronic Controls
Energy Star
Not Qualified
Qualified
Filter Type
Standard
Steam
0.0 - 1.9 Cu. Ft.
Yes
2.0 - 2.9 Cu. Ft.
Lock Included
Electrostatic
No
3.0 - 3.9 Cu. Ft.
No Lock
Paper
Type
4.0 - 4.9 Cu. Ft.
Lock
Number of Baskets
Charcoal
Heating/Cooling Option
Built In
18"
Side by Side
5.0 - 5.9 Cu. Ft.
No Baskets
Cooling Only
Portable
24"
Stackable
6.0 - 6.4 Cu. Ft.
1 Basket
Cool & Heat
Undersink
25"
Prestacked
6.5 - 10.4 Cu. Ft.
2 Baskets
Heat Pump
Countertop
27"
Portable Side by Side
10.5 - 13.4 Cu. Ft.
3 Baskets
Kilowatt Hours
> 27”
Portable Stackable
13.5 - 14.4 Cu. Ft.
4+ Baskets
Actual kWh
Other
Portable Washer/Dry Comb
14.5 - 15.4 Cu. Ft.
Washer/Dryer Comb
15.5 - 16.4 Cu. Ft.
No Dividers
Type of Sensor
16.5 - 17.4 Cu. Ft.
1 Divider
1 Setting
Soil Sensor
17.5 - 18.4 Cu. Ft.
2 Dividers
2 Settings
Load Sensor
18.5 - 19.4 Cu. Ft.
3 Dividers
3 Settings
Soil and Load Sensor
19.5 - 20.4 Cu. Ft.
4+ Dividers
4 Settings
Tub Type
Energy Type
Short
Electric
Tall
Gas
Type of Controls
Integrated
Itron, Inc.
Num of Drying Programs
1-4 Drying Programs
3-3
Number of Dividers
Mounting Kit Included
Included
No Mounting Kit
Number of Fan Settings
Appendix A
Measure Cost Study - Task 5 Report
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data
Dishwashers
Clothes Dryers
Semi-Integrated
5 Drying Programs
Standard/Visible
6 Drying Programs
Clothes Washers
Refrigerators
No Sensor
20.5 - 21.4 Cu. Ft.
Width
Freezers
Number of Shelves
Room Air Conditioners
5 Settings
21.5 - 22.4 Cu. Ft.
No Shelves
6 Settings
7 or more Settings
7 Drying Programs
Less than 18 inches
22.5 - 23.4 Cu. Ft.
1 Shelf
8 Drying Programs
18"
23.5 - 24.4 Cu. Ft.
2 Shelves
9 Drying Programs
24"
24.5 - 25.4 Cu. Ft.
3 Shelves
Included
10+ Drying Programs
25"
25.5 - 26.4 Cu. Ft.
4 Shelves
No Slide Out Chassis
27"
26.5 - 27.4 Cu. Ft.
5 Shelves
Yes
Greater than 27 inches
27.5 - 28.4 Cu. Ft.
No
Other Width Item
28.5 - 29.4 Cu. Ft.
Steam
Temperature Level
Controls
1 Temperature Level
Mechanical
2 Temperature Levels
Electronic
6+ Shelves
Type
Slide Out Chassis Option
Slide Out Filter Option
Slide Out Filter Included
No Slide Out Filter
29.5 - 30.4 Cu. Ft.
Chest
30.5+ Cu. Ft.
Upright
Timer Included
Compact
No Timer
Height
3 Temperature Levels
Agitator
4 Temperature Levels
Yes
30-39 inches
5 Temperature Levels
No
60-65 inches
Built In
Not Applicable
6+ Temperature Levels
Less than 30 inches
Timer Option
Kilowatt Hours
Actual kWh
Type
Slider/Casement
66 inches
Window
Impeller
67 inches
Other Configuration
Yes
68 inches
Split
Side by Side
No
69 inches
Stackable
Not Applicable
70 inches
Variable
Type
Portable Side by Side
Portable Stackable
Other inch items
Kilowatt Hours
Actual kWh
Width
Less than 18 inches
Controls
Mechanical
18 - 19 inches
Electronic
20 - 24 inches
28 inches
30 inches
Itron, Inc.
3-4
Appendix A
Measure Cost Study - Task 5 Report
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data
Dishwashers
Clothes Dryers
Clothes Washers
Refrigerators
Freezers
Room Air Conditioners
33 inches
36 inches
42 inches
48 inches
Other Inch
Shelf Type
Glass/Plastic Shelves
Other
Wire Shelves
Type
French Doors
Refrigerator only
Side by Side
Single Door-Internal Freezer
Freezer on Top
Compact
Freezer on Bottom
Water Filtration
Water Filtration included
No Water Filtration
Kilowatt Hours
Actual kWh
Num of Ext Refrigerator Doors
1
2
3
4
Itron, Inc.
3-5
Appendix A
Measure Cost Study - Task 5 Report
Table 3-1: Equipment Attributes Available for NPD’s Appliance POS Data
Dishwashers
Clothes Dryers
Clothes Washers
Refrigerators
Freezers
Room Air Conditioners
5+
Type of 4th Door
Flex Drawer
Refrigerator Only
Freezer Only
Other
Not Applicable
Itron, Inc.
3-6
Appendix A
Measure Cost Study - Task 5 Report
Table 3-2: Equipment Attributes Available for NPD’s Electronics POS Data1
Televisions
Desktop Computers
Monitors
Categories
3D
3D
2D LCD
All-In-One
Aspect Ratio
3D LCD
Bluetooth
Display Resolution
2D Plasma
Consumer-Commercial
Display Size
3D Plasma
Digital Connector
Display Type
Direct View
Display Resolution
Dot Pitch
Rear Projection
Display Size
Number of HDMI ports
Portable TV
Display Type
Screen Format
3D Full HD
DVD Format
Speakers
3D Glasses Bundled
DVD Included
Touchscreen Monitor
3D HDMI Version
DVD Read/Write
TV Tuner
3D Viewing Type
Flash Drive Capacity
USB
A/C Power Source
Graphics Controller
Vertical Refresh Rate (Max)
Advanced OS
Graphics Controller Brand
Backlight Source
Graphics Memory
Browser Installed
Hard Drive Disk Size
Cable Card Slot Included
Hard Drive Included
Colors per Pixel
HD Form Factor
Connected TV Type
Included Drives
Depth without stand
Integrated Camera
Digital Interface Included
Internal Memory
Display Resolution
Maximum Processor Capability
Display Segment
MHz
Display Type
Next Gen DVD Technology
DVD Included
Op Sys
Digital Connector
PC Form
DVD Format
PC Memory
DVD Read/Write
PC Processor brand
DVD Technology
PC Technology
Electronic Programming Guide
PC Technology Family
Energy Star On Mode Power (watts)
PC Top brands
Energy Star Screen Area (in2)
Processor Core
Energy Star Sleep Mode Power (watts)
Refurbished
Energy Star Version
Removable Media
Hard Drive Disk Size
Removable Media Type
Hard Drive Included
Screen Finish
Hard Drive Recorder Type
Screen Format
HD Form Factor
Storage Drive Type
Itron, Inc.
3-7
Appendix A
Measure Cost Study - Task 5 Report
Table 3-2: Equipment Attributes Available for NPD’s Electronics POS Data1
Televisions
Desktop Computers
Hard Drive Recorder Included
Television Tuner
Integrated Media Device
Touchscreen Display
Monitors
Internal Memory
Native Scan Formats
Networking Capable
Number of NTSC (Analog) Tuners
Number of ATSC (Digital) Tuners
Number of HDMI Connectors
Rear Projection TV Technology Type
Refresh Rate
Remote
Removable Media
Removable Media Type
RGB
Screen Format
Software Service Name
Touchpad CTV
1 – For the sake of brevity, the specific fields and ranges available for each attribute are not shown. A full list of all
fields and ranges available for each attribute in NPD’s POS data are available upon request.
Itron, Inc.
3-8
Appendix A
4
Appendix B - Sample Extract of C4A Invoice Data
Itron, Inc.
4-1
Appendix B
Measure Cost Study - Task 5 Report
Table 4-1: Sample Extract of California Cash for Appliances Program Invoice Database
Product
Type
Brand of Product
Model Number
AHRI
Certified
Reference
Number
Date of
Product
Purchase
Retailer
Purchase
Price
Amount
of
Rebate
Payment
ZIP Code
of Product
Delivery
7100
1000
956613614
10252
1000
956082761
CAC
CAC
Carrier
48VLNA360603
3389387
7/15/2010
American Standard
4A7B4030E1
3493822
8/2/2010
CAC
Amana
ASX180481A
3585505
7/28/2010
One Hour Heating & Air Conditi
9990
1000
934655067
CAC
Frigidaire
R6GF-X48K120X
3540034
10/11/2010
Sawyers Heating & Air Condtion
10800
1000
956339610
CAC
Rheem
14AJM48A01
3570336
7/29/2010
Bonney Plumbing Heating and Ai
7300
750
957478373
CAC
Carrier
24ACC636A003
3685859
8/3/2010
Allbritten
8136
1000
937304770
CAC
Trane
4YCY4036B1075A
3509219
8/29/2010
raymond hubbell dba hubbell ai
5900
1000
933075656
CAC
Ruud
RRPL-B030JK06XAJA
830348
8/18/2010
Comfort Air, Inc.
5230
1000
952589730
CAC
York
YCJF30S41S1A
3326855
8/1/2010
Energy Solutions
5765
1000
945211664
CAC
Lennox
XC21-060-230-05
4103248
8/5/2010
All Air Appliance Masters Inc
11400
1000
923746352
CAC
Carrier
48VLNO420603TP
3389390
8/3/2010
Superior Air
5561
1000
932302624
CAC
Trane
4TTR5036E1
3432940
7/30/2010
New Century Air Systems
7765
1000
958263540
CAC
Carrier
24APA748A031
3999141
8/3/2010
CAC
Bryant
187BNA060000DAAA
3747412
8/2/2010
CAC
Bryant
116BNA030000BAAA
3632694
CAC
Rheem
RRPL-B030JK06X
CAC
York
CAC
CAC
CAC
CAC
Itron, Inc.
Comfort Master Of Sacramento
Cassel Air Cond
9256
1000
930631076
Roseville Sheet Metal, Inc
15512
750
957477116
8/5/2010
Roseville Sheet Metal, Inc.
6333
1000
956211802
823912
7/29/2010
Oasis Air Conditioning Inc
4975
1000
933111604
YCJF24S41S1
3860713
7/30/2010
Spoors Heating & Air
5741
1000
956033532
Lennox
15GCSXBV-60-083X
3947018
8/10/2010
royce air
13800
1000
956622412
Amana
ASX140481A
3644432
8/2/2010
J Anthony Plumbing Heating & A
6200
500
922037504
Trane
4YCY4036B1075A
3509219
8/5/2010
Raymond Hubbell Dba Hubbell Ai
6093
1000
933092529
Armana
SSX140361B*
3799065
6/28/2010
Service Champions
8828
500
958222418
4-2
Gerald Giarrusso/Family Air
Appendix B
5
Appendix C - Final Disposition of DNV KEMA’s Fall
2011 Shelf Surveys
Itron, Inc.
5-1
Appendix C
Measure Cost Study - Task 5 Report
Table 5-1: Distribution of Completed Store Visits by Chain/Independent, Participating/Non-Participating Stores, Channel,
and IOU (source: DNV KEMA)
Channel
Discount
Discount
Discount Subtotal
Drug
Drug
Drug Subtotal
Grocery
Grocery
Grocery Subtotal
Hardware
Hardware
Hardware Subtotal
Home Improvement
Home Improvement
Home Impr Subtotal
Mass Merchandise
Mass Merchandise
Mass Merch Subtotal
Membership Club
Membership Stores
Membership Subtotal
Total Part Stores
Total NP Stores
All Stores
Itron, Inc.
Part / Non
Part
NP
Total
Part
NP
Total
Part
NP
Total
Part
NP
Total
Part
NP
Total
Part
NP
Total
Part
NP
Total
Part
NP
Total
Chain
8
0
8
6
4
10
1
2
3
1
2
3
8
2
10
5
5
10
10
0
10
39
15
54
PG&E
Indep
1
1
2
0
0
0
3
4
7
2
5
7
0
0
0
0
0
0
0
0
0
6
10
16
Total
9
1
10
6
4
10
4
6
10
3
7
10
8
2
10
5
5
10
10
0
10
45
25
70
Chain
8
0
8
4
3
7
2
3
5
3
1
4
9
0
9
5
3
8
10
0
10
41
10
51
SCE
Indep
1
1
2
0
3
3
3
2
5
4
3
7
0
0
0
0
0
0
0
0
0
8
9
17
5-2
Total
9
1
10
4
6
10
5
5
10
7
4
11
9
0
9
5
3
8
10
0
10
49
19
68
Chain
4
0
4
1
6
7
2
3
5
2
1
3
4
3
7
1
5
6
6
0
6
20
18
38
SDG&E
Indep Total
1
5
2
2
3
7
0
1
0
6
0
7
0
2
2
5
2
7
1
3
2
3
3
6
0
4
0
3
0
7
0
1
0
5
0
6
0
6
0
0
0
6
2
22
6
24
8
46
Chain
20
0
20
11
13
24
5
8
13
6
4
10
21
5
26
11
13
24
26
0
26
100
43
143
Total
Indep
3
4
7
0
3
3
6
8
14
7
10
17
0
0
0
0
0
0
0
0
0
16
25
41
Total
23
4
27
11
16
27
11
16
27
13
14
27
21
5
26
11
13
24
26
0
26
116
68
184
Appendix C
Measure Cost Study - Task 5 Report
Table 5-2: Number of Total Advanced and Non-Advanced Lamps by Channel and Detailed Lamp Type (source: DNV KEMA)
Channel
Lamp Type
Discount
Drug
Grocery
Hardware
Home
Improv.
Mass
Merch.
Memb. Club
Overall
ADVANCED
High-wattage and specialty MSB CFLs
High-wattage MSB CFLs (>30 Watts)
13
11
14
188
442
82
4,881
5,631
Specialty MSB CFLs: dimmable
−
158
70
348
3,686
373
7,764
12,399
Specialty MSB CFLs: 3-way
5
51
59
842
592
254
246
2,049
Other advanced MSB CFLs (≤30 Watts)
Reflector/flood
489
627
192
1,880
6,312
953
18,275
28,728
1,841
521
220
1,246
2,521
4,231
5,532
16,112
320
90
39
212
1,565
1,936
11,883
16,045
Candelabra (MSB)
−
94
17
113
302
277
−
803
Tube
−
−
5
61
142
14,412
−
14,620
Bug Light
−
33
27
85
230
128
−
503
Circline
−
−
−
16
4
−
−
20
Candelabra base CFLs
29
81
−
144
3,167
11,341
−
14,762
GU base CFLs
1
−
7
319
724
341
−
1,392
Pin base CFLs
−
−
−
2,728
4,921
476
−
8,125
Large base CFLs
−
−
−
66
67
19
−
152
Candelabra base CFLs with MSB adaptor
4
2
35
36
538
10
1,548
2,173
Reflector/flood MSB LEDs
−
−
−
223
2,375
45
2,101
4,744
A-lamp MSB LEDs
−
−
40
45
1,462
69
9,186
10,802
Other LEDs
−
27
90
1,278
2,634
945
5,187
10,161
Hybrid CFL/LEDs
−
−
−
−
166
3
−
169
Cold Cathodes
−
−
−
−
158
−
−
158
Basic CFLs (≤30 Watts)
12,398
2,662
7,223
9,868
57,897
12,480
74,592
177,120
Incandescent/Halogens
16,012
11,761
11,970
56,902
185,838
50,965
5,582
339,030
−
−
−
583
1,764
22
−
2,369
31,112
16,118
20,008
77,183
277,507
99,362
146,777
668,067
A-lamp
Globe
Other advanced non-MSB CFLs
Other advanced non-CFLs
NON-ADVANCED
High Intensity Discharge Lamps
Number of Lamps
Itron, Inc.
5-3
Appendix C