HomeMy WebLinkAboutEUS_Implementation Plan_V14_Sept_1
AEA End Use Study- Implementation Plan
Final
September 1, 2011
AEA End Use Study- Methodology Page | 1
September 1, 2011
Brian Saylor &
Associates
Table of Contents
1. Introduction ....................................................................................................................................... 4
1.1 Purpose and Objectives ................................................................................................................ 5
1.2 Guiding Principles ......................................................................................................................... 5
1.3 Timelines ....................................................................................................................................... 6
2. Background Resources ...................................................................................................................... 7
2.1 Review of Existing Energy End-Use Surveys .................................................................................. 7
2.2 Annotated Review of Alaska Energy Data Resources, Programs and Related Data ..................... 7
3. Overall Survey Methods .................................................................................................................... 7
3.1 Geographic and Sector Segmentation .......................................................................................... 7
3.2 Statewide Energy Use ................................................................................................................... 8
3.2.1 Statewide Energy Use by Sector ............................................................................................... 8
3.2.2 Statewide Street Lighting Energy Use- Mini Study ................................................................... 8
3.2.3 Water Treatment and Wastewater Treatment Statewide Energy Use- Mini Study ................. 8
3.3 Overview of Energy End use Data Collection Design .................................................................... 9
3.3.1 Establish the Sampling Frame ................................................................................................... 9
3.3.2 Select a Sample of Buildings from the Sampling Frame ........................................................... 9
3.3.3 Collect Energy Data from Sampled Buildings.......................................................................... 10
3.3.4 Estimate Energy End Uses Based on Survey Data................................................................... 10
3.3.5 Balancing Data Precision and Resources ................................................................................ 11
4. Railbelt and Southeast Residential Study Methodology ................................................................. 12
4.1 Evaluating the Use of ARIS data for Southeast and Railbelt Residential Analyses ..................... 12
4.1.1 Regional Distribution of ARIS Data ......................................................................................... 13
4.1.2 Characteristics of ARIS and Alaska Residential Housing Stock ............................................... 14
4.1.3 Conclusion .............................................................................................................................. 15
4.1.4 Use of ARIS Energy Rater Data................................................................................................ 16
4.1.5 Supplementing Missing Electrical Data in the ARIS Database ................................................ 16
4.2 Sample Frame and Sample Size Estimation ................................................................................ 16
4.3 Modified survey sample for Climate Zones 6, 7, and 8 ............................................................... 19
4.4 Survey Instrument Development ................................................................................................ 21
4.5 Energy End Use Analysis ............................................................................................................. 21
4.5.1 Heating, Ventilation, and Air-Conditioning ............................................................................. 22
AEA End Use Study- Methodology Page | 2
September 1, 2011
Brian Saylor &
Associates
4.5.2 Domestic Hot Water (DHW) ................................................................................................... 22
4.5.3 Interior Lighting ...................................................................................................................... 22
4.5.4 Exterior Lighting ...................................................................................................................... 23
4.5.5 Major Appliances .................................................................................................................... 24
4.5.6 Primary Cooking...................................................................................................................... 30
4.5.7 Other Kitchen Equipment ....................................................................................................... 32
4.5.8 Information Technology ......................................................................................................... 34
4.5.9 Entertainment......................................................................................................................... 35
4.5.10 Miscellaneous Electrical Appliances ....................................................................................... 36
5. Railbelt & Southeast Alaska Non-Residential .................................................................................. 38
5.1 Municipal/Borough Parcel Data .................................................................................................. 38
Location .................................................................................................................................................... 38
Parcel Data Availability ............................................................................................................................ 38
Data Detailed ........................................................................................................................................... 38
Current ..................................................................................................................................................... 38
5.2 Sample Frame ............................................................................................................................. 38
5.3 Preliminary Sample Size Estimates ............................................................................................. 40
5.4 Preliminary Sample Size by climate zone .................................................................................... 30
5.5 Available Data ............................................................................................................................. 30
5.6 Unique Stratification ................................................................................................................... 31
5.7 Survey Design .............................................................................................................................. 31
5.8 Survey Pre-Test, Validation, Reliability, and Vetting ................................................................... 31
5.8.1 Training ................................................................................................................................... 32
5.9 Data Collection Tools and Framework ........................................................................................ 32
5.9.1 Initial Data Collection & Review for Quality Control .............................................................. 34
5.10 End Use Analysis ......................................................................................................................... 34
5.10.1 Heating Ventilation and Air-Conditioning (HVAC) .................................................................. 35
5.10.2 Lighting ................................................................................................................................... 36
5.10.3 Office Equipment and Information Technology ..................................................................... 38
5.10.4 Food Service, Cooking and Refrigeration ................................................................................ 41
5.10.5 Process and Other Miscellaneous End Uses ........................................................................... 49
6. Rural North and West, Residential and Non-Residential ................................................................ 49
6.1 Available Data ............................................................................................................................. 49
AEA End Use Study- Methodology Page | 3
September 1, 2011
Brian Saylor &
Associates
6.2 Sample Frame ............................................................................................................................. 50
6.3 Survey Design. ............................................................................................................................. 51
6.4 Unique Stratification ................................................................................................................... 51
6.5 Data Collection Tools and Framework ........................................................................................ 52
6.6 HUB Community Methodology: Bethel Residential Study ........................................................ 52
6.7 HUB Community Methodology: Bethel Nonresidential. ........................................................... 54
6.8 Village Methodology. .................................................................................................................. 55
6.9 Collect Community Building Information for up to 225 Rural Communities. ............................. 56
6.10 End use Analysis .......................................................................................................................... 58
6.11 Survey Pre-Test, Validation, Reliability, and Vetting ................................................................... 58
6.12 Survey Implementation ............................................................................................................... 58
7. Future Opportunities and Needs ..................................................................................................... 59
7.1 Rural Fuel Consumption Study .................................................................................................... 59
AEA End Use Study- Methodology Page | 4
September 1, 2011
Brian Saylor &
Associates
1. Introduction
The Alaska Energy Authority (AEA) set a goal of increasing energy efficiency (electrical and heat)
by 15% by the year 2020. AEA contracted with WHPacific to develop a system for collecting
baseline data related to power and heat usage to refine energy efficiency improvement efforts
in residential and nonresidential structures in Southeast, Railbelt, and Rural Alaska.1 The overall
intent is to perform an end-use energy study and establish a baseline record of end-use energy
use.
Changes in energy policy which seek to change the energy use behavior of individuals,
communities, and businesses can be controversial, resource intensive, and difficult to
implement. It is essential to carefully monitor the effectiveness of policy initiatives in attaining
the desired reductions in end user energy consumption. The importance of measuring the
effectiveness of energy policy implementation has been clearly demonstrated in studies of
electricity market reforms in the UK, Norway, Alberta, and California.2
WHPacific is assisting AEA collect baseline data to monitor the effectiveness of policy changes on
energy use in Alaska. This work involves two steps. In the first step, WHPacific designed a data
collection method that gathers information about residential and non-residential Alaskan energy
use. To the extent possible, the data collection system takes advantage of existing research and
data gathering efforts, such as billing data, energy audit data, and other data collection efforts
like RuralCAP home visits. Primary data collection through phone or other types of surveys and
selected site visits are initiated only after all existing data sources have been consulted and used
to the maximum extent possible. Following the development and AEA approval of the data
collection method, the second step is the implementation of the approved method that will result
in an accurate assessment of energy use. The intent is that AEA will use this data for educational
purposes, as well as to develop and implement results-oriented energy improvement projects.
This report presents a detailed description of the intended data collection plan and energy end-
use methodology. The report begins with WHPacific’s understanding of the purposes and uses of
the baseline data, and briefly describes how the plan could support an ongoing performance
measurement system. While there is not an existing performance measurement system, the
proposed methodology could help establish such a system. The second section presents available
experience in measuring both residential and nonresidential energy use. The purpose of this
review is to better understand the advantages and disadvantages of alternative data collection
protocols. The third section briefly describes the three major geographic areas of interest to AEA:
Southeast Alaska, the Railbelt region (including Anchorage and Fairbanks), and rural Alaska.
1 AEA end-use study proposal, “Understanding of the Project and Commitment” prepared by WHPacific,
March, 2010. 2 Woo,C., Lloyd, D., Tishler, A, (2003) ”Electricity Market Reforms Failures: UK, Norway, Alberta and
California.” Energy Policy, 31:11, 1103-1115.
AEA End Use Study- Methodology Page | 5
September 1, 2011
Brian Saylor &
Associates
The subsequent three sections describe the proposed sampling and data collection methodology
for residential and nonresidential end user energy consumption in each of these regions. The last
section of this report discusses possible future directions for end-user energy data collection,
with special attention to direct measurement of household home heating oil use in rural Alaska.
1.1 Purpose and Objectives
The purpose of this project is to:
• Provide baseline data on energy use in residential and nonresidential buildings through
statistical estimates of building energy consumption by “end-use” (e.g., heating, cooling,
lighting, etc.), stratified by building type, location, and other parameters.
• Establish a framework for future end-use studies.
This end-use data is used to:
• Identify and rank opportunities for energy efficiency measures.
• Track changes over time in building and community energy use intensities and
greenhouse gas emissions.
1.2 Guiding Principles
The WHPacific team developed the following guiding principles as a means to focalize our
efforts:
• Alaska has had a longstanding focus on energy conservation and has collected much
valuable data related to building energy use (e.g., the ARIS database). This project will
use these existing data resources and infrastructure to the greatest extent possible. This
will result in a richer study with more contextual data than could have been
accomplished without this project.
• Maximize existing data sources to facilitate future integration and replication.
• Establish a framework for future end-use studies by utilizing available data to gauge
progress in intervening years.
• Provide a research design for this first-ever State of Alaska Energy End Use Baseline
Study.
• Facilitate future conduct follow-up studies at 5 and 10 years.
• Build upon the ISER End Use Energy Data Collection for Alaska Buildings-Guidance
document prepared by Steve Colt.
• Emphasize data collection over complex analysis.
• Identify gaps in statewide knowledge regarding structures and support an on-going
assessment of overall energy use in buildings.
• Establish as the project goal the reduction in overall energy use through a
comprehensive research design that allows stakeholders improved access to current
energy end-use data, thus enhancing the knowledge base at every level possible for
AEA, ISER and the State of Alaska.
AEA End Use Study- Methodology Page | 6
September 1, 2011
Brian Saylor &
Associates
The first phase of this project was to chronicle all national “lessons learned” which provided a
contextual background for the data collection plan. Please refer to the appendices for
information on projects consulted. For this type of project, there are not “best practices” per
se, rather just “practices” that are arguably best suited to the specific situation, available
funding, and overall goals. The project vision was reflective of a goal established in the April
2010 Alaska Energy Pathway and reinforced by recent legislation “to improve energy efficiency
(electrical and heat) 15% between 2010 and 2020.“ As stated in the RFP issue date February 24,
2011, “the goal is to collect baseline data and develop repeatable methodology that will allow
the AEA, project partners, or others to be able to measure changes in energy use and infer
improvements to energy efficiencies at a future point in time.”
1.3 Timelines
The project’s general timeframe is as follows:
• Phase I: (May 6, 2011- September 1, 2011). This includes the development of the
overall data collection implementation and analysis plan.
• Phase II: (September 1- December 1, 2011). This phase encompasses the general data
collection period.
• Phase III: (December 1- March 1, 2011). This phase includes analysis and final report
write-up.
AEA End Use Study- Methodology Page | 7
September 1, 2011
Brian Saylor &
Associates
2. Background Resources
This section summarizes key background resources, data, and programs used to inform and
guide the design of this end use study.
2.1 Review of Existing Energy End-Use Surveys
A review of existing energy end use surveys was conducted to inform the development of this
energy end-use survey, identify best practices, identify methodologies and data that could be
used by this study, and identify any lessons learned and issues that may present challenges. A
brief overview of each program is provided in the Appendix, including links to key documents and
data for each of the surveys. A detailed summary comparison table is also provided.
The review of existing surveys was used to inform and develop the AEA End Use Study, including
sampling methodology, end use energy calculation, and other criteria.
2.2 Annotated Review of Alaska Energy Data Resources, Programs and Related Data
One of the goals of this energy end use survey is to leverage existing data resources to the
greatest extent possible. A review of existing and planned energy studies, data, databases,
programs, and related information was conducted. An annotated list of these resources and a
discussion of their potential uses for this energy end-use study are provided in the Appendix.
3. Overall Survey Methods
This section describes the overall approach to population and sampling frame development,
sampling, the use of historical data and analytic methods employed in this study.
3.1 Geographic and Sector Segmentation
The study is segmented into four regions Southeast (Climate Zone 6); Railbelt (Climate Zone 7)
and the Interior Rail belt (Climate Zone 8); and rural north/west. These regions are further
segmented by residential and non-residential buildings. Non-residential buildings include
offices, commercial buildings, service buildings, public assembly, etc., but exclude military,
industrial and manufacturing.
• Railbelt- Climate Zones 7 & 8
• Residential
• Non-Residential (excluding manufacturing, industrial and military)
• Southeast- Climate Zone 6
• Residential
• Non-Residential (excluding manufacturing, industrial and military)
• Rural
• Combined residential and non-residential
AEA End Use Study- Methodology Page | 8
September 1, 2011
Brian Saylor &
Associates
3.2 Statewide Energy Use
The primary purpose of this study is to provide energy end use details for residential and non-
residential buildings. However, it is important to place building energy use in context of
statewide energy consumption. Further details on several non-building energy uses, including
street lighting and water/waste water treatment infrastructure, are desired. The team will
coordinate with AEA to document overall statewide energy use, street lighting, and
water/wastewater infrastructure energy use as described below.
3.2.1 Statewide Energy Use by Sector
The team will work with AEA to obtain total sectored energy use summaries from the utilities for
military, industrial, residential, commercial, and other aggregation levels as available. AEA will
request total energy sales from the utilities broken out by sector. The team will aggregate this
data and incorporate it into an overall statewide summary and place this in context with the
building energy end use study. If the utilities are unable to provide this data, the team will work
with AEA to develop a method to estimate missing data. Note that detailed energy end use data
collection will be conducted for sectors other than residential and non-residential buildings.
3.2.2 Statewide Street Lighting Energy Use- Mini Study
AEA desires energy end use data on street lighting to evaluate energy efficiency upgrade
opportunities, such as conversion to LED lamps, hi/low dimming coupled with occupancy
sensors, etc. Detailed street lighting data is generally available from the municipalities, DOT, and
other agencies that own and maintain this data. The team will work with AEA and other lighting
stakeholders to locate and solicit street lighting data from identified personnel. If needed, the
team will create a street lighting data request form that can be sent to relevant agencies. The
team will integrate and analyze existing street lighting data, and to the extent possible, provide
a detailed energy end use breakout by lamp type.
The team will also conduct a survey of street lighting (fixture counts, bulb types, and bulb
wattages) in the rural villages and rural hub during the site visits for the rural sector field study.
Aside from this, the team will not conduct field surveys or street lighting counts.
3.2.3 Water Treatment and Wastewater Treatment Statewide Energy Use- Mini Study
AEA also desires to document energy consumption for water and wastewater treatment plants.
The Alaska Native Tribal Health Consortium/ARUC program has extensive access to rural water
and wastewater treatment facilities and has already secured support.
The team will develop a data request form to be submitted to municipalities and other entities
that own or operate wastewater treatment plants, requesting data on their total annual energy
consumption. The team will work with ARUC to identify end use energy patterns for the rural
wastewater treatment plants in the villages and hubs visited for the rural building energy end
use data collection. The team will then aggregate the data, estimate energy consumption from
non-reported municipalities, and estimate statewide energy consumption.
AEA End Use Study- Methodology Page | 9
September 1, 2011
Brian Saylor &
Associates
3.3 Overview of Energy End use Data Collection Design
This section presents an overview of the process by which the research team approached each
specific component of the data collection plan. These tasks were characterized by a thorough
and defensible data collection approach specific to each region and of facility type.3
3.3.1 Establish the Sampling Frame
The sampling frame includes the total population of buildings one is trying to understand. The
first step in sampling is to define the population of interest clearly and accurately. In this case,
the populations requested by AEA included residential and nonresidential facilities in four
distinct geographic areas. These “sampling units” are selected so as not to overlap and exhaust
the entire population.
The second step is to define the sampling frame. The sampling units are organized into sampling
frames. The sampling frame helps identify the elements within each sampling unit. This requires
specific knowledge of the number and characteristics of the total residential and nonresidential
building population.
3.3.2 Select a Sample of Buildings from the Sampling Frame
A sample must be representative of the total population (frame) and must be able to provide
information that will allow AEA to make inferences about the full population. To ensure that the
data collected from the sample can be used to generate conclusions about the entire population,
the sample must reflect the overall characteristics of that population. For the purposes of this
project, these characteristics included consideration of:
• geographic region
• climactic zone
• community size
• residential building type
• nonresidential building type and aggregate energy use
Special attention was given to the use of existing data whenever possible. These data sources
included ARIS and RuralCAP data, among others. Statistical analyses compared these existing data
sets to enumerated data (e.g. 100% of the population, such as the Housing Census4), results of
random household surveys5, or utility records from larger utilities.
3 Much of the procedural material in this discussion is from Sapsforf, R., Jupp, J. (eds) Data Collection and
Analysis, Sage Publications, Thousand Oaks, CA, 1996. 4 Alaska Housing Census Data 2010, from the Census Bureau’s website. (The data was taken from the
Census Bureaus American Community Survey 2009 data set. It is the most current Alaska data available
from the U.S. Census Bureau) 5 2009 Alaska Housing Assessment, prepared for the Alaska housing finance Corporation by Information
Insights, December, 2009
AEA End Use Study- Methodology Page | 10
September 1, 2011
Brian Saylor &
Associates
3.3.3 Collect Energy Data from Sampled Buildings
Whenever possible, existing data sets, such as ARIS and RuralCAP, will be used. This may require
sampling within the data sets to ensure that the data is representative of the populations being
considered. This data may be supplemented with additional data elements that are currently
not included in existing data sets, particularly when data on the energy use in complex
structures is inadequate.
3.3.4 Estimate Energy End Uses Based on Survey Data
There are a variety of approaches used to estimate building energy end uses. The following
figure illustrates the general range of energy end use calculation approaches, from simple (and
least time/cost intensive) to most complex. The left end of the spectrum represents relatively
simple, high-level end use calculation methods, such as the example end use calculation
outlined in ISER guidance document in the RFP and contract. For example, heating energy use
could be simplified to be simply a function of the climate, measured by heating degree days
(HDD), and the building’s heating system fuel type. A more nuanced approach would be to use
an “engineering calculation” which includes additional parameters. For example, heating energy
estimates would consider wall construction, wall area, window area, window type, and other
details in addition to HDD and fuel type. This method provides more detailed and nuanced
estimates. The next step would be to perform a regression analysis and statistically adjust the
simple or engineering calculations to match utility bills. At the most complex end of the
spectrum, detailed building energy simulations could be used to estimate energy end uses. Each
method has strengths and drawbacks, and appropriate applications.
Simple Calculation or Value
e.g., Htg Energy = f (HDD, Fuel Type))
$
Simple Energy End - Use Calculation Methods Complex
Engineering Calculation “Tuned”
Via Regression Analysis
$$$
Engineering Calculation
e.g., Htg Energy = f ( Sqwft , R - value, Window Area,
Window Type, Infiltration, Htg Type, Furnace Age, etc.)
ASHRAE, IESNA & Other industry - based calcs ;
methodologies/data from other end - use surveys
$$
Energy Audit + Building
Energy Model
e.g., AK Warm
$$$$
AEA End Use Study- Methodology Page | 11
September 1, 2011
Brian Saylor &
Associates
Figure 1: Continuum of Energy end use Calculation Methods
The team has balanced the desire for more complex/detailed energy end use calculations with
budget and time constraints, and project guidance to focus more on data collection, and less on
complex data analysis. The team is planning on using a mixture of approaches. For the Railbelt
and Southeast residential sectors, the team plans on using energy audit data in the ARIS
database as the primary source of building thermal end uses. This provides a very high level of
analytical rigor, and aligns with existing state analysis and data collection tools and practices. If
this data is not available, the team will need to rely on simpler end use estimation approaches
due to budget and time considerations. The ARIS data does not contain robust electrical end use
or lighting data. Therefore, a supplemental survey will be sent out to residential homes in the
Railbelt and Southeast.
For nonresidential buildings in the Railbelt and Southeast, pre-existing data is more sparse. The
team plans on using engineering calculations.
In keeping with the program guidance to focus on data collection over data availability, as well as
constraints imposed by utilizing pre-existing data, the team does not plan to perform the
regression analyses of the “statistically adjusted engineering model” approach.
For each building sampled, energy end uses will be determined. The “Unit Energy Consumption”
(UEC) is the annual energy use for a specific end use and specific fuel type, or UECend use, fuel type.
The total building energy use is the sum of all building end uses and all fuel types, or:
𝐴𝑛𝑛𝑢𝑎𝑙 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑠𝑒=��𝑈𝐸𝐶𝑖,𝑗𝑗=𝑓𝑢𝑒𝑙 𝑡𝑦𝑝𝑒𝑖=𝑒𝑛𝑑 𝑢𝑠𝑒
Annual building energy use can be aggregated at various levels, including the overall state level.
UEC’s can also be aggregated at various levels, allowing for various statistical analyses.
3.3.5 Balancing Data Precision and Resources
The graphic below summarizes the elements that impact the total project budget and tradeoffs.
Each of these elements must be considered to develop a methodology that meets the program’s
goals and desires while staying within budget and timeline.
AEA End Use Study- Methodology Page | 12
September 1, 2011
Brian Saylor &
Associates
Figure 2: Balancing Data Precision & Resources
4. Railbelt and Southeast Residential Study Methodology
4.1 Evaluating the Use of ARIS data for Southeast and Railbelt Residential Analyses
Consistent with the principle of using available data whenever possible, the research team
examined the use of available data for measuring end user energy consumption in residential
settings. The ARIS data base emerged as an important resource for this study.
ARIS data was compared to the Alaska Housing Assessment random sample and the 2009 US
Census housing data6 to determine the extent to which ARIS data was representative of the
residential housing stock. ARIS data analyzed included all homes built since 2008, and energy
audits designated as:
• As-is
• BEES
• From Plans
• Weatherization audit as-is
• Housing Authority
• ACHP
6 Alaska Housing Census Data 2010, from the Census Bureau’s website. (The data was taken from the
Census Bureau’s American Community Survey 2009 data set. It is the most current Alaska data available
from the U.S. Census Bureau)
2009 Alaska Housing Assessment, prepared for the Alaska Housing Finance Corporation by Information
Insights, December, 2009
AEA End Use Study- Methodology Page | 13
September 1, 2011
Brian Saylor &
Associates
Non parametric statistical tests were conducted to compare the two data sets. Data from the
housing census and/or the AHFC Alaska housing assessment random survey were used as the
expected values in a non parametric chi square analysis. The results of that analysis are shown
in the following figures.
4.1.1 Regional Distribution of ARIS Data
ARIS data is not representative of the regional inventory of Alaskan residences, as illustrated in
Tables 1 and 2 and Figure 3.
Table 1: Comparison of ARIS and Housing Census Data by Region
ARIS 2000 Census 2008 Estimate
Number Percent Number Percent Number Percent
SE (Other Urban) 2718 7.7% 42424 16.3% 49656 17.5%
Railbelt 31508 89.3% 133659 51.3% 147921 52.2%
Rural 1075 3.0% 84529 32.4% 85920 30.3%
35,301 260,612 283,497
Table 2. Comparison of ARIS Data with AHFC Housing Assessment Random Sample
ARIS Alaska Housing Assessment
Number Percent Anchorage Fairbanks Other Urban Rural 1 Rural 2
Less than 500 105 0.4% 0.08% 3.0% 0.0% 2.0% 12.5%
501-1,000 1650 6.3% 12.0% 15.2% 16.8% 16.2% 36.9%
1001-2,000 11706 44.4% 46.2% 48.2% 45.8% 54.0% 39.7%
More than 2000 12930 49.0% 40.9% 33.6% 37.4% 27.7% 10.8%
26391
Missing 8936 25.3%
Observations 35327 1700
AEA End Use Study- Methodology Page | 14
September 1, 2011
Brian Saylor &
Associates
Figure 3: Regional distribution of ARIS data versus Census data
Chi Square non parametric test confirms that these two samples are significantly different.
4.1.2 Characteristics of ARIS and Alaska Residential Housing Stock
ARIS was thought to be representative of the characteristics of Alaskan residences. Two
dimensions of housing stock were tested; the age of the structure, and the number of bedrooms
(a proxy for size). The results of these analyses are shown in Figures 4 and 5.
Figure 4: Year built: ARIS data versus Census data
Chi square non parametric test confirms that these two samples are not significantly different.
16.3% 17.5%
89.3%
51.3% 52.2%
3.0%
32.4% 30.3%
7.7%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
SE (Other Urban) Railbelt Rural
Percent
ARIS 2000 Census 2008 Estimate
AEA End Use Study- Methodology Page | 15
September 1, 2011
Brian Saylor &
Associates
Figure 5: Number of Bedrooms: Aris versus Census data
Chi square non parametric test confirms that these two samples are not significantly different.
4.1.3 Conclusion
The research team concluded that ARIS data is representative of the Alaska residential housing
stock. Rural areas appear to be underrepresented; therefore, ARIS data can be used to measure
end user energy consumption for Southeast and Railbelt regions. Residential buildings in rural
Alaska may be informed using ARIS data, but additional data sets will be required to measure
end user energy consumption.
ARIS data appears to include sufficient observations on:
• Single family dwellings, detached
• Single family dwellings, attached (zero lot line or condominiums)
The data set appears to substantially under-represent mobile homes and multi-family dwellings.
However, based on preliminary review of the data, there appear to be sufficient mobile homes
and multi-family dwellings in the Railbelt and Southeast regions to present a statistically
significant sample.
0.03% 1.72%
13.87%
48.85%
28.06%
7.47%
4.5%
14.0%
27.6%
36.1%
13.9%
3.9%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
0 1 2 3 4 5 or more
Percent
ARIS 2010 Housing Census Data
AEA End Use Study- Methodology Page | 16
September 1, 2011
Brian Saylor &
Associates
4.1.4 Use of ARIS Energy Rater Data
The State of Alaska continues to provide energy ratings to residential properties through the
residential energy rebate program. This year’s program is expected to complete between 40
and 50 houses per week, yielding between 480 and 600 records during the timeframe of this
study. While is it unlikely that all of these will be completed, the effort should generate
adequate data to permit reasonable generalizations about energy use in these regions.
4.1.5 Supplementing Missing Electrical Data in the ARIS Database
The residential energy rebate program only requires energy raters to collect detailed building
thermal performance data. Data on other electrical and lighting end-uses are not part of the
existing data collection protocols. An additional data collection component has been developed
by the WHPacific research team to address this shortcoming. AKWarm energy rate data
collection protocols will be supplemented by including an additional electrical data collection
schedule and survey. In addition, electrical data may be required for mobile homes and multi-
family residential facilities. If possible, historical ARIS data on these types of facilities will
provide the basic information for thermal energy use.
4.2 Sample Frame and Sample Size Estimation
For the purposes of this study, the sample needed to address four specific housing types: single family
detached dwellings; single family attached dwellings (zero lot lines); multifamily dwellings; and mobile
homes. Current data is not available on the distribution of housing types by climactic zones. Consequently,
total occupied housing data was obtained from preliminary 2010 U.S. Census data for each Alaska census
area7. The Census data was then combined by climactic zone. Data on occupied and vacant housing is
shown in Table 3.
The research team noted a high percentage of vacant housing in climatic zones 6 and 7. Approximately
13.5 percent of Railbelt housing units and 19.1 percent of Southeast Alaska housing units were listed as
vacant. However, most of those units appear to be seasonal buildings, such as hunting cabins or other
structures that may not require an in-depth energy analysis (Table 3).
7 Alaska Department of labor and workforce analysis, Research and analysis section, US Census Demographics, Accessed at http://live.laborstats.alaska.gov/cen/dparea.cfm
AEA End Use Study- Methodology Page | 17
September 1, 2011
Brian Saylor &
Associates
Table 3 Alaska Housing Units by Census Area and Climactic Zone
Climactic Zone Borough or Census
Area
Total Occupied Vacant Seasonal
Number Percent of
All Units
Number Percent of
Vacant
Units
Zone 6 Haines 1,631 1,149 482 29.6% 345 71.6%
Hoonah/Angoon 1,771 931 858 48.4% 580 67.6%
Juneau 13,055 12,187 868 6.6% 311 35.8%
Ketchikan 6,166 5,305 861 14.0% 382 44.4%
Petersburg 1,994 1,599 395 19.8% 262 66.3%
Prince of Wales 2,992 2,194 798 26.7% 476 59.6%
Sitka 4,102 3,545 557 13.6% 237 42.5%
Skagway 636 436 200 31.4% 48 24.0%
Wrangell 1,428 1,053 375 26.3% 248 66.1%
Valdez Cordova 6,102 3,966 2,136 35.0% 1,341 62.8%
Yakutat 450 270 180 40.0% 131 72.8%
Total Southeast Alaska 40,327 32,635 7,710 19.1% 4,361 56.6%
Zone 7 Anchorage 113,032 107,332 5,700 5.0% 1,499 26.3%
Kenai 30,578 22,161 8,417 27.5% 6,083 72.3%
Mau Su 41,329 31,824 9,505 23.0% 6,832 71.9%
Total Zone 7 184,939 161,317 23,622 12.8% 14,414 61.0%
Zone 8 Denali 1,771 806 965 54.5% 744 77.1%
Fairbanks North Star 41,738 36,441 5,342 12.8% 1,676 31.4%
SE Fairbanks 3,915 2,567 1,384 35.4% 724 52.3%
Total Zone 8 47,424 39,814 7,691 16.2% 3,144 40.9%
Total Rail-belt 232,363 201,131 31,313 13.5% 17,558 56.1%
Once the total number of occupied buildings was determined, it was necessary to establish the number of
buildings within each of the four housing types. Again, as this data was not available by Borough or Census
area, the statewide average of the distribution of housing types was used to estimate the number of
houses of each type by climactic zone. This method assumes that the statewide distribution can be
extrapolated to regional data.
The estimated sample size for each building type and within each climactic zone is shown in Table 4.
Sample size was estimated using a margin of error of +/- 7.5% (MOE = 15), a confidence level of 90%, and a
response distribution of 30%. The geographic areas specified in the proposal are reflected in the sampling
plan.
AEA End Use Study- Methodology Page | 18
September 1, 2011
Brian Saylor &
Associates
As displayed in Table 4, a preliminary sample size of 206 observations will be required for this study.
However, these observations may not be proportional with the overall distribution of building type. As
discussed with AEA staff, ARIS data from 2008 through 2010 will be used to provide thermal energy use
information for mobile homes and multifamily dwellings.
Table 4 Residential Sample Size Estimates with no Climactic Zones (MOE=15%, 90% Confidence, 30% Response
Distribution) cell-based
Table 5 lists the total number of observations required for each building type (i.e. the number of
observations anticipated from the AKWarm Energy Ratings scheduled between July 15 and October 15,
2011). The distribution was taken from an analysis of existing ARIS residential “as is “audit data from 2008
to 2010. This analysis assumes that the distribution of audits by building type will follow historical patterns
of ARIS data. In some cases, there is sufficient data from the planned AKWarm energy audits to meet the
sampling requirements. Single family detached homes are clearly able to meet the sample requirements.
Single family attached homes, often called zero lot line homes, may present some challenges, particularly in
Fairbanks (Zone 8) and Southeast Alaska (Zone 6). However, the most significant challenges are associated
with the potential underrepresentation of multifamily dwellings and mobile homes in all regions. Additional
sampling may be required to overcome this shortcoming.
US Census
Building
Type
Distributio
n
Southeast US Census
Building
Type
Distributio
n
Railbelt Total Sample
Estimate
d Units
Sample
Size
(MOE=15
)
Estimate
d Units
Sample
Size
(MOE=15
)
Total
Units
Housing Type
MOE=1
5
Single Family
Detached 49.0% 13,933 26 52.0% 184,292 26
198,22
5 52
Single Family
Attached 5.0% 1,422 25 8.0% 25,897 26 27,319 51
Multifamily 36.0% 10,237 26 34.0% 72,573 26 82,809 52
Mobile Home 10.0% 2,844 25 6.1% 18,369 26 21,212 51
Total 100% 28,435 102 100% 185,325 104
213,76
0 206
AEA End Use Study- Methodology Page | 19
September 1, 2011
Brian Saylor &
Associates
Table 5 Additional Observations Needed
Housing Type
Estimated Observations
in ARIS, 2008-2010
Southeast Railbelt Total
US Census
Building Type
Distribution
Additiona
l
Observati
ons
Needed
(MOE =
15%, ARIS
= 600)
US
Census
Building
Type
Distribut
ion
Additiona
l
Observati
ons
Needed
(MOE =
15%, ARIS
= 600)
Additiona
l
Observati
ons
Needed
(MOE =
15%, ARIS
= 600)
Single Family
Detached 235 294 49.0% 0 52.0% 0 0
Single Family
Attached 12 15 5.0% 10 8.0% 11 21
Multifamily 4 5 36.0% 21 34.0% 21 42
Mobile Home 0 1 10.0% 24 6.1% 25 49
Total 480 600 100% 55 100% 57 112
4.3 Modified survey sample for Climate Zones 6, 7, and 8
If a three tiered climatic zone approach is chosen, the modified sample size will be conducted by Alaska
climate zone as delineated below. The completed data collection quotas will be performed per the tables
as found in the attached appendix. Data will be provided in an excel format, compatible with RDI
requirements, and in a Sequel Server 2008 format. In terms of the Climate Zone Methodology, the
following boroughs will constitute the various climate zones as represented in the map below:
• Climate Zone 6 (Southeast) = As represented in the map below.
• Climate Zone 7 (Southcentral) = Kenai Peninsula Borough’s, Matanuska Susitna Borough’s,
Kodiak Island Borough, Municipality of Anchorage, and Valdez Cordova.
• Climate Zone 8 (Fairbanks) = Denali Borough, Southeast Fbx, and Fairbanks- North Star Borough.
AEA End Use Study- Methodology Page | 20
September 1, 2011
Brian Saylor &
Associates
Figure 2: Climate Zone Map
Table 6- Residential Preliminary Sample Size by Climate Zone
Zone 6 Zone 7 Zone 8 Total
Location Units Sample MOE Units Sample MOE' Units Sample MOE Units Sample Bldg Type
Single Fam Detached 13,611 17 20.0% 82,829 17 20.0% 17,452 17 20.0%
113,892
51
Single Fam Attached 1,389 17 20.0% 12,745 17 20.0% 2,685 17 20.0%
16,819
51
Multifamily 9,999 17 20.0% 54,160 17 20.0% 11,411 17 20.0%
75,570
51
Mobile 2,778 17 20.0% 9,717 17 20.0% 2,047 17 20.0%
14,542
51
Total 27,777 68 159,301 68 33,596 68 220,823 204
AEA End Use Study- Methodology Page | 21
September 1, 2011
Brian Saylor &
Associates
4.4 Survey Instrument Development
Energy raters will continue to use the available AKWarm data entry screens for measuring
thermal energy use. It is understood that additional data collection for electrical uses must be
completed on all new energy audits as the data requested by AEA exceeds that of the new
AKWarm software. WHPacific will develop data collection protocols for electrical energy
consumption. To the extent possible, residential and nonresidential data collection will use the
same protocols for comparability purposes.
As requested by AEA, the survey team will try to solicit permissions from respondents to access
utility records. Up to 10% of the respondent’s utility records maybe reviewed for verification
purposes. This information will be used to verify end-use energy consumption calculations.
WHPacific has developed the data collection instrument found in the Appendix for the
residential component of SEAK, Railbelt. This instrument may also be applied in the rural sector.
4.5 Energy End Use Analysis
The Railbelt and Southeast Alaska residential energy end use study has been designed to take
advantage of the extensive number of high quality residential energy audit data stored in the
Alaska Retrofit Information System (ARIS) database. The ARIS database contains detailed
residential energy audit data dating to 1996 (93% of the data is from 2008-2011), collected as
part of Alaska Home Energy Rebate Program, Building Energy Efficiency Standard (BEES)
compliance certifications, weatherization, and related programs. Heating, Ventilation and Air-
Conditioning (HVAC) and Domestic Hot Water (DHW).
The Alaska Home Energy Rebate Program data contained in ARIS is particularly valuable to this
end use study. The program requires a certified home energy rater to conduct a detailed
building energy audit. The rater must useAKWarm8 to analyze building energy performance,
focusing on building HVAC energy consumption. These audits take ~3-4 hours, and cost between
$325 to $500 per home. The level of detail and rigor in these audits and energy analyses is
significantly greater than that which could be funded through this energy end use study.
Between 2008 and 2011, approximately 22,240 “as-is” (e.g., pre-retrofit) home energy rebate
audits were conducted. At $325/audit, this represents a $7,228,000 investment in high quality
residential energy audit data. The team plans to leverage this data to provide a significantly
higher quality energy end use analysis of residential buildings beyond what could be performed
within the constraints of the funding available for this project.
The team understands that the ARIS database contains detailed AKWarm results, including
energy end use breakouts by fuel type for building HVAC end uses and DHW, and is planning to
8 AKWarm is building an energy simulation modeling software tool that analyzes building energy use on an
hourly basis using detailed climatic data, and uses detailed building envelope and HVAC system data
obtained through the energy audit. AKWarms’ performance has been validated through the Building Energy
Simulation Test (BESTest).
AEA End Use Study- Methodology Page | 22
September 1, 2011
Brian Saylor &
Associates
use the ARIS data as the primary data source for HVAC and DHW residential energy end use data
in Climate Zones 6, 7, & 8. The team is still working to obtain access to the complete ARIS
database and confirm that the necessary data is available. If ARIS does not contain the expected
data (HVAC and DHW energy end use data by fuel type), this approach will need to be
reevaluated.
The audit data in ARIS does not contain detailed end use data on lighting, appliances, cooking,
and other end use data. A supplemental survey will be conducted to obtain information on
these end uses. Engineering calculations will be used to estimate energy end uses based on the
survey data. The team explored the feasibility of sampling homes in the ARIS database to obtain
linked data for HVAC, DHW, and other end uses. However, this is not feasible. Therefore, the
sample will be drawn from other sources.
Due to the detailed nature of the energy data in ARIS, the inability to link the HVAC and other
end use data, and the programmatic focus on data collection over complex analysis, the team
does not plan to perform a regression analysis to adjust the HVAC and DHW end uses calculated
by AKWarm and the other engineering models to historical utility consumption data (e.g.,
statistically adjusted engineering models).
4.5.1 Heating, Ventilation, and Air-Conditioning
The team plans to use existing energy end use data in the ARIS database as the primary source
of HVAC end use data. HVAC end use estimates are based on a detailed building energy audit by
a certified auditor and the use of a detailed hourly building energy simulation model (AKWarm).
The team plans to use ARIS data from 2008-2011 to provide a large sample size and ensure that
sufficient data is available for mobile homes and multifamily home. AKWarm uses average
climatic data as the basis of the end use energy estimates (i.e., the end use results are already
weather-normalized), so no further weather normalization is required.
4.5.2 Domestic Hot Water (DHW)
The team plans to use existing DHW energy end use data in the ARIS database as the primary
source of DHW end use data. If DHW data is not in ARIS, per the team’s understanding, this
approach will need to be updated.
4.5.3 Interior Lighting
The supplemental electrical end use survey obtains lamp quantity, wattage and weekly hours of
operation data for each primary lamp technology (incandescent, halogen, CFL, fluorescent, and
LED). The Unit Energy Consumption (UEC) is calculated from the following formula:
𝑈𝐸𝐶𝑖𝑛𝑡𝑒𝑟𝑖𝑜𝑟 𝑙𝑖𝑔ℎ𝑡𝑖𝑛𝑔=�𝑇𝑜𝑡𝑎𝑙 𝐿𝑎𝑚𝑝 𝑊𝑎𝑡𝑡𝑠× ℎ𝑜𝑢𝑟𝑠𝑤𝑒𝑒𝑘× 52 𝑤𝑒𝑒𝑘𝑠/𝑦𝑒𝑎𝑟𝑙𝑖𝑔ℎ𝑡 𝑡𝑦𝑝𝑒
Where:
AEA End Use Study- Methodology Page | 23
September 1, 2011
Brian Saylor &
Associates
UECinterior lighting = unit energy consumption (kWh/year)
Total Lamp Watts = total lamp wattage for each lamp type, including lamp and ballast
wattage, per table Table 5. This accounts for the added power consumed by the
ballast, for lamps typically powered by non-integral ballasts.
Reported Lamp Watts = lamp wattage reported in survey
Hours/week = average weekly hours of lighting use. Because the survey will be
conducted close to the equinox, we believe that the reported weekly hours will
be a good representation of average annual hours of operation.
Table 7: Residential Lighting Fixture Wattage
Lamp Type Total Lamp Watts
Incandescent Reported Lamp Watts
Halogen Reported Lamp Watts
CFL Reported Lamp Watts
Fluorescent Reported Lamp Watts
LED Reported Lamp Watts + 6 Watts/Lamp Typical Ballast Power9
HID Reported Lamp Watts + 6 Watts/Lamp Typical Ballast Power
Other Reported Lamp Watts
Questions about lighting controls are also asked in the survey. These items are informational
purposes only, and do not directly factor into the UEC calculations.
4.5.4 Exterior Lighting
Exterior lighting calculations are nearly identical to interior lighting calculations:
𝑈𝐸𝐶𝑒𝑥𝑡𝑒𝑟𝑖𝑜𝑟 𝑙𝑖𝑔ℎ𝑡𝑖𝑛𝑔=�𝑇𝑜𝑡𝑎𝑙 𝐿𝑎𝑚𝑝 𝑊𝑎𝑡𝑡𝑠× ℎ𝑜𝑢𝑟𝑠𝑤𝑒𝑒𝑘× 52 𝑤𝑒𝑒𝑘𝑠/𝑦𝑒𝑎𝑟𝑙𝑖𝑔ℎ𝑡 𝑡𝑦𝑝𝑒
Where:
UECexterior lighting = unit energy consumption (kWh/year)
Total Lamp Watts = total lamp wattage for each lamp type, including lamp and ballast
wattage, per table Table 5. This accounts for the added power consumed by the
ballast, for lamps typically powered by non-integral ballasts.
Reported Lamp Watts = lamp wattage reported in survey
Hours/week = average weekly hours of lighting use. Because the survey will be
conducted close to the equinox, we believe that the reported weekly hours will
be a good representation of average annual hours of operation.
9 Ballast power is variable depending on lamp size, ballast type (electronic vs. magnetic), ballast factor, and
other factors. Ballast power typically ranges between 3 to 13 watts per lamp. A typical value of 6
Watts/lamp is used in this analysis.
AEA End Use Study- Methodology Page | 24
September 1, 2011
Brian Saylor &
Associates
4.5.5 Major Appliances
4.5.5.1 Refrigerator
Refrigerator unit energy consumption is based on methodology develop by the Lawrence
Berkeley National Lab for the Home Energy Saver program10. The primary factors impacting
refrigeration energy use are vintage, size, and type. The unit energy consumption is calculated
as follows:
𝑈𝐸𝐶𝑟𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟=�365 × 𝐴𝑉𝐸𝐹# 𝑅𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟𝑠
Where:
UECrefrigerator = unit energy consumption (kWh/year)
AV = adjusted volume (ft3)
EF = Energy Factor (kWh/ft3/year), per Table 6, which is a lookup table factoring in
refrigerator type and year of manufacture
And the adjusted volume is defined as:
𝐴𝑉=𝑠𝑖𝑧𝑒× (𝑓𝑟𝑎𝑐+(1 −𝑓𝑟𝑎𝑐)× 1.63)
Where:
size = "Nominal" refrigerator/freezer volume (ft3)
frac = Fraction of refrigerator volume devoted to fresh-food storage (0 ≤ frac ≤ 1). For
side-by-side refrigerators, a fresh-food fraction of 0.6 is used, while all other
configurations use a fraction of 0.66.
10 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Section 3.3.1 Refrigerator Energy Consumption
AEA End Use Study- Methodology Page | 25
September 1, 2011
Brian Saylor &
Associates
Table 8: Shipment Weighted Energy Factors (EF) for Refrigerators11
Year General
Automatic defrost Manual
Defrost Side-by-Side Top Freezer
1972 3.84 3.57 3.56 6.69
1973 4.03 3.81 3.81 6.77
1974 4.22 4.05 4.06 6.85
1975 4.41 4.29 4.31 6.93
1976 4.60 4.53 4.56 7.01
1977 4.79 4.77 4.81 7.09
1978 4.96 5.02 4.75 7.18
1979 5.27 5.32 5.21 7.25
1980 5.59 5.62 5.67 7.32
1981 6.09 5.93 6.12 7.39
1982 6.12 6.02 6.30 7.69
1983 6.39 6.10 6.47 7.98
1984 6.57 6.12 6.75 8.19
1985 6.72 6.36 6.89 5.85
1986 6.83 6.49 6.95 6.14
1987 7.45 7.28 7.66 5.45
1988 7.60 7.45 7.83 5.09
1989 7.78 7.68 8.06 4.55
1990 8.15 7.78 8.51 4.84
1991 8.44 8.26 8.91 4.32
1992 8.80 8.69 9.36 3.50
1993 11.13 12.18 11.39 3.89
1994 11.19 12.45 11.37 4.13
1995 11.22 12.41 11.47 3.75
1996 11.22 12.08 11.48 4.21
1997 10.63 11.44 10.88 3.99
1998 10.5 11.30 10.74 3.94
1999 10.4 11.20 10.64 3.90
2000 11.11 11.96 11.37 4.17
2001 13.58 14.62 13.89 5.10
2002 15.17 16.33 15.52 5.69
2003 15.30 16.47 15.65 5.74
2004 15.70 16.89 16.06 5.88
2005 16.09 17.32 16.46 6.03
2006 16.49 17.74 16.87 6.18
2007 16.89 18.17 17.27 6.32
2008 17.28 18.59 17.68 6.47
2009 17.68 19.02 18.08 6.62
2010 18.07 19.45 18.49 6.77
11 Table data for years through 2003 is from Table 14 in “The Home Energy Saver: Documentation of
Calculation Methodology, Input Data, and Infrastructure”. 2011 data was taken from the ENERGY STAR
energy savings calculator
http://www.energystar.gov/ia/business/bulk_purchasing/bpsavings_calc/Consumer_Residential_Refrig_Sa
v_Calc.xls (accessed 6/27/11), and linearly interpolated between 2003. The Home Energy Saver weighting
factor methodology was used to develop EF data for different door styles..
AEA End Use Study- Methodology Page | 26
September 1, 2011
Brian Saylor &
Associates
2011 18.47 19.87 18.89 6.92
The following figure provides a graphic summary of the EF variation with time and equipment
type:
Figure 3: Refrigerator EF vs. vintage and type
4.5.5.2 Freezer
Freezer unit energy consumption is based on methodology developed by the Lawrence Berkeley
National Lab for the Home Energy Saver program12. The primary factors impacting freezer
energy use are vintage, size, and type. The unit energy consumption is calculated as follows:
𝑈𝐸𝐶𝑓𝑟𝑒𝑒𝑧𝑒𝑟=�365 × 𝐴𝑉𝐸𝐹# 𝑅𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟𝑠
Where:
UECrefrigerator = unit energy consumption (kWh/year)
AV = adjusted volume (ft3)
EF = Energy Factor (kWh/ft3/year), per Table 6, which is a lookup table factoring in
refrigerator type and year of manufacture
And the adjusted volume is defined as:
𝐴𝑉=𝑠𝑖𝑧𝑒× 1.73
12 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Section 3.3.2 Freezer Energy Consumption
0
5
10
15
20
25
19721974197619781980198219841986198819901992199419961998200020022004200620082010EFGeneral
Automatic
Defrost, Side-by-
Side
Automatic
Defrost, Top
Freezer
Manual Defrost
AEA End Use Study- Methodology Page | 27
September 1, 2011
Brian Saylor &
Associates
Where:
Size = "Nominal" refrigerator/freezer volume (ft3)
Table 9: Shipment Weighted Energy Factors for Freezers13
13 Data for years through 2003 is from Table 16 of “The Home Energy Saver: Documentation of Calculation
Methodology, Input Data, and Infrastructure”. Updated data for 2011 was obtained and linearly
extrapolated back to 2004.
Year General
Upright Design
Chest
Freezers
Automatic
Defrost
Manual
Defrost
1972 7.29 5.23 7.65 8.78
1973 7.72 5.43 7.93 9.27
1974 8.15 5.63 8.21 9.76
1975 8.58 5.83 8.49 10.25
1976 9.01 6.03 8.76 10.74
1977 9.44 6.23 9.03 11.23
1978 9.92 6.41 9.31 11.74
1979 10.39 6.95 9.84 11.77
1980 10.85 7.49 10.37 11.8
1981 11.13 8.03 10.89 11.82
1982 11.28 8.23 11.38 11.87
1983 11.36 8.43 11.44 11.91
1984 11.6 8.58 11.51 12.31
1985 11.55 9.5 11.56 12.04
1986 12.07 9.44 12.07 12.84
1987 12.93 9.57 12.6 14.41
1988 12.91 9.31 12.61 14.46
1989 13.89 9.47 13.86 15.48
1990 14.19 10.41 14.15 15.67
1991 14.17 10.43 13.95 15.92
1992 13.95 10.38 13.73 15.63
1993 17.38 13.65 17.3 19.43
1994 16.91 13.14 17.02 18.89
1995 16.57 13.16 16.95 18.28
1996 16.56 13.11 17.09 18.18
1997 16.41 12.99 16.94 18.02
1998 16.30 12.90 16.82 17.89
1999 16.16 12.79 16.68 17.74
2000 15.93 12.61 16.44 17.49
2001 17.38 13.76 17.94 19.08
2002 17.83 14.12 18.40 19.57
2003 17.06 13.51 17.61 18.73
2004 17.94 14.21 18.51 19.70
2005 18.82 14.91 19.42 20.66
2006 19.70 15.60 20.33 21.63
2007 20.58 16.30 21.24 22.60
AEA End Use Study- Methodology Page | 28
September 1, 2011
Brian Saylor &
Associates
4.5.5.3 Dishwasher
Dishwasher unit energy consumption is based on data and analysis conducted by the Florida Solar
Energy Center14. The primary factors impacting dishwasher energy use are vintage, the average
number of loads washed, water heater type, and whether the dishwasher is ENERGY STAR® rated.
In 1993, average dishwasher energy use was ~2.6 kWh and 10 gallons of water per load. In 2003,
the U.S. DOE established the first dishwasher energy efficiency standards, requiring a minimum
dishwasher energy use of 2.17 kWh/load (Energy factor of 0.46 cycles/kWh) for a standard size
dishwasher in the “normal” cycle. By 2004, the ‘average’ energy use was 1.8 kWh and 6 gallons
of water per load. An ENERGY STAR dishwasher uses ~1.8 kWh/load, and best available
dishwashers use just under 1 kWh/load. Standby losses for control electronics are reported to
be about 2 watts (~17 kWh/yr). Labeled annual energy use of dishwashers is based on a typical
household dishwasher usage of 215 dishwasher loads each year, or 4.1 loads/week15. This is
used as the default value or imputed value where data is unavailable.
The unit energy consumption is calculated as follows:
𝑈𝐸𝐶𝑑𝑖𝑠ℎ𝑤𝑎𝑠ℎ𝑒𝑟=𝐿𝐸× 𝐿𝑜𝑎𝑑𝑠× 52+SE
Where:
UECdishwasher = unit energy consumption (kWh/year)
LE = Average dishwasher energy use per load (kWh/load), per, which is a function of
vintage and type
SE = Standby energy for controls, etc. ~ 17 kWh/year
Table 10: Dishwasher-load energy (LE) vs. vintage and type
Vintage ENERGY STAR rated? LE (kWH/load)
Pre-2003 n/a (no rating) 2.6
2004 and later Standard 2.17
ENERGY STAR Rated 1.8
14 Florida Solar Energy Center (FSEC), “How Energy Efficient are Modern Dishwashers?” 2008,
http://www.fsec.ucf.edu/en/publications/pdf/FSEC-CR-1772-08.pdf. 15 Florida Solar Energy Center (FSEC), “How Energy Efficient are Modern Dishwashers?” 2008,
http://www.fsec.ucf.edu/en/publications/pdf/FSEC-CR-1772-08.pdf.
2008 21.46 17.00 22.15 23.56
2009 22.34 17.69 23.05 24.53
2010 23.22 18.39 23.96 25.50
2011 24.10 19.09 24.87 26.46
AEA End Use Study- Methodology Page | 29
September 1, 2011
Brian Saylor &
Associates
4.5.5.4 Clothes Washer and Clothes Dryer
Clothes washers consume significant energy and water nationwide. Newer front loading ENERGY
STAR rated washers can save over 50% of the water of a standard top-loading washer. This
translates into significant water heating savings. Furthermore, front loading ENERGY STAR
washers spin the clothes faster and remove more water that standard top loading washers. This
significantly reduces dryer energy use. An ENERGY STAR standard for clothes washers was
released in 1997. ENERGY STAR clothes washers are about 30% more efficient than standard
models that simply meet the federal minimum standard for energy efficiency. There is no
published data on standby energy use; this analysis assumes that standby losses for controls will
be similar to dishwashers.
Clothes washer and dryer unit energy consumption is based on the U.S. DOE’s ENERGY STAR
clothes washing methodology and performance data16. The unit energy consumption is
calculated as follows:
𝑈𝐸𝐶𝑐𝑙𝑜𝑡ℎ𝑒𝑠 𝑤𝑎𝑠ℎ𝑒𝑟 & 𝐷𝑟𝑦𝑒𝑟=𝐿𝐸× 𝐿𝑜𝑎𝑑𝑠× 52 +2 × 𝑆𝐸
Where:
UECdishwasher = unit energy consumption (kWh/year)
LE = Average dishwasher energy use per load (kWh/load), per Table 9, which is a
function of vintage and type.
SE = Standby energy for controls, etc. ~ 17 kWh/year, multiplied by two to represent
standby losses for the clothes washer and dryer.
Table 11: Clothes washer/dryer energy use verses type and fuel
Type
Water
Heating
Fuel
Dryer
Fuel
Electricity Use
per Load
(kWh/load)
Fuel Use per
Load
(Therms/load)
Front Loading (or ENERGY STAR) Electric Electric 1.44 0
Front Loading (or ENERGY STAR) Electric Fuel 0.49 0.03
Front Loading (or EnergyStar
ENERGY STAR) Electric None 0.49 0
Front Loading (or ENERGY STAR) Gas Electric 0.15 0.05
Front Loading (or ENERGY STAR) Gas Fuel 0.99 0.02
Front Loading (or ENERGY STAR) Gas None 0.15 0.02
Standard / Top Loading Electric Electric 2.01 0
16 General background information:
http://www.energystar.gov/index.cfm?fuseaction=find_a_product.showProductGroup&pgw_code=CW
Energy calculator:
http://www.energystar.gov/ia/business/bulk_purchasing/bpsavings_calc/CalculatorConsumerClothesWash
er.xls
AEA End Use Study- Methodology Page | 30
September 1, 2011
Brian Saylor &
Associates
Standard / Top Loading Electric Fuel 0.85 0.04
Standard / Top Loading Electric None 0.85 0
Standard / Top Loading Gas Electric 0.21 0.08
Standard / Top Loading Gas Fuel 1.24 0.04
Standard / Top Loading Gas None 0.21 0.04
4.5.6 Primary Cooking
4.5.6.1 Stove and Oven
Stove and oven energy consumption is primarily a function of hours of use and fuel type. Stove
unit energy consumption is based on methodology developed by the Lawrence Berkeley
National Lab for the Home Energy Saver program17. The unit energy consumption for electric
stoves is calculated as follows:
𝑈𝐸𝐶𝑠𝑡𝑜𝑣𝑒 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐=𝑝𝑜𝑤𝑒𝑟× 𝑢𝑠𝑎𝑔𝑒𝑑𝑎𝑦× 365
Where:
UECstove electric = Annual energy consumption in kWh
power = energy consumption rate of stove; for this study, a typical value of 1 kW is used
usageday = hours of use per day for all burners combined
The unit energy consumption for gas stoves is calculated as follows:
𝑈𝐸𝐶𝑠𝑡𝑜𝑣𝑒 𝑔𝑎𝑠=�𝑟𝑎𝑡𝑒𝑠𝑡𝑜𝑣𝑒 𝑔𝑎𝑠× 𝑢𝑠𝑎𝑔𝑒𝑑𝑎𝑦× 365�+𝑝𝑖𝑙𝑜𝑡𝐿𝑖𝑔ℎ𝑡
Where:
UECstove gas = Annual energy consumption in therms
ratestove gas = energy consumed by stove; a typical value of 0.09 therms/hr is used here
usageday = hours of use per day for all burners combined
pilotLight = energy consumed by the pilot light, which is assumed to be 17 therms/year
Oven unit energy consumption follows a similar methodology developed by the Lawrence
Berkeley National Lab for the Home Energy Saver program18. For electric ovens, the unit energy
consumption is calculated as follows:
17 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Equations 21 and 22. 18 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Equations 23 and 24.
AEA End Use Study- Methodology Page | 31
September 1, 2011
Brian Saylor &
Associates
𝑈𝐸𝐶𝑔𝑎𝑠 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐=𝑝𝑜𝑤𝑒𝑟× 𝑢𝑠𝑎𝑔𝑒𝑤𝑒𝑒𝑘× 52
Where:
UECoven electric = Annual energy consumption in kWh
power = energy consumption rate of oven; a typical value of 2.3 kW is used here
usageweek = hours of use per week for the oven
The unit energy consumption for gas ovens is calculated as follows:
𝑈𝐸𝐶𝑜𝑣𝑒𝑛 𝑔𝑎𝑠=�𝑟𝑎𝑡𝑒𝑜𝑣𝑒𝑛 𝑔𝑎𝑠× 𝑢𝑠𝑎𝑔𝑒𝑤𝑒𝑒𝑘× 52�+𝑝𝑖𝑙𝑜𝑡𝐿𝑖𝑔ℎ𝑡
Where:
UECoven gas = Annual energy consumption in therms
rateoven gas = energy consumed by oven; a typical value of 0.11 therms/hour is used here
usageweek = hours of use per week for the oven
pilotLight = energy consumed by the pilot light, which is assumed to be 17 therms/year
4.5.6.2 Microwave
Microwave unit energy consumption is based on methodology developed by the Lawrence
Berkeley National Lab for the Home Energy Saver program19. The calculation is outlined in the
following equation:
𝑈𝐸𝐶𝑚𝑖𝑐𝑟𝑜𝑤𝑎𝑣𝑒=𝐴𝑐𝑡𝑖𝑣𝑒𝑃𝑜𝑤𝑒𝑟+𝑆𝑡𝑎𝑛𝑑𝑏𝑦𝑃𝑜𝑤𝑒𝑟1000
Where:
UECmicrowave = annual energy consumed by the microwave (kWh)
ActivePower = power draw in active mode × hours per year microwave is on
StandbyPower = (8760 – hours/year microwave is on) × power draw in standby mode
For this study, typical microwave power of 1 kW in active mode, and 2.8 W in standby mode is
used20.
19 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. 20 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. Table
21.
AEA End Use Study- Methodology Page | 32
September 1, 2011
Brian Saylor &
Associates
4.5.7 Other Kitchen Equipment
4.5.7.1 Coffee Maker
Coffee maker unit energy consumption is based on methodology developed by the Lawrence
Berkeley National Lab for the Home Energy Saver program21. The calculation is outlined in the
following equation:
𝑈𝐸𝐶𝑐𝑜𝑓𝑓𝑒𝑒 𝑚𝑎𝑘𝑒𝑟=𝐴𝑐𝑡𝑖𝑣𝑒𝑃𝑜𝑤𝑒𝑟+𝑆𝑡𝑎𝑛𝑑𝑏𝑦𝑃𝑜𝑤𝑒𝑟1000
Where:
UECcoffee maker = annual energy consumed by the coffee maker (kWh)
ActivePower = ((P1 * P1Usage) + (P2 * P2Usage) +... + (Pn * PnUsage))
Pn = the energy consumed at various power levels (e.g. active use, on but idle etc.), per
Table 10, which is a function of coffee maker typeTable 10: Coffee Maker Energy Use by
Type
PnUsage = hours per year the coffee maker spends at this power rate
StandbyPower = (8760 – Sum(all active Usages)) * StandbyRate
StandbyRate = energy consumed in standby mode, per Table 10, which is a function of
coffee maker type (Table 8).
Table 12: Coffee Maker Energy Use by Type22
Appliance Active Power
Consumption (W)
Standby Power
Consumption (W)
Coffee Maker: Drip (Brew) 1500 1
Coffee Maker: Drip (Warm) 70 0
Coffee Maker: Percolator (Brew) 600 0
Coffee Maker: Percolator (Warm) 80 0
Espresso Maker 360 0
21 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Equation 25. 22 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. Table
21.
AEA End Use Study- Methodology Page | 33
September 1, 2011
Brian Saylor &
Associates
4.5.7.2 Miscellaneous Kitchen Appliances
Miscellaneous kitchen appliances may be included in this study, if desired. If survey
administration time permits, the following appliances may be included:
• Broiler
• Deep fryer
• Electric fry pan
• Slow cooker
• Toaster
• Toaster oven
Unit energy consumption for miscellaneous kitchen appliances is based on methodology
developed by the Lawrence Berkeley National Lab for the Home Energy Saver program23. The
calculation is outlined in the following equation:
𝑈𝐸𝐶=𝐴𝑐𝑡𝑖𝑣𝑒𝑃𝑜𝑤𝑒𝑟+𝑆𝑡𝑎𝑛𝑑𝑏𝑦𝑃𝑜𝑤𝑒𝑟1000
Where:
UEC = annual energy consumed by the equipment (kWh)
ActivePower = ((P1 * P1Usage) + (P2 * P2Usage) +... + (Pn * PnUsage))
Pn = the energy consumed at various power levels (e.g. active use, on but idle etc.), per
Table 11
PnUsage = hours per year the equipment spends at this power rate, per Table 11
StandbyPower = (8760 – Sum(all active Usages)) * StandbyRate
StandbyRate = energy consumed in standby mode, per Table 11
Table 13: Typical energy use patterns for miscellaneous kitchen appliances24
23 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Equation 25. 24 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. Table
21.
AEA End Use Study- Methodology Page | 34
September 1, 2011
Brian Saylor &
Associates
4.5.8 Information Technology
Information technology equipment in this study includes:
• Desktop computer
• Monitor
• Laptop computer
• Printer/ Multi-Function Device (MFD)
• Router/DSL/Cable Modern
• Fax Machine
• Home Copy Machine
Unit energy consumption (UEC) for information technology equipment is based on methodology
developed by the Lawrence Berkeley National Lab for the Home Energy Saver program, as
described previously. The power consumption data and typical usage patterns are summarized
in Table 12.
Appliance
Estimated
Wattage
Active
Usage
(hours/
Year)
Active
consumption
(kWh/year)
Standby
Wattage
Standby
Usage
(hour/yr)
Standby
kWh/year Total kWh
Broilers 1400 1 hour / week 52 73 0 8708 0 73
Deep Fryer 1000 23 minutes / week 20 20 0 8740 0 20
Electric Fry Pans 1000 14 hours / month 162 162 0 8598 0 162
Slow Cookers 200 13 hours / week 693 139 0 8067 0 139
Toaster 1100 6 minutes / day 37 40 0 8724 0 40
Toaster Oven -Toasting 460 4 minutes / day 25 12 0 8735 0 12
Toaster Oven -Oven 1500 23 minutes / day 140 210 0 8620 0 210
Typical Use
AEA End Use Study- Methodology Page | 35
September 1, 2011
Brian Saylor &
Associates
Table 14: Typical energy use patterns for information technology equipment25
4.5.9 Entertainment
Entertainment equipment in this survey includes:
• TV
• Gaming console
• Digital video recorder (DVR) or TIVO
• VCR/DVD
• Cable box
• Music playing systems
Unit energy consumption for entertainment equipment is based on methodology developed by
the Lawrence Berkeley National Lab for the Home Energy Saver program, as described
previously. The power consumption data and typical usage patterns are summarized in the
following table.
25 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. Table
21.
Appliance
Estimated
Wattage
Active
Usage
(hours/
Year)
Active
consumption
(kWh/year)
Standby
Wattage
Standby
Usage
(hour/yr)
Standby
kWh/year Total kWh
Desktop 68 5 hours / day 1825 124 1.2 6935 8 132
Monitor 84 5 hours / day 1825 153 2 6760 14 167
Laptop Charger 0 0 0 0 4.5 8760 39 39
Printers (Inkjet) 13 1 hour / week 52 1 4.2 8708 37 37
Printers (Laser) 250 1 hour / week 52 13 4.2 8708 37 50
Router/DSL/Cable Modem 6 5 hours / day 1825 11 2 6935 14 25
Fax Machine 800 30 minutes / day 183 146 5.1 8578 44 190
Home Copy Machine 175 4 minutes / day 24 4 30 8736 131 135
Typical Use
AEA End Use Study- Methodology Page | 36
September 1, 2011
Brian Saylor &
Associates
Table 15: Typical energy use patterns for entertainment equipment26
4.5.10 Miscellaneous Electrical Appliances
Miscellaneous electrical appliances in this survey include:
• Transformers
• Garage door opener
• Electric water bed
• Hot tub
• Water well pumps
• Sewage lift pump
• Sump pump
• Head bolt/engine block heater
• Heat trace
• Electric space heater
• Decorative lights
• Grow lights
• Electric vehicles
26 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf. Table
21.
Appliance
Estimated
Wattage
Active
Usage
(hours/
Year)
Active
consumption
(kWh/year)
Standby
Wattage
Standby
Usage
(hour/yr)
Standby
kWh/year Total kWh
TV (CRT -Projection) 225 2 hours / day 730 164 6.4 8030 51 216
TV (CRT) 60 2 hours / day 730 44 6.4 8030 51 95
TV (DLP) 175 2 hours / day 730 128 6.4 8030 51 179
TV (LCD) 150 2 hours / day 730 110 6.4 8030 51 161
TV (Plasma) 300 2 hours / day 730 219 6.4 8030 51 270
Gaming Console 20 1 hour / day 365 7 0 8395 0 7
Receiver 28 2 hours / week 104 3 2.8 8656 24 27
Satellite stations (standby
losses)25 2 hours / week 104 3 15 8656 130 132
VCRs 18 2 hours / week 104 2 5.3 8656 46 48
DVD Player 16 4 hours / week 208 3 5.5 8552 14 17
Cable Boxes (standby
losses)140 90 minutes / day 548 77 11.6 8213 95 172
CD Player 7 30 minutes / week 26 0.2 3.7 8734 19 19
Boom Box 8 30 minutes / week 26 0.2 5.2 8734 45 46
Tape Player 8 2 hours / week 104 1 1 8656 9 9
Typical Use
AEA End Use Study- Methodology Page | 37
September 1, 2011
Brian Saylor &
Associates
• Other appliances
Unit energy consumption for miscellaneous equipment is calculated from the following
equation:
𝑈𝐸𝐶=(𝑃𝑎𝑐𝑡𝑖𝑣𝑒× 𝑇𝑎𝑐𝑡𝑖𝑣𝑒)+(𝑃𝑠𝑡𝑎𝑛𝑑𝑏𝑦× 𝑇𝑠𝑡𝑎𝑛𝑑𝑏𝑦)
Where:
UEC = annual energy consumed by the equipment (kWh/year or kBTU/year)
Pactive= power in active mode (kW)
Tactive= time in active mode (hours)
Pstandby= power in standby mode (kW)
Tstandby= time in standby mode (hours)
AEA End Use Study- Methodology Page | 38
September 1, 2011
Brian Saylor &
Associates
5. Railbelt & Southeast Alaska Non-Residential
There is little existing data on nonresidential Railbelt and Southeast Alaska and user energy
consumption. This section describes how the research team determined the categorization of
non-residential facilities, estimated sample sizes, and collected the energy consumption data.
5.1 Municipal/Borough Parcel Data
o Municipalities and boroughs maintain detailed property records at the parcel level for
taxation and other purposes. The level of detail varies by jurisdiction, with some
jurisdictions maintaining detailed data on each parcel, and others collecting minimal
data. Anchorage, for example, maintains very detailed parcel data, including information
on building size, construction, etc.
o The availability of the parcel data also varies by jurisdiction. Many jurisdictions maintain
this data in digital format, although some jurisdictions store the data in a paper-based
filing system.
o The following table summarizes the digital parcel data27 that is available for this project,
with a qualitative evaluation of its level of detail and currency.
The available parcel data was obtained from each jurisdiction in aggregated form from Ingens
(www.ingens.com), a commercial provider of public information in Alaska.
Table 16 Summary of Alaska Parcel Data
Location Parcel Data Availability Data Detailed Current
Anchorage Y Y-Very Y Fairbanks Y N – TBD* Y Juneau Y Y - Moderate Y Kenai Y ? Y Mat-Su Y ? Y Ketchikan Y Y – “Decent” Y Kodiak Y Y – “Decent” Y Unavailable: Haines, Cordova, Everywhere else
5.2 Sample Frame
Parcel data from Alaskan boroughs were occasionally incomplete. While the number of buildings
was known, the distribution of building types was missing from select parcel data sets. The non-
residential sampling frame was based on parcel data from the municipality of Anchorage that was
collapsed into nine building types. The detailed Anchorage parcel data was used in the
development of the collapsed building type categories. These categories were prorated and
applied to summary parcel data from Fairbanks and the Boroughs in the southeast region.
27 Parcel data maintained in paper archives would require significantly time and resources to process and is
not an option for this project
AEA End Use Study- Methodology Page | 39
September 1, 2011
Brian Saylor &
Associates
Table 17. Preliminary Sampling Frame
Anchorage Percent Distribution of Building Types
Building Type Building Subtype Anchorage Parcel
Data
Building Type
Totals
Building Type
Percent
Food Services Restaurant 133 214 4.5%
Food Stand 0
Fast Food 48
Bar/Lounge 33
Night Club 0
Warehouse and
Storage
Hangar 173 1704 35.9%
Refrigerated Warehouse 0
Warehouse--General 1531
Institutional Education 103 277 5.8%
Public Assembly 0
Public Order & Safety 0
Religious Worship 174
Library 0
Cemetery 0
Institutional--Other 0
Health Care Health Care--Inpatient 0 69 1.5%
Health Care--Outpatient 69
Nursing Home 0
Lodging Hotel/Motel 74 113 2.4%
Dormitories 39
Home for elderly 0
Lodging--Other 0
Office 701 701 14.8%
Mercantile and Retail Strip Malls 56 681 14.4%
Enclosed Malls 201
Food Retail 35
Retail--Other 389
Service Cinema/Theater 0 260 5.5%
Automotive Oriented Services 232
Spa/Salon 0
Communication 0
Service--other 28
Other Parking 0 722 15.2%
Sports facilities 32
Multipurpose 0
Miscellaneous--Other 690
Totals 4741 4741 100.0%
AEA End Use Study- Methodology Page | 40
September 1, 2011
Brian Saylor &
Associates
5.3 Preliminary Sample Size Estimates
Proration of parcels in Southeast and Railbelt Alaska allowed for the estimation of sample sizes
for Railbelt and Southeast Alaska. The basic sample size parameters used in this study include a
margin of 15-18.4% (+/- 6) a confidence interval of 90%, and include a response distribution of
30%. These parameters are based upon balancing the precision with available budgetary
resources and the complexity of collecting non-residential data.
Table 18: Railbelt Preliminary Sample Size Estimation
Railbelt
Railbelt Mat-Su Kenai Valdez/O
ther
Railbelt
Fairbanks Denali/SE
FBKS
Total
Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food Service 351 14 116 5 112 4 12 0 116 5 20 1 727 29 15%
Warehouse 2797 10 807 3 892 3 93 0 922 3 75 1 5586 20 18,4%
Institutional 455 12 335 9 145 4 15 1 150 4 12 0 1112 30 15%
Health Care 113 12 80 8 36 4 4 0 37 4 3 0 273 28 15%
Lodging 185 10 193 11 59 3 6 1 61 3 20 1 524 29 15%
office 1,151 17 44 1 367 5 38 1 379 6 27 0 2006 30 15%
Mercantile/
retail
1,118 15 308 4 356 5 37 1 368 5 30 0 2217 30 15%
Service 427 11 414 11 136 4 14 0 141 4 11 0 1143 30 15%
Other 1,185 17 71 1 378 5 39 1 390 6 25 0 2088 30 15%
Total 7,782 119 2368 52 2,481 38 259 5 2,564 39 223 5 15,676 256 5.1%
AEA End Use Study- Methodology Page | 29
September 1, 2011
Brian Saylor &
Associates
Table 19: Southeast Alaska Preliminary Sample Size
Southeast Alaska
Juneau
Kodiak/Cordov
a
Ketchika
n
Sitk
a
Other
Southeast
Total
Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food
Service
49 12 17 4 11 3 8 2 14 3 99 24 15.0
%
Warehouse 386 10 134 3 90 2 62 2 109 3 781 20 18.2
%
Institutiona
l
63 14 22 5 15 3 3 1 6 1 109 24 15.0
%
Health Care 16 8 6 3 4 2 3 1 5 3 34 17 15.0
%
Lodging 26 8 9 3 6 2 10 3 20 6 71 22 15.0
%
office 159 15 55 5 37 3 25 2 35 3 311 28 15.0
%
Mercantile/
retail
154 14 54 5 36 3 25 2 43 4 312 28 15.0
%
Service 59 12 21 4 14 3 9 2 17 4 120 25 15.0
%
Other 164 15 57 5 38 3 26 2 40 3 325 28 15.0
%
Total 1,07
6
10
8
374 37 251 25 17
1
17 289 30 2,16
2
21
6
5.2%
AEA End Use Study- Methodology Page | 30
September 1, 2011
Brian Saylor &
Associates
5.4 Preliminary Sample Size by climate zone
If the climate zone approach is chosen, proration of parcels by climate zone allowed for the
estimation of sample sizes for climate zones 6, 7, & 8. The basic sample size parameters used in
this study include a margin of 20% (+/- 6) a confidence interval of 90%, and include a response
distribution of 30%. These parameters are based upon balancing the precision with available
budgetary resources and the complexity of collecting non-residential data.
Table 20 Preliminary Sample Size by Climate Zone
Zone 6 Zone 7 Zone 8 Total
Location Units Sample MOE Units Sample MOE' Units Sample MOE Units Sample Bldg Type
Food Service 82 15 20.0% 612 17 20.0% 136 16 20.0% 830 48
Warehouse 647 17 20.0% 4728 17 20.0% 997 17 20.0% 6372 51
Institutional 87 15 20.0% 979 17 20.0% 162 16 20.0% 1228 48
Health Care 28 11 20.0% 243 16 20.0% 40 13 20.0% 311 40
Lodging 62 14 20.0% 456 17 20.0% 81 15 20.0% 599 46
office 256 16 20.0% 1663 17 20.0% 406 17 20.0% 2325 50
Mercantile/ retail 258 16 20.0% 1878 17 20.0% 398 17 20.0% 2534 50
Service 99 15 20.0% 1017 17 20.0% 152 16 20.0% 1268 48
Other 268 16 20.0% 1737 17 20.0% 415 17 20.0% 2420 50
Total 1,787 135 13,313 152 2,787 144 17,887 431
5.5 Available Data
The following data is available to the team for the non-residential methodologies:
• AEA Commercial Energy Audits
o ~130 audits to be done during study period
• DOT Energy Audits of State Buildings
o Extent of data availability being investigated
• Retro-Energy Assessment for Loans (REAL)
o Public buildings (municipal buildings, schools)
o Extent of data availability being investigated
• ARIS
o Some non-residential data reported. Extent of non-residential reporting in ARIS
to be determined when researchers have the chance to review the actual
database.
AEA End Use Study- Methodology Page | 31
September 1, 2011
Brian Saylor &
Associates
• AK Warm Commercial
o Data inputs are available
5.6 Unique Stratification
The stratification of the sample will be based on a proportional representation method
combined with a rationalization of the anticipated end use calculations. Each of the commercial
use classifications will be proportionally represented, as defined by the overall sample frame.
One exception would be if classifications are similar enough to not demonstrate variation, thus
justifying limiting those structures to a smaller number within the sample.
For example, in a proportional sample, the commercial use classification of warehouse may
represent approximately 15 percent of the commercial structures in the Railbelt. Due to the
light energy use, building categories with a wide variance in energy use will be overrepresented
in the sample as compared to building categories with fairly tight distributions of energy use.
During the analysis phase, we will weight the data relative to their appropriate size, as required
by proportional representation sampling.
This modified approach will also allow us to collect additional data for those structures which
are under-represented in a proportional sample.
5.7 Survey Design
Data collection for non residential buildings will combine web-based data collection, telephone
surveys, and, when needed, facility walk-throughs. Respondents will be contacted and offered
an incentive to participate in the non-residential data collection effort. Participants who agree
will be asked to furnish basic energy use data through the Internet using web-based data entry
screens. Trained survey researchers will contact participants to conduct phone survey
interviews; responses will be documented using CATI technology. Certain types of buildings will
require additional on-sight data collection.
A monetary incentive will be offered to participants to encourage participation. The incentive
will be sent once the requested information has been provided. The incentive will be calculated
by applying a fair hourly labor rate to the required participation time.
As requested by AEA, the survey team will try to solicit permissions from respondents to access
utility records. This information will be used to verify end-use energy consumption calculations.
As in the residential methodology, not all utility records will be requested- up to 10% of the
sample frame could be requested for verification purposes.
5.8 Survey Pre-Test, Validation, Reliability, and Vetting
The survey will be pre-tested to ensure reliability and validity. One possible method of reducing
respondent error involves detailed conversations with potential respondents regarding their
understanding of the questions being asked. This technique, called cognitive interviewing, is
AEA End Use Study- Methodology Page | 32
September 1, 2011
Brian Saylor &
Associates
used to evaluate sources of response error in survey questionnaires. It was developed during
the 1980s through an interdisciplinary effort by survey methodologists and psychologists28.
Consideration of this technique was spurred by the agreement among the research team that
some respondents may not have a working knowledge or understanding of end-use energy
questions. This technique may assist the survey or is in better understanding the extent of
potential respondent errors.
The WHPacific team will conduct as many survey pretests as necessary. The WHPacific team will
conduct a pretest of survey instruments in each of the desired target areas, including Railbelt
and Southeast, as well as with each non-residential use classification.
The one exception will be high-rise buildings which require an onsite assessment. The results of
the pre-test will be used to refine the survey and add additional questions as needed to address
variation and other factors.
5.8.1 Training
The training of interviewers and help desk staff will be completed by the WHPacific team.
Research and survey staff will be trained for the onsite, Internet, and phone survey portion of
the data collection. The training will include instruction survey content and logic, question
delivery protocol based upon built-in skip and rotation patterns written especially for the study
during the design phase. Walk-throughs will also be conducted to enhance the conceptual
understanding of commercial structures.
5.9 Data Collection Tools and Framework
The survey data will be collected using appropriate data collection tools, including, but not
limited to, WEB/CATI29 surveys and onsite visits. The WHPacific team will refine and modify
the programming for the CATI system to accomplish all employed methodologies. The survey
will then be programmed for the Internet using HTML and PHP coding and include graphical
illustrations of computers, appliances, and other end use equipment. These graphical
representations will assist with methodological compliance.
The data collection process will vary based upon respondent preference and technical
capabilities. It will be possible for the respondent to speak to an interviewer on a headset/cell
phone while walking through their facility. This technology also allows interviewers to pause a
survey, collect data over multiple phone conversations, or integrate phone data with data
collected via the Internet or onsite. Due to the anticipated length of the survey, the data
collection process may be conducted in a multi-phased approach, using multiple methods.
28 Caspar, RA, Lessler, JT and Willis, GB, Cognitive Interviewing: A “How To” Guide, based on a short course
presented at the American Statistical Association , Research Triangle Institute, 1999 29 CATI is a generic term used in the research industry and means Computer Aided Telephone
Interview. The CATI system has become an important tool for effective research and surveying
systems. There are several vendors of CATI technology available on the market today.
AEA End Use Study- Methodology Page | 33
September 1, 2011
Brian Saylor &
Associates
Quota controls will be used to ensure the representativeness and methodological compliance of
the data collection process. Call summary reports provided by the survey system will allow the
team’s survey supervisors and analysts to monitor the progress of the data collection process.
Interview logic will be embedded in all the survey instruments to control the manner in which
questions and answer choices are presented to each respondent. This will entail the use of
question skip algorithms, the randomization of question sequences, and the presentation order
of answer-choices. Respondents will only answer questions relevant to their structure, etc.
An initial call will be made to building owners securing their participation. We will identify,
secure permission and gain an understanding as to whom will complete the survey (e.g. building
owner or property manager). Survey respondents will be offered a CATI, web based or a hybrid
CATI/web survey. The hybrid approach will allow each respondent to view detailed graphical
images while simultaneously speaking with a trained interviewer who can further clarify the
data required. This protocol is graphically illustrated in Figure 7.
AEA End Use Study- Methodology Page | 34
September 1, 2011
Brian Saylor &
Associates
Figure 7. Data collection Process for Non-residential Southeast and Railbelt Alaska
Please refer to the Appendix for the complete data collection instrument.
5.9.1 Initial Data Collection & Review for Quality Control
An initial analysis will be conducted on the first 10 completed surveys to identify potential
programming or respondent issues. After the initial assessment, the data will be reviewed on a
daily basis to for quota controls and interviewer feedback.
5.10 End Use Analysis
The energy end use estimates will be based on engineering calculations and building energy
modeling. The WHPacific team will compile the survey data collected from the interviews into a
format for further analysis and commercial use classifications. This includes compiling the
necessary frequency distributions, descriptive and inferential statistics, and variable cross
tabulations. Frequency distributions show categories of responses to questions by the actual
number of responses and percentage of the total.
The WHPacific team will combine frequency tables to describe two pieces of data related in
some way (called cross tabulations). They will test variables of interest for correlation with
AEA End Use Study- Methodology Page | 35
September 1, 2011
Brian Saylor &
Associates
population demographic variables using cross tabulations. All relevant statistical tables and
findings will be summarized and used for end use calculations.
5.10.1 Heating Ventilation and Air-Conditioning (HVAC)
Building HVAC end uses will be analyzed through building energy simulation using AKWarm
Commercial. The survey has been designed to obtain a minimal set of inputs necessary for an
AKWarm simulation. The model will be built with less detail than would typically be required for
a full building energy audit, but should provide significantly more analytical rigor than the
simpler HVAC correlations used by many end use studies. The AKWarm Commercial Model is
relatively easy to use, addresses many Alaska-specific issues, and accommodates the higher-
level analysis required for the end use study, as compared to other building energy simulation
models (e.g. eQuest, DOE2.1, e+, etc.). As a point of comparison, the California Commercial End
use Study (CEUS) also used building energy simulation, but they had to develop a custom
software solution. Their budget and timing (several years) also permitted a more detailed level
of analysis than can be accommodated for this project.
Note that the team plans on using AKWarm Commercial to only analyze the HVAC and DHW end
uses. The other non-residential end uses will be analyzed outside of AKWarm using engineering
models. AK Warm has the capability to enter miscellaneous building electrical and thermal end
uses, but uses a more general approach where one must specify high-period and low-period
power consumption (e.g., “on” and “standby” power use), and specify the appropriate schedule.
This is better suited for a full building energy audit. For this end use study, the team will rely on
more detailed engineering calculations which allow the team to focus the survey questions
around key variables in the limited time available for each building.
To account for the interactive impacts that lighting and the other end uses have on building
HVAC loads, the results from the miscellaneous end use engineering calculations will be
aggregated to provide an average power consumption during building occupied (high period)
and unoccupied (low period) schedule.
To successfully employ this approach, the team will coordinate with AHFC and/or the AKWarm
developers on two key issues:
• First, based on conversations with the AKWarm developers, the team understands that
it is possible to create an AKWarm input file in XML format, and that the AKWarm
development team can provide a script to convert the XML file into the appropriate
binary format that AKWarm can read. The team plans to automate creation of the XML
file from the survey data to the greatest extent possible. The team will need to
coordinate with the AHFC and/or the AKWarm developers to obtain the XML format and
the binary conversion script.
• Second, the team also understands that AKWarm is able to output building energy end
uses by fuel type. For example, for a building that uses a natural gas boiler for primary
space heating, electric reheat, and a supplemental wood stove for heating, the team will
AEA End Use Study- Methodology Page | 36
September 1, 2011
Brian Saylor &
Associates
be able to obtain electric heating energy use (kWh/year), heating fuel use (kBTU/year),
and wood used for heating (kBTU/year). If this data is not available, the use of AKWarm
to analyze HVAC end uses will need to be re-evaluated.
5.10.2 Lighting
Lighting energy use calculations are based on the CBECS methodology. For interior lighting,
energy use is a factor of average lamp power per floorspace and average annual operating
hours. The interior lighting relies on survey data for the percentage floorspace lit by each lamp
type, and building operating hours. Typical values are assumed for average lamp system efficacy
(lumens per watt) for each lamp type, and recommended average illuminance levels by building
type. The exterior lighting portion assumes a fixed average power density per lamp type, by
exterior lighting application: exit signs, exterior architecture, parking, exterior signs, and exterior
landscaping. Average annual operating hours by building type are also assumed. Lighting UECs
are calculated from the following equations:
LightingInterior = OpHrs * SqFt * LPT
Where
OpHrs = annual operating hours (hrs),
SqFt = building floor space (ft2)
LPT =∑÷∗
bpeBuildingtyiLampType
iibLampLPWLampPIL
,,
,
Where
ILb = recommended lighting illumance levels by building type (lumens)
LampPi = percentage of floorspace lit by a lamp type (%), per Table 19
LampLPWi = average system efficacy, accounting for fixture efficiency and lumen
degradation over time (lumens per watt), per Table 18. The presence of
electronic ballasts is determined by the type of fluorescent lamp present,
T12 or T8.
Table 21: Lighting system efficacy (lumens/Watt) for various lamp types
Lamp Type Ballast Type Reflector
Lighting Efficacies
(lumens per watt)
Fluorescent
Magnetic Ballast (T12) No specular reflector 29.8
Electronic Ballast (T8) No specular reflector 43.7
Magnetic Ballast (T12) Specular reflector 37.3
AEA End Use Study- Methodology Page | 37
September 1, 2011
Brian Saylor &
Associates
Electronic Ballast (T8) Specular reflector 54.6
Halogen n/a 8.6
HID n/a 27
Incandescent n/a 4.9
CFL n/a 20.6
Other n/a 11.4
Table 22: Typical lighting illumination levels by building type
Building Type
Lighting Illumination Level
(Lumens/sqft)
Vacant 50
Office/ Professional 35
Laboratory 40
Warehouse non-refrigerated 30
Food sales 40
Public order/safety 30
Health care (outpatient) 50
Warehouse (refrigerated.) 30
Religious worship 20
Public assembly 20
Education 30
Food service 30
Health care (inpatient) 50
Skilled nursing 30
Hotel/ Motel/Dorm 20
Strip shopping 50
Enclosed shopping center/mall 50
Retail (excluding mall) 50
Service (excl. food) 50
Other 50
Exterior lighting unit energy consumption is calculated from the following formula:
LightingExterior = ∑∗
b type,Building i ,ghtingTypeExteriorLi ibPwrOpHrs
Where
OpHrsb = annual operating hours, by building type (hrs)
Pwri = weighted average wattage per square foot by exterior lighting category (W)
AEA End Use Study- Methodology Page | 38
September 1, 2011
Brian Saylor &
Associates
5.10.3 Office Equipment and Information Technology
The methodology for calculating unit energy consumption for office and IT equipment is
straightforward, and takes the general form of the following equation, which sums up
equipment energy consumption for each piece of equipment’s average on-time power use and
average off or standby power use:
𝑈𝐸𝐶𝑜𝑓𝑓𝑖𝑐𝑒 & 𝐼𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡=�(𝑃𝑜𝑛× 𝑇𝑜𝑛)𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡+�𝑃𝑠𝑡𝑎𝑛𝑑𝑦× 𝑇𝑠𝑡𝑎𝑛𝑑𝑦�
Where:
UEC = unit energy consumption (kWh/year)
Pon = Average equipment power during building occupied hours
Ton = Time on, hours per year. Unless specified otherwise, this is based on the annual
hours of operation for the building.
Pstandby = Average equipment power during building unoccupied hours
Ton = Annual building unoccupied hours
Building occupancy schedule data is obtained from the survey. There are four primary sources
for equipment and IT energy consumption that have been used. This includes the 2003 CBECS
methodology and data tables30, energy savings calculators developed by the Federal Energy
Management Program (FEMP)31, ENERGY STAR savings calculators and background data, data
from the U.S. DOE’s Appliances & Commercial Equipment Standards program32, and the DOE
study “Energy Consumption by Office and Telecommunications Equipment in Commercial
Buildings.”33 Note that the latter study’s second volume, “Energy Savings Potential”34 may
provide useful programmatic insight for improvement opportunities once the end use study is
complete.
30 Kema, “Energy End-Use Consumption Estimates for Commercial Buildings (Sub-Task 3.2 Documentation
of 2003 CBECS EUC Estimation, Final Report)” (U.S. Energy Information Agency, May 19, 2008). 31 Federal Energy Management Program, “Federal Energy Management Program: Energy Cost Calculators
for Energy-Efficient Products”, n.d.,
http://www1.eere.energy.gov/femp/technologies/eep_eccalculators.html. 32 http://www1.eere.energy.gov/buildings/appliance_standards/commercial/ 33Arthur D. Little, Inc., “Energy Consumption by Office and Telecommunications Equipment in Commercial
Buildings Volume I: Energy Consumption Baseline” (U.S. DOE, January 2002),
http://www.biblioite.ethz.ch/downloads/Roth_ADL_1.pdf. 34 TIAX LLC, “Energy Consumption by Office and Telecommunications Equipment in Commercial Buildings,
Volume II: Energy Savings Potential” (U.S. DOE, December 2004),
http://www.tiaxllc.com/aboutus/pdfs/DOE_Energy_Consumption_1204_Rpt_033105.pdf.
AEA End Use Study- Methodology Page | 39
September 1, 2011
Brian Saylor &
Associates
Server unit energy consumption is based on the CBEC’s methodology35 and server power for
different sized equipment, as tabulated for that study and summarized in the following table.
Servers operate continuously, and for this study are assumed to operate 8,760 hours/year.
Table 23: Server power
Type # Servers Server Power (W)
Server Closet 1-2 207
Server Room 3-24 213
Localized Data Center 25-199 249
Mid-Tier 200-899 241
Enterprise Class ≥900 284
The primary source for computer and related equipment power consumption is data tabulated
for the CBECS analysis. Key data tables that have been excerpted for use in this study are
summarized below.
Table 24: Computer off-hour power
Description Computer Off-Hrs Power (W)
Power down / always turns off 2.5
Sometimes turns off 26
Never turns off 51
Table 25: Monitor on-hours power
Description Monitor On-Hours Power (W)
Flat-screen (LCD) 20
Mostly flat-screen 29
Some flat-screen 48
Not flat-screen (CRT) 57
Table 26: Monitor off-hours power
Description Description Monitor Off-Hrs Power (W)
Always turns off
Flat-screen (LCD) 2
Mostly flat-screen 1.8
Some flat-screen 1.3
Not flat-screen (CRT) 1.1
Sometimes turns off
Flat-screen (LCD) 6
Mostly flat-screen 8
Some flat-screen 13
35 Kema, “Energy End-Use Consumption Estimates for Commercial Buildings (Sub-Task 3.2 Documentation
of 2003 CBECS EUC Estimation, Final Report)” (U.S. Energy Information Agency, May 19, 2008).
AEA End Use Study- Methodology Page | 40
September 1, 2011
Brian Saylor &
Associates
Not flat-screen (CRT) 15
Never turns off
Flat-screen (LCD) 11
Mostly flat-screen 15
Some flat-screen 24
Not flat-screen (CRT) 29
Table 27: Printer on-hours power
Description Printer On-Hours Power (W)
Inkjet 5
Laser 69
Other 74
Both 37
Table 28: Printer off-hours power
After-Hours Power
Management Printer Type
Printer Off Hours
Power (W)
Always turns off Inkjet 2
Laser 0
Other 0
Both 1
Sometimes turns off Inkjet 2
Laser 14
Other 21
Both 8
Never turns off Inkjet 5
Laser 27
Other 41
Both 16
Table 29: Fax and copier off-hours power
Description Faxes Off-Hours
Power (W)
Copier Off-Hours Power
(W)
Always turn off 0 1
Sometimes turn off 17 71
Never turn off 35 141
AEA End Use Study- Methodology Page | 41
September 1, 2011
Brian Saylor &
Associates
5.10.4 Food Service, Cooking and Refrigeration
Commercial refrigeration and cooking represents a significant energy end use in certain building
types. Energy end uses are explicitly calculated for the following sub-categories.
5.10.4.1 Commercial Walk-In Coolers & Freezers
Commercial walk-in cooler and freezer unit energy consumption (UEC) is based on the
methodology and calculations provided in the Walk-In Coolers and Freezers Preliminary
Technical Support Document developed by the U.S. Department of Energy's Appliances and
Commercial Equipment Standards Program36. The program provides detailed market and energy
end use analysis for walk-in coolers and freezers of all classes.
The technical support document categorizes walk-in coolers and freezers into 36 distinct
equipment class types based on (1) refrigeration equipment parameters: operating
temperature, the location of the walk-in (i.e., indoors or outdoors) and the type of condensing
unit; as well as (2) walk-in envelope parameters: display/non-display, cooler/freezer, and size
(volume). Engineering calculations were completed for each viable equipment class combination
(pairing refrigeration equipment with walk-in envelope types) to arrive at individual baseline
annual energy consumption values. To capture the efficiencies of the various refrigeration
technologies available to each equipment class, additional energy efficiency design options (e.g.
strip curtain, LED bubs, etc.) were modeled for each class as well. These energy consumption
values were weather normalized by state. A reference table with all the UEC values is provided
in the supporting Non-Residential Survey Methodology spreadsheet.
The following table describes the parameters needed to determine the Alaska-specific annual
UEC values for walk-in coolers and freezers including two additional refrigeration design options
and three additional envelope design options above the baseline design per equipment class.
Table 30: Parameters required for estimated walk-in cooler/freezer unit energy consumption
Parameter Type Parameter Parameter Inputs
Walk-In Envelope Class
Operating Temperature Cooler, Freezer
Display or Non-Display? Display, Non-Display
Size small, medium, large
Refrigeration System
Class
Condensing Type Dedicated, Multiplex
Operating Temperature Medium, Low
Condenser Location Indoor, Outdoor
Selected Envelope
Design Options
Infiltration Reduction Device air curtain (active), strip curtain (passive), none
Anti-Sweat Heaters yes, no
Lighting LED, T8
Selected Refrigeration High Efficiency Scroll yes, no
36 DOE, “Appliances & Commercial Equipment Standards, Walk-In Coolers & Freezers Preliminary Technical
Support Document.” Updated 3/23/2011
http://www1.eere.energy.gov/buildings/appliance_standards/commercial/wicf_preliminary_tsd.html
AEA End Use Study- Methodology Page | 42
September 1, 2011
Brian Saylor &
Associates
System Design Options Compressors
Defrost Control yes, no
A ‘display’ walk in cooler includes coolers with glass doors, such as those sometimes found in
gas stations and convenience stores that have a walk-in beer cooler with glass door, or glass side
access doors with a walkway behind the shelves for stocking, as illustrated below.
Figure 4: Walk-in display cooler (left), and non-display walk-in cooler (right)
5.10.4.2 Commercial Refrigeration Equipment
Commercial refrigeration equipment unit energy consumption (UEC) is based on the
methodology and calculations provided in the Commercial Refrigeration Equipment (CRE)
Preliminary Technical Support Document developed by the U.S. Department of Energy's
Appliances and Commercial Equipment Standards Program37. The program provides detailed
market and energy end use analysis for commercial refrigeration equipment of all classes.
Figure 5: Commercial refrigeration equipment
The technical support document categorizes commercial refrigeration equipment into 23
distinct equipment class types based on based on condensing unit type, operating temperature,
door type, and orientation. Engineering calculations were completed for each equipment class
to arrive at individual baseline annual energy consumption values on either a per unit or per
37 DOE, “Appliances & Commercial Equipment Standards, Commercial Refrigeration Equipment Preliminary
Technical Support Document.” Updated 3/23/2011
http://www1.eere.energy.gov/buildings/appliance_standards/commercial/cre_prelim_analysis_tsd.html
AEA End Use Study- Methodology Page | 43
September 1, 2011
Brian Saylor &
Associates
linear foot basis, depending on the equipment class. To capture the efficiencies of the various
refrigeration technologies available to each equipment class, additional energy efficiency design
options (e.g. night curtain, LED bubs, etc.) were modeled for each class as well. A reference table
with all the UEC values is provided in the supporting Non-Residential Survey Methodology
spreadsheet.
The following table describes the parameters needed to determine the Alaska-specific annual
UEC values for commercial refrigeration equipment including two additional design options
above the baseline design per equipment class.
Table 31: Parameters required for estimated commercial refrigeration equipment unit energy consumption
Parameter Parameter Inputs
Condensing Unit Type self-contained, remote-condensing
Operating temperature med (38 F), low (0 F), ice-cream (-15 F)
Door Type transparent door, solid door, no door
Orientation horizontal, semi-vertical, vertical
# of units or linear feet # of units or feet
5.10.4.3 Residential Style Refrigerators
Refrigerator unit energy consumption is based on methodology develop by the Lawrence
Berkeley National Lab for the Home Energy Saver program38. The primary factors impacting
refrigeration energy use is vintage, size, and type. The unit energy consumption is calculated as
follows:
𝑈𝐸𝐶𝑟𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟=�365 × 𝐴𝑉𝐸𝐹# 𝑅𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟𝑠
Where:
UECrefrigerator = unit energy consumption (kWh/year)
AV = adjusted volume (ft3)
EF = Energy Factor (kWh/ft3/year), per Table 6, which is a lookup table factoring in
refrigerator type and year of manufacture
And the adjusted volume is defined as:
𝐴𝑉=𝑠𝑖𝑧𝑒× (𝑓𝑟𝑎𝑐+(1 −𝑓𝑟𝑎𝑐)× 1.63)
38 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Section 3.3.1 Refrigerator Energy Consumption
AEA End Use Study- Methodology Page | 44
September 1, 2011
Brian Saylor &
Associates
Where:
size = "Nominal" refrigerator/freezer volume (ft3)
frac = Fraction of refrigerator volume devoted to fresh-food storage (0 ≤ frac ≤ 1). For
side-by-side refrigerators, a fresh-food fraction of 0.6 is used, while all other
configurations use a fraction of 0.66.
Table 32: Shipment Weighted Energy Factors (EF) for Refrigerators39
Year General
Automatic defrost Manual
Defrost Side-by-Side Top Freezer
1972 3.84 3.57 3.56 6.69
1973 4.03 3.81 3.81 6.77
1974 4.22 4.05 4.06 6.85
1975 4.41 4.29 4.31 6.93
1976 4.60 4.53 4.56 7.01
1977 4.79 4.77 4.81 7.09
1978 4.96 5.02 4.75 7.18
1979 5.27 5.32 5.21 7.25
1980 5.59 5.62 5.67 7.32
1981 6.09 5.93 6.12 7.39
1982 6.12 6.02 6.30 7.69
1983 6.39 6.10 6.47 7.98
1984 6.57 6.12 6.75 8.19
1985 6.72 6.36 6.89 5.85
1986 6.83 6.49 6.95 6.14
1987 7.45 7.28 7.66 5.45
1988 7.60 7.45 7.83 5.09
1989 7.78 7.68 8.06 4.55
1990 8.15 7.78 8.51 4.84
1991 8.44 8.26 8.91 4.32
1992 8.80 8.69 9.36 3.50
1993 11.13 12.18 11.39 3.89
1994 11.19 12.45 11.37 4.13
1995 11.22 12.41 11.47 3.75
1996 11.22 12.08 11.48 4.21
1997 10.63 11.44 10.88 3.99
1998 10.5 11.30 10.74 3.94
1999 10.4 11.20 10.64 3.90
39 Table data for years through 2003 is from Table 14 in “The Home Energy Saver: Documentation of
Calculation Methodology, Input Data, and Infrastructure”. 2011 data was taken from the ENERGY STAR
energy savings calculator
http://www.energystar.gov/ia/business/bulk_purchasing/bpsavings_calc/Consumer_Residential_Refrig_Sa
v_Calc.xls (accessed 6/27/11), and linearly interpolated between 2003. The Home Energy Saver weighting
factor methodology was used to develop EF data for different door styles..
AEA End Use Study- Methodology Page | 45
September 1, 2011
Brian Saylor &
Associates
2000 11.11 11.96 11.37 4.17
2001 13.58 14.62 13.89 5.10
2002 15.17 16.33 15.52 5.69
2003 15.30 16.47 15.65 5.74
2004 15.70 16.89 16.06 5.88
2005 16.09 17.32 16.46 6.03
2006 16.49 17.74 16.87 6.18
2007 16.89 18.17 17.27 6.32
2008 17.28 18.59 17.68 6.47
2009 17.68 19.02 18.08 6.62
2010 18.07 19.45 18.49 6.77
2011 18.47 19.87 18.89 6.92
5.10.4.4 Residential-Style Freezer
Freezer unit energy consumption is based on methodology develop by the Lawrence Berkeley
National Lab for the Home Energy Saver program40. The primary factors impacting freezer
energy use are vintage, size, and type. The unit energy consumption is calculated as follows:
𝑈𝐸𝐶𝑓𝑟𝑒𝑒𝑧𝑒𝑟=�365 × 𝐴𝑉𝐸𝐹# 𝑅𝑒𝑓𝑟𝑖𝑔𝑒𝑟𝑎𝑡𝑜𝑟𝑠
Where:
UECrefrigerator = unit energy consumption (kWh/year)
AV = adjusted volume (ft3)
EF = Energy Factor (kWh/ft3/year), per Table 6, which is a lookup table factoring in
refrigerator type and year of manufacture
And the adjusted volume is defined as:
𝐴𝑉=𝑠𝑖𝑧𝑒× 1.73
Where:
size = "Nominal" refrigerator/freezer volume (ft3)
40 Lawrence Berkeley National Lab, “The Home Energy Saver: Documentation of Calculation Methodology,
Input Data, and Infrastructure”, July 2005, http://evanmills.lbl.gov/pubs/pdf/home-energy-saver.pdf.
Section 3.3.2 Freezer Energy Consumption
AEA End Use Study- Methodology Page | 46
September 1, 2011
Brian Saylor &
Associates
Table 33: Shipment Weighted Energy Factors for Freezers41
Year General
Upright Design
Chest
Freezers
Automatic
Defrost
Manual
Defrost
1972 7.29 5.23 7.65 8.78
1973 7.72 5.43 7.93 9.27
1974 8.15 5.63 8.21 9.76
1975 8.58 5.83 8.49 10.25
1976 9.01 6.03 8.76 10.74
1977 9.44 6.23 9.03 11.23
1978 9.92 6.41 9.31 11.74
1979 10.39 6.95 9.84 11.77
1980 10.85 7.49 10.37 11.8
1981 11.13 8.03 10.89 11.82
1982 11.28 8.23 11.38 11.87
1983 11.36 8.43 11.44 11.91
1984 11.6 8.58 11.51 12.31
1985 11.55 9.5 11.56 12.04
1986 12.07 9.44 12.07 12.84
1987 12.93 9.57 12.6 14.41
1988 12.91 9.31 12.61 14.46
1989 13.89 9.47 13.86 15.48
1990 14.19 10.41 14.15 15.67
1991 14.17 10.43 13.95 15.92
1992 13.95 10.38 13.73 15.63
1993 17.38 13.65 17.3 19.43
1994 16.91 13.14 17.02 18.89
1995 16.57 13.16 16.95 18.28
1996 16.56 13.11 17.09 18.18
1997 16.41 12.99 16.94 18.02
1998 16.30 12.90 16.82 17.89
1999 16.16 12.79 16.68 17.74
2000 15.93 12.61 16.44 17.49
2001 17.38 13.76 17.94 19.08
2002 17.83 14.12 18.40 19.57
2003 17.06 13.51 17.61 18.73
2004 17.94 14.21 18.51 19.70
2005 18.82 14.91 19.42 20.66
2006 19.70 15.60 20.33 21.63
2007 20.58 16.30 21.24 22.60
2008 21.46 17.00 22.15 23.56
2009 22.34 17.69 23.05 24.53
2010 23.22 18.39 23.96 25.50
2011 24.10 19.09 24.87 26.46
41 Data for years through 2003 is from Table 16 of “The Home Energy Saver: Documentation of Calculation
Methodology, Input Data, and Infrastructure”. Updated data for 2011 was obtained and linearly
extrapolated back to 2004.
AEA End Use Study- Methodology Page | 47
September 1, 2011
Brian Saylor &
Associates
5.10.4.5 Commercial Cooking
There is a very wide range of equipment used in commercial cooking. While there are some data
and methodologies for calculating energy consumption for individual commercial cooking
equipment42, this represents only a portion of the diverse equipment found in a kitchen.
Furthermore, it would be difficult to obtain accurate estimations of annual individual equipment
usage via survey. The team plans on using methodology for obtaining cooking UECs based on
the CBECs methodology. This method relies on per square foot energy use data reported by
CEUS. The following figure shows total energy end use (electricity and natural gas) as reported
by CEUS. As shown below, cooking represents a significant amount of energy in a certain class of
buildings (Figure 10).
Figure 10: Cooking and other miscellaneous energy use by ft2 and building type
42 For example,, the Federal Energy Management Program FEMP has developed a set of energy efficiency
Calculators for commercial cooking equipment including Electric Fryers, Hot Food Holding Cabinet, Gas
Steam Cookers, Beverage Vending Machines, Ice Machines, Range, Microwave, Toaster (oven), and a
Coffee Maker. http://www1.eere.energy.gov/femp/technologies/eep_eccalculators.html
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
kBTU/Segment FS/YearAir Compressors
Motors
Process
Miscellaneous
Office Equipment
Interior Lighting
Exterior Lighting
Refrigeration
Cooking
Water Heating
Ventilation
Cooling
Heating
AEA End Use Study- Methodology Page | 48
September 1, 2011
Brian Saylor &
Associates
The following two data tables summarize the CBECs data for electricity and natural gas end use
consumption by building type.
Table 34: Cooking and other end use energy intensities, Electric (kWh/ ft2/Year)43
Table 35: Cooking and other end use energy intensities, Natural Gas (kBTU/ft2/Year)44
43 Data is from the California Commercial End Use Study (CEUS), online data access page,
http://capabilities.itron.com/CeusWeb/ 44 Data is from the California Commercial End Use Study (CEUS), online data access page,
http://capabilities.itron.com/CeusWeb/
End Use All CommercialAll OfficeAll WarehouseCollegesGroceryHealthLarge OfficeLodgingMiscRefrigerated WarehouseRestaurantRetailSchoolSmall OfficeWarehouseHeating 0.22 0.38 0.03 0.77 0.08 0.71 0.49 0.42 0.09 0.02 0.05 0.08 0.13 0.20 0.04
Cooling 2.04 3.23 0.33 1.91 2.88 3.87 3.57 2.41 1.10 0.33 5.76 2.21 1.17 2.61 0.33
Ventilation 1.63 2.43 0.28 2.05 2.58 4.04 3.06 1.79 0.86 0.24 3.24 1.81 0.96 1.29 0.28
Water Heating 0.12 0.17 0.04 0.12 0.14 0.08 0.12 0.03 0.13 0.03 0.38 0.14 0.10 0.25 0.05
Cooking 0.57 0.11 0.02 0.27 1.85 0.43 0.12 0.68 0.26 0.04 10.38 0.22 0.18 0.10 0.02
Refrigeration 1.83 0.47 2.21 0.46 22.42 0.71 0.41 0.90 0.86 13.44 9.87 1.03 0.50 0.58 0.28
Exterior Lighting 0.80 0.65 0.27 0.91 0.95 0.57 0.49 0.61 1.07 0.35 2.02 0.92 0.74 0.95 0.26
Interior Lighting 3.92 4.24 2.29 3.84 8.55 4.81 4.46 3.50 2.61 2.74 6.45 6.05 2.88 3.83 2.21
Office Equipment 0.97 3.09 0.23 0.72 0.37 0.86 3.58 0.17 0.35 0.17 0.63 0.49 0.46 2.19 0.24
Miscellaneous 0.80 0.65 0.42 0.49 0.95 2.52 0.58 1.11 1.00 0.57 1.13 0.69 0.25 0.78 0.39
Process 0.04 0.02 0.02 0.01 0.01 0.01 0.03 0.00 0.12 0.04 0.01 0.05 0.00 0.00 0.02
Motors 0.57 0.54 0.52 0.58 0.18 0.78 0.72 0.48 1.08 1.82 0.27 0.29 0.08 0.22 0.29
Air Compressors 0.13 0.09 0.08 0.14 0.04 0.22 0.09 0.02 0.30 0.23 0.02 0.09 0.01 0.10 0.06
Segment Total 13.63 16.08 6.74 12.26 40.99 19.61 17.70 12.13 9.84 20.02 40.20 14.06 7.46 13.10 4.45
End Use All CommercialAll OfficeAll WarehouseCollegesGroceryHealthLarge OfficeLodgingMiscRefrigerated WarehouseRestaurantRetailSchoolSmall OfficeWarehouseHeating 9.46 14.18 2.40 19.83 9.51 32.70 17.22 7.28 7.04 0.79 7.75 3.02 10.01 8.62 2.68
Cooling 0.39 0.35 0.00 3.46 0.00 1.55 0.54 0.07 0.37 0.00 0.00 0.00 0.12 0.00 0.00
Water Heating 8.27 2.27 0.39 8.41 7.66 31.37 2.60 28.95 9.34 0.79 48.61 0.78 4.69 1.66 0.32
Cooking 5.88 0.20 0.19 1.66 10.35 3.37 0.23 4.42 1.02 1.22 153.29 0.52 1.05 0.12 0.01
Miscellaneous 0.47 0.08 0.03 0.86 0.02 1.45 0.10 1.43 0.99 0.01 0.01 0.27 0.03 0.04 0.04
Process 1.53 0.83 0.43 0.02 0.07 5.09 1.23 0.26 4.58 2.78 0.33 0.04 0.06 0.10 0.02
Segment Total 25.99 17.90 3.44 34.24 27.60 75.53 21.93 42.40 23.34 5.60 209.98 4.62 15.97 10.54 3.07
AEA End Use Study- Methodology Page | 49
September 1, 2011
Brian Saylor &
Associates
5.10.5 Process and Other Miscellaneous End Uses
The survey instrument will determine the presence of other miscellaneous end uses. The survey
will not focus on obtaining detailed data on process loads and other industrial type end uses.
Rather, we will use the CEUS data shown in the preceding graphs to estimate process and
miscellaneous energy use per square foot of total building area.
6. Rural North and West, Residential and Non-Residential
Data from rural Alaska may require information from multiple sources largely due to the high cost
of logistical data collecton in rural AK. ARIS data, largely from the weatherization program,
appears to be taken from a wide distribution of Alaska communities, both geographically and by
community size.
ANTHC has conducted energy ratings as part of their water and sewer program. Rural CAP has
collected residential energy use on 2000 existing homes through their Energy Wise Program. An
additional 518 homes will be assessed during the implementation of this study in the NANA
Region during the timeframe of this study; this data will be used to supplement the existing ARIS
data and Energy Wise data. The data set appears to have detailed energy- end use data.
Additional communities in northwest Alaska will provide additional data to Rural CAP.
The rural methodology will seek to secure a complete energy picture of at least one community
and one HUB. For example, the community of Selawik has been the recipient of the Energy Wise
program, the ANTHC Water and Sewer Audit, and has had some of their community facilities
benchmarked as a result of the AHFC REALS Program. Approximately 60% of Selawik’s energy use
practices have been collected through multiple agencies.
The general approach is outlined below:
• Conduct additional analyses of the extent to which existing data sources represent
energy use in one to three village communities and one HUB Community
• Leverage key informants within the various communities
• Stratify by size and class (HUB Community and village)
• Use combined data sources to develop an end use energy framework of existing
residential and nonresidential rural energy use
• A field technician would be sent into the community to verify secondary data collect
other end use data, such as the Tribal Council building, street lighting counts, and other
appropriate building types
6.1 Available Data
There are multiple data sets to incorporate into the rural methodology. These data sets will be
treated as primary data and included in the selected community’s end use energy analysis.
AEA End Use Study- Methodology Page | 50
September 1, 2011
Brian Saylor &
Associates
Energy Wise45. Through RuralCAP’s Energy Wise Program, data from approximately 2300
homes in over 35 communities will be available. Approximately 1922 individual home survey
records are currently available and an additional 388 home surveys will be collected during the
timeframe of the study.
ARIS Rural Data46. There are 437 existing ARIS home records dating back to 2008. These home
records are likely due to increased funding in the Home Energy Rebate Program and the
weatherization program.
ANTHC/ARUC Water/Sewer Energy Audit and Electrical Use Data.
o Labor, Electricity, fuel usage, (FY2009-2011) for all communities.
o Fuel purchase amount and cost for all ARUC communities each community.
o Labor, electricity, fuel usage data in a different format, and for some non-ARUC
communities
o Energy Audits available on select communities.
AHFC REALS Data. AHFC has up to 500 buildings that have been benchmarked available for
inclusion in the end use energy study.
AEA Energy Efficiency Grants through ASC EECBG, VEEP, and the Whole Village Retrofit 2010-
2012
AEA has approximately 109 communities enrolled in this village based program. To the extent
that there is benchmark or energy data in the cluster sample, this will be leveraged.
6.2 Sample Frame
WHPacific will employ a cluster sampling approach, with a focus on a small number of
communities with a high sampling fraction in each community. We have targeted up to three
(3) communities for potential inclusion in the comprehensive energy end use analysis. Bethel
will be targeted as the HUB.
The proposed sample frame consists of the 35 communities that participated in the RuralCAP
Energy Wise Program, due to the detailed and robust home energy records available in this data
set. This sample frame was narrowed to three communities through consultation with data
from ANTHC/ARUC program, AHFC REALS program, considerations for geographic dispersion,
and consensus between WHPacific, AEA, and ANTHC staff. The resulting cluster sample includes
the following three communities:
45 WHPacific has access to all of the existing Energy Wise Data. This data is available for review; but is too
voluminous to be included in this plan. 46 The ARIS Rural Data is found in the Appendix.
AEA End Use Study- Methodology Page | 51
September 1, 2011
Brian Saylor &
Associates
Table 31- Sample Frame- Selected Communities
Community Region Available Data
Villages
Selawik NW Arctic Borough; NANA
Region
Energy Wise Data; ANTHC Energy Audit Data;
Duplex
High School Addition; Service to 5 Plex; Tank
Farm SVC; Teacher Housing; Teacher Housing
Savoonga Norten Sound; Kawerak Energy Wise; ANTHC Water & Sewer Data;
Hogarth Kingeekuk Sr. Memorial School
New Stuyahok Bristol Bay Region; SW Alaska Energy Wise; ANTHC Water & Sewer Data; and
AHFC REALS Data; School building (electrical
data only)
HUB
Dillingham SW AK ARIS Data; AHFC REALS Data;
6.3 Survey Design.
Utilizing a cluster sampling approach with the three identified communities above, the
WHPacific team will undertake a comprehensive data analysis and collection effort in these
communities.
Data collection for non residential will be based upon the same methods to collect non-
residential buildings as found in the non-residential section of this plan. A technician will be
sent to the community to perform a walk-through of targeted buildings, including the school,
tribal buildings, power plant, and water and sewer system. Street light counts and other energy
data (with the exception of transportation) will also be considered.
As requested by AEA, the survey team will try to solicit permissions from respondents to access
utility records. This information will be used to verify end-use energy consumption calculations.
Data collection for residential will follow the protocols developed by RuralCAP and their existing
home survey data base and will be analyzed and included in the villages end use energy
framework. Existing ARIS data will be used to supplement the thermal analysis at the home
level. Please refer to the Appendix for a copy of the Home Visit Record template.
6.4 Unique Stratification
The stratification of the sample will be based upon a cluster sampling approach. This entails a
focus on a small number of communities with a high sampling fraction in each one. This is
largely due to the extraordinary cost of collecting data in rural Alaska. Additional village-based
data collection will be performed onsite. There is additional reasoning for a cluster sampling
approach. It is likely that the variation in use among individual buildings within a rural
AEA End Use Study- Methodology Page | 52
September 1, 2011
Brian Saylor &
Associates
community is greater than the variation in average use between communities. By collecting
from a high proportion of all buildings in one community, we can be relatively confident that we
are capturing this variation47.
6.5 Data Collection Tools and Framework
The WHPacific will employ the following tools in the data collection of the rural communities.
The HUB & Village Methodology is delineate below.
Table 32: Data Collection Tools
Data Collection Tools Building Type
Rural CAP Home Visit Record Residential
WHPacific Non-Residential
Methodology
Non-residential (School)
Walk-through Field Data
Collection
Field Protocol
6.6 HUB Community Methodology: Bethel Residential Study
6.6.1 Confirm availability of Bethel Energy rating (AKWarm & Weatherization Data) energy consumption data
and its relative usefulness, readiness, and relevance for thermal/heating calculations.
Reportedly, AKWarm residential energy consumption data was collected in Bethel sometime within the past five
years by AHFC certified energy raters and the housing authority. WHPacific will confirm this assertion and use what
energy ratings are available. We are in the process of locating the data. It is uncertain at this time as to its
completeness, the extent to which it represents the Bethel housing stock or if it has been entered into the ARIS
database. If the available data is located, it may be in hard copy. In this case, it must be entered into the ARIS
database or database of similar format and scope. Data entry will be performed by WHPacific and will be inputted
into an appropriate spreadsheet for analysis. Data entry will be performed as an additional expense.
6.6.2 Conduct preliminary analysis of energy consumption data.
Once the data is in a machine-readable form, either in the ARIS database or in some other analytic database,
WHPacific can conduct a preliminary data quality assessment. Although WHPacific’s preference is to rely on existing
data whenever possible, it is important to understand the extent of missing data, and to perform the needed data
quality edits and checks to ensure that outlying values are identified. This will help increase the reliability of end-use
energy estimates for the community. The WHPacific team will assess the data to determine if it is adequate for the
energy calculations used in the similar estimates of residential energy consumption in Climate Zones 6, 7, & 8.
Additional plans must be prepared for AEA review in the event that the existing data is insufficient to generate the
quality and precision of the end-use energy calculations desired by AEA. Additional resources may be required to
collect additional energy use data to increase the precision of the energy calculations.
47 Excerpted from End-Use Energy Data Collection for Alaska Buildings- Guidance Document
AEA End Use Study- Methodology Page | 53
September 1, 2011
Brian Saylor &
Associates
6.6.3 Confirm preliminary sample estimates by housing type for Bethel and these housing types represent
Bethel.
Conversations with ISER researchers identifies the demographic characteristics of the household as one of the best
predictors of energy consumption. The 2010 US Census housing data for the Bethel community will be used as a
way of developing a general sampling plan for residential end-use energy assessment in Bethel. The sampling plan
for Bethel residential end-use energy data collection uses these characteristics for stratification of the sample. A
preliminary sample size of 130 is used for budgeting purposes.
Table 36
Bethel Preliminary Sample Size
Sampling Size Estimates, Bethel City Households
90% Confidence Level, Margin of error = 10%
Household Characteristics Number Size Percent
Total Occupied
Families with children under 18 869 63 7.2%
Non Family and Families without children
under 18
1027 64 6.2%
Total 1896 127 6.7%
2010 US Census Data, Bethel City, accessed on August 17 at
http://live.laborstats.alaska.gov/cen/dp.cfm#h
Existing data will be compared with these expected values. In addition, the data will be checked
against the four housing categories used in Railbelt and Southeast Alaska:
• single family detached
• single family attached
• multifamily
• mobile homes
Under represented cells will be supplemented with additional data collection efforts.
WHPacific's preference is to use existing data whenever possible. In this case, it is likely that the
end-use energy consumption data collected for residences in Bethel will match the characteristics
of the sampling plan. However, this can be confirmed only after all available data has been
entered and analyzed.
6.6.4 Conduct residential electrical energy consumption survey
In the event that the existing residential end-use energy consumption data is available and can be
entered into an analytic data set, an additional survey for electrical energy consumption will
probably need to be conducted.
6.6.5 Combine Energy Rating/Weatherization with the electrical data collected via survey
A database containing Bethel residential end-use energy consumption will be developed which
includes existing data supplemented, as needed, with additional information on general
AEA End Use Study- Methodology Page | 54
September 1, 2011
Brian Saylor &
Associates
household energy use characteristics conducted via survey research. This information will be
combined with the electrical energy consumption survey data to produce a final analytic data set.
In the event that existing energy consumption data is not available, AEA may be
asked for additional resources to complete a survey to collect energy consumption
data for a sample of Bethel households.
6.7 HUB Community Methodology: Bethel Nonresidential.
6.7.1.1 Complete nonresidential inventory by building type. The beginning of this process will
require an inventory of nonresidential buildings by building type. The method for
identifying building types was reviewed earlier with AEA. This can be done by securing a
commercial building list from the City of Bethel and reviewing the various building types.
6.7.1.2 Review and revise nonresidential sampling plan. The inventory of building types will be
used to determine the preliminary Bethel nonresidential sampling plan. The WHPacific
team will rely largely on existing data to determine data collection requirements. At this
time, we are assuming that there are approximately 200 commercial buildings in Bethel.
We will sample approximately 50-75 buildings of this total in order to estimate the total
end-use energy efficiency of non-residential buildings in Bethel. For budgeting purposes,
we assume 75 buildings and 5 hours to collect the needed information.
6.7.1.3 Confirm availability of data. WHPacific will review available data for relevance to Bethel-
REALS, AHFC, and AEA Sources. While WHPacific believes that there is some data in the
data sources noted above on nonresidential end-use energy consumption, the extent to
which existing data will be sufficient to establish an end-use energy framework is unclear.
WHPacific will review and confirm the relevance of existing data to the proposed scope of
work and that it is sufficient to meet sample requirements. The existing data sources will
be compared with the sampling estimates described in #1 and 2 above. Discrepancies will
be noted and discussed with AEA.
6.7.1.4 Supplement with additional nonresidential walk-throughs A hybrid approach, utilizing
walk-throughs and survey research, will be implemented to collected data from
nonresidential facilities. Raters/data collection experts will collect any additional data
elements that are required for end-use energy calculations. It is anticipated that electrical
energy consumption will be among the data elements collected.
6.7.1.5 Complete energy use calculations. The protocol for the end-use analysis shown in the
EUS Methodololgy will be used for nonresidential energy calculations in Bethel.
AEA End Use Study- Methodology Page | 55
September 1, 2011
Brian Saylor &
Associates
6.8 Village Methodology.
WHPacific has identified the following communities to target for the village methodology/approach. These three
communities were identified and chosen due to their involvement in Energy Wise, all three are ARUC communities,
and all three communities were beneficiaries of the AHFC REALS program. These communities were selected
through a discussions with ANTHC- New Stuyahok, Selawik; and Savoonga48;
6.8.1.1 Obtain the most recent available US Census data on household and demographic
characteristics.
As shown in the table below, many of the essential data elements required by the ISER predictive models
come from the U.S. Census. It is uncertain if this data can be obtained at the community level.
6.8.1.2 Compare characteristics with Energy Wise data.
WHPacific will utilize Energy Wise data for the identified communities for residential use. Once the availability
of data is confirmed, a preliminary analysis will allow WHPacific to compare the community demographic and
household characteristics from the Energy Wise data with those contained in the 2010 US Census. The results
of this assessment will be reviewed by AEA and ISER to determine additional resource requirements for data
collection. As in previous parts of the rural Alaska end-use energy study, the review of the extent of missing
data is an important step in using the data in future predictive models. WHPacific staff will work with AEA and
ISER to develop an acceptable and approved format for data to be used in the predictive calculations.
6.8.1.3 Confirm requirements of the ISER predictive models & collect additional data needed.
Conversations with ISER researchers and AEA staff generated the following summary of required data
elements for the models to predict energy use and other rural communities. This table will be periodically
reviewed, modified and approved by AEA to ensure that data collected in this study will be sufficient for the
models used to predict rural energy use. The data in this table will be collected and provided to ISER for its
predictive model. See table below.
6.8.1.4 Complete energy use calculations.
The protocol for the end-use analysis shown in the methodolgoy will be used for residential energy
calculations in the three rural communities.
6.8.1.5 Utility Collect and synthesize utility data.
Water and wastewater (ARUC/ANTHC)
• Street lighting
• Utility data will be obtained from existing sources. Many of these data
elements are also shown in the table above.
48 Huslia was discussed as a village community. However, Huslia is not part of the ARUC program and does
not have longitudinal data on the water and sewer system.
AEA End Use Study- Methodology Page | 56
September 1, 2011
Brian Saylor &
Associates
6.8.1.6 Complete energy use calculations.
WHPacific will work with ARUC and other organizations to identify end-use energy patterns for rural
wastewater treatment plants in these communities. The team will then aggregate the data, estimate
energy consumption from non-reported utilities, and produce final community utility energy
consumption estimates.
6.8.1.7 Conduct or obtain nonresidential inventory for each respective community.
An inventory of nonresidential facilities will be conducted similar to nonresidential inventory in the
Bethel hub community. The building inventory is small and can be secured through the Department
of Commerce and Community and Economic Development’s website.
6.8.1.8 Perform walkthroughs and manager interviews.
Most communities have the facilities listed below. In some instances, there is available data on
energy consumption. Gaps in the available data compared with the nonresidential inventory will be
reviewed with AEA to determine the need for additional data collection and the attendant resource
requirements to complete the task. A WHPacific engineer/technician will be sent to the designated
community to collect the data. available data on: Schools, Clinics, Tribal and City Council offices,
Grocery stores, National Guard Amory, Other facilities
6.8.1.9 Complete energy use calculations.
The protocol for the end-use analysis shown in EUS methodology will be used for nonresidential energy
calculations in the three rural communities.
6.9 Collect Community Building Information for up to 225 Rural Communities.
WHPacific will collect community profile information for all communities in the state in order to support estimates
and extrapolations that ISER will perform for the rural areas and to support estimates and extrapolations that AEA
will make to support regional and local energy plans. There are an estimated 225 communities. WHPacific will
attempt to secure information from each building in the communities including address, sq footage, type, year
built, and other information found in the tax records from the larger communities and from the Census. If the
building-level data is not available short of going through paper records in city offices, WHPacific will obtain
whatever information is available, such as from Energy Wise, water and sewer data, AHFC data, through DCCED’s
Community Database (http://www.commerce.state.ak.us/dca/commdb/CF_CUSTM.htm), and through
other resources of WHPacific’s recommendation. The following fields are potential examples of the type of data to
be collected for the community data base: TOT HOUSING UNITS, OCCUPIED HU, VACANT HU, SEASONAL USE
OWNER OCC HU, RENTER OCC HU POP IN HH , GROUP QUARTERS, TOTAL HH , AVG HH SIZE, FAMILY HH ,
NONFAMILY HH TOTPOP18P, TOTPOP62P, TOTPOP16P.
WHPacific will provide the source and the date of information. WHPacific does not
guarantee that the building information inventory for each community will be complete.
WHPacific has budgeted 6 hours per community to collect this data. This data will be
provided in a raw format with no end-use analysis.
AEA End Use Study- Methodology Page | 57
September 1, 2011
Brian Saylor &
Associates
Table 37 Additional Data collection
Variables Needed for Rural North and West End Use Study
Data Gathered as Part of Village/Hub
Studies
Sources of the Same data in
Non-Studied Communities
Entity Responsible
Population by community (to match
Census Designated Areas) Census ISER
Number of housing units by sector
(single family, multi, mfg/mobile home) Census ISER
Total number of people per housing unit Census ISER
Number of people under 18 per housing
unit Census ISER
Number and square footage of non-
residential buildings by sector (sectors
used in Roadbelt/SE)
Tax records, other sources??
AHFC REALS Data-
WHPacific to
consolidate and collect.
Schools: square footage, year built,
annual energy use (if available)
AHFC, Dept of Edu, School
Districts, RPSU (Gather
actual annual energy use by
community where available)
WHPacific
Water and wastewater system type
(vacuum, pumps, heat trace, other?)
ARUC, cities, other? (Gather
actual annual energy use by
community where available)
WHPacific to provide as
part of the mini—end-
use study.
Street lighting type EUS Survey
WHPacific to provide as
part of the mini—end-
use study.
Heating degree days AEA AEA/AHFC
Wind AEA AEA/AHFC
PCE Known building electrical energy use PCE/AEA AEA
Other known building energy use (heat
and electric)
Assemble any known
building or facility data in
place of the variables for
communities where this
information is known, such
as home electrical use
through EnergyWise, AHFC's
REELs data, water plant
energy use, etc.
WHPacific
AEA End Use Study- Methodology Page | 58
September 1, 2011
Brian Saylor &
Associates
6.10 End use Analysis
The energy end use analysis will employ a combination of an engineering calculation utilizing the
methodology developed in the residential section of this document and energy audit and
building energy model for the non-residential buildings.
The WHPacific team will compile the survey data collected from the Energy Wise Home Visit
Records into a format for further analysis and commercial use classifications. This includes
compiling the necessary frequency distributions, descriptive and inferential statistics, and
variable cross tabulations. Frequency distributions show categories of responses to questions by
the actual number of responses and percentage of the total. This data will then be imputed into
the residential methodology as developed in this paper.
The non-residential buildings will employ the building energy audit/energy modeling developed
in the non-residential section of this document.
6.11 Survey Pre-Test, Validation, Reliability, and Vetting
The Rural CAP Home Visit Record has been tested and employed in over 1900 homes. The
WHPacific team will work with the Rural CAP staff for modification, quality control and
assurance to the extent practical. Rural CAP has been working with ISER on the evaluation and
data collection element of this program. We will work with ISER and Rural CAP to assure that
new data to be collected will be complete.
Before the non-residential survey and field visit/walk-throughs are performed in Rural Alaska. A
substantive number of non-residential building walk-throughs will be conducted before it is
performed in these cluster samples.
6.12 Survey Implementation
The field visit for these cluster samples will be conducted in October and November of 2011.
This will allow for the non-residential protocol to be refined. The new Energy Wise data home
visits will be conducted between October 15 and December 1, 2011. All other data has been
collected and will be analyzed from November-January.
6.12.1 Training
There are two components that will require training: the field engineer/technician and the Rural
CAP Energy Wise Field crews.
The Field Visit/Engineer technician will be trained and oriented per the non-residential
guidelines as found in this document. Ideally, this technician will have already performed
several walk-through the rural work.
AEA End Use Study- Methodology Page | 59
September 1, 2011
Brian Saylor &
Associates
The Rural CAP Energy Wise Training is scheduled for October 15-25. Crew members will be
trained on how to collect the data during these field based trainings.
6.12.2 Data Collection & Review for Quality Control
While in the field, WHPacific staff will remain in contact with both the Energy Wise field crews
and the field technician via the telephone for quality control purposes. There will be a field
supervisor to oversee the Energy Wise crew who will be responsible for reviewing onsite data
collection activities. All other existing data will be reviewed with key informants for quality
control and assurance.
Before the field technician leaves the community, all tools will be e-mail/faxed to a central
WHPacific point of contact for QA/QC.
7. Future Opportunities and Needs
7.1 Rural Fuel Consumption Study
AEA may be interested in a detailed procedure for accurately measuring the consumption of
heating oil in rural communities. During deployment of Energy Wise in the NANA Region, home
heating fuel records maybe collected via instrumentation methods in up to 20 residences. This
brief paper summarizes preliminary research on this topic.
Measuring the distribution of home heating oil
Consumers in rural Alaska appear to have a variety of ways of paying for a home heating fuel.
Although many houses have a 55 gallon drum or other storage unit outside their homes, many do
not have routine fuel delivery from a central delivery station. They may obtain fuel in smaller
quantities when fuel stores are low or as cash becomes available. Therefore, the use of fuel
distributor billing data may not include all purchases for each home. Consumer survey data based
on responses from individual householders about consumption habits may not meet AEA’s
required level of precision. Therefore, this method was rejected.
Measuring the consumption of home heating fuel
There are a few sources of fuel flow meters which could monitor domestic fuel oil consumption.
These meters must be capable of monitoring low flow rates, have clear monitoring at readouts
and be reasonably priced. Some with low minimum flow rates are manufactures in the United
Kingdom. The table below summarizes findings to date.
Table 33: Fuel flow meters capable of monitoring domestic fuel oil consumption
Brand Minimum
Flow Rate
Availability in
the US
Ease of installation Unit cost
Brenntag VZ04 oil
heating meter, UK
1 Liter/Hr None May require metric to
standard conversion
£ 139.95
($201.17)
Incenta Controls .01 Liters/Hr None May require metric to unknown
AEA End Use Study- Methodology Page | 60
September 1, 2011
Brian Saylor &
Associates
Keromate, UK standard conversion
FC4 Oil Meter, Elster
Metering Company,
UK
1 – 3 Liters/Hr
Yes (Georgia) Standard couplings $650
Grainger 0YG1 .26 GPM Yes Standard couplings $336.20
GPI Turbine Design
01A Series Flow
Meter
3 GPM Yes (VT) Standard couplings $166
Kent/AMCO Positive
Displacement Oil
Meters,
2.6 GPH Yes (NJ) Standard couplings unknown
Staff at HASCO (a metering products distributor in Anchorage) estimated that these units could
be installed in 1 to 2 hours in each home. The ability to reset the readouts to use in multiple
homes is unknown.
AEA End Use Study- Implementation Plan Page | 1
July 11, 2011
Brian Saylor &
Associates
Appendices
AEA End Use Study- Implementation Plan Page | 2
July 11, 2011
Brian Saylor &
Associates
Annotated Review of Energy End-Use Surveys
AEA End Use Study- Implementation Plan Page | 3
July 11, 2011
Brian Saylor &
Associates
Annotated Review of Energy End-Use Surveys
US Energy Information Agency’s Commercial Building Energy Consumption Survey (CBECS)
CBECS is the DOE’s primary commercially building energy end use study covering buildings in the US. The
target population is commercial buildings in the US greater than 1,000 ft2. CBECS uses a multistage area
probability sample supplemented by a sample of buildings drawn from various special list frames within
the primary sampling units (PSUs). A sample of 6,955 potential building cases was selected, with 5,215
completed building interviews for a response rate of 82%. The CBECS is conducted in two data-collection
stages: a Building Characteristics Survey and an Energy Suppliers Survey. The Building Characteristics
Survey collects information about selected commercial buildings through voluntary interviews with the
buildings’ owners, managers, or tenants using Computer-Assisted Personal Interviewing (CAPI)
techniques. Upon completion of the Building Characteristics Survey, the Energy Suppliers Survey is
initiated for those cases that did not provide satisfactory consumption and expenditures information. This
Suppliers Survey obtains data about the building’s actual consumption of and expenditures for energy
from records maintained by energy suppliers.
Note that the last (2007) survey was recently cancelled rather unexpectedly. This leaves the 2003 study
(with analysis completed in 2008) as the latest available data and methodology (very few details on the
2007 methodology, etc. have been released, and are not likely finalized). There is also a National
Academies review paper discussing issues and challenges related to the 2007 methodology in preparation
for updating the method for the 2011 survey (also cancelled due to budget cuts). Note that this study is
not directly tied to nor focuses on issues related to the cancelation of the 2007 survey.
• CBECs website, with a variety of documents and links.
o http://www.eia.doe.gov/emeu/cbecs/.
• Basic overview of sample and methodology
o “2003 CBECS Sample Design”, http://www.eia.gov/emeu/cbecs/2003sample.html.
• Detailed calculation methodology
o Energy Information Administration (Prepared by KEMA, Inc.) “Energy End-Use
Consumption Estimates for Commercial Buildings, Sub-Task 3.2, Documentation of 2003
CBECS EUC Estimation Final Report” May 19, 20081
o This document provides detailed descriptions of the engineering estimates for various
energy end-uses. These are potentially useful for this study. .
• Survey Forms:
o US DOE, “2003 CBECS Building Questionnaire”, 2003,
http://www.eia.gov/emeu/cbecs/cbecs03ques.pdf.
o US DOE, “2003 CBECS Electricity Usage Form”, 2003,
http://www.eia.gov/emeu/cbecs/electricity2003.pdf.
o US DOE, “2003 CBECS Natural Gas Energy Usage Form”, 2003,
http://www.eia.gov/emeu/cbecs/natgas2003.pdf.
• 2007 CBECs survey review data
o National Academies, Committee on National Statistics, “Commercial Buildings Energy
Consumption Survey Letter Report”, May 18, 2010,
http://www.nap.edu/openbook.php?record_id=12922&page=1.
1 This document (CBECS Estimation Sub-Task 3 2 Final (6).doc) was provided by Jay Olsen at the the US EIA.
AEA End Use Study- Implementation Plan Page | 4
July 11, 2011
Brian Saylor &
Associates
o National Academies, Committee on National Statistics, “Meeting 1: Redesigning the
Commercial Buildings and Residential Energy Consumption Surveys of the Energy
Information Administration”, February 1, 2010,
http://www8.nationalacademies.org/cp/meetingview.aspx?MeetingId=4080 .
Notice of 2007 CBECS cancellation from DOE: http://www.eia.gov/emeu/cbecs/
US Energy Information Agency’s Residential Energy Consumption Survey (RECS)
This is the DOE’s primary residential building energy end use study for buildings covering the entire US.
Specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and
household demographics. This information is combined with data from energy suppliers to these homes
to estimate energy costs and usage for heating, cooling, appliances and other end. RECS uses a multi-
stage area probability design:
• 430 Counties were randomly selected;
• 3000 segments/5420 Census Blocs were randomly selected;
• ~19,000 homes selected for interviews (of these only ~15,300 were occupied primary residences
and eligible.
• Of these, 12,100 responded to the survey. Response rate ~ 79%)
Trained interviewers conducted the interviews. A non-linear statistical model was applied to data from
the Household and Energy Supplier Surveys to disaggregate total energy consumption into end-use
components.
• Program overview and methodology
o RECS homepage, with links to program details and forms.
http://www.eia.gov/consumption/residential/.
o Methodology Brief,
http://www.eia.gov/consumption/residential/methodology/2009/brief.cfm.
• Data Collection Forms
o US DOE, “2009 RECS Household Questionnaire”, 2009,
http://www.eia.gov/emeu/recs/recs09/09recsquestionnaire.pdf.
o US DOE, “2005 RECS Electricity Usage Form”, 2005
http://www.eia.gov/emeu/recs/recs2005/2005ElectricSuppliersQuex.pdf.
o US DOE, “2005 RECS Fuel Oil Usage Form”, 2005,
http://www.eia.gov/emeu/recs/recs2005/2005FuelOilSuppliersQuex.pdf.
o US DOE, “2005 RECS Natural Gas Usage Form”, 2005,
http://www.eia.gov/emeu/recs/recs2005/2005NatGasSuppliersQuex.pdf .
o US DOE, “2005 RECS Propane Usage Form”, 2005,
http://www.eia.gov/emeu/recs/recs09/09recsquestionnaire.pdf.
US Energy Information Agency’s Manufacturing Energy Consumption Survey (MECS)
The US Energy Information Agency also conducts a nationwide manufacturing sector energy consumption
survey.
• MECS homepage
o http://www.eia.gov/emeu/mecs/contents.html
AEA End Use Study- Implementation Plan Page | 5
July 11, 2011
Brian Saylor &
Associates
California Residential Appliance Saturation Study (RASS)
The California Energy Commission funded and administered a Residential Appliance Saturation Study for
the state of California in 2009 that updates a similar 2003 study. California’s largest utilities participated
in the study – Pacific Gas and Electric Company (PG&E), Southern California Edison (SCE), San Diego Gas &
Electric Company (SDG&E), Southern California Gas Company (SoCal Gas), and Los Angeles Department of
Water and Power (LADWP). KEMA was the prime consultant. The study was implemented as a mail
survey with an option for respondents to complete it online. The survey requested households to provide
information on appliances, equipment, and general consumption patterns. Data collection was completed
in early 2010.
The study yielded energy consumption estimates for 27 electric and 10 natural gas residential end-uses
and appliance saturations for households. These consumption estimates were developed using a
conditional demand analysis, an approach that applied statistical methods to combine survey data,
household energy consumption data and weather information to calculate average annual consumption
estimates per appliance. The 2009 RASS resulted in end-use saturations for 24,464 individually metered
and 1,257 master-metered households. Survey and conditional demand analysis results were weighted to
provide population level estimates representative of the participating utilities that allow comparison
across utility service territories, forecast climate zones and other variables of interest- dwelling type,
dwelling age group, and income.
• RASS homepage
o “Residential Appliance Saturation Survey”, http://www.energy.ca.gov/appliances/rass/.
• Online RASS Database (maintained by Kema), providing customized data queries.
o http://websafe.kemainc.com/RASSWEB/DesktopDefault.aspx
• Methodology & documentation
o Kema, “2009 California Residential Appliance Saturation Study - Executive Summary”,
October 2010, California Residential Appliance Saturation Study.
http://www.energy.ca.gov/2010publications/CEC-200-2010-004/CEC-200-2010-004-
V1.PDF
o Kema, “2009 California Residential Appliance Saturation Study - Methodology and
Appendices”, October 2010, http://www.energy.ca.gov/reports/400-04-009/2004-08-
17_400-04-009ALL.PDF.
o RASS Appendices including survey, pretest results, direct mail materials, data collection
protocols, etc. (24 MB): http://www.energy.ca.gov/2010publications/CEC-200-2010-
004/CEC-200-2010-004-AP.PDF
California Commercial End-Use Survey (CEUS)
The California Commercial End-Use Survey (CEUS) is a comprehensive study of commercial sector energy
use, primarily designed to support the state's energy demand forecasting activities. A stratified random
sample of 2,790 commercial facilities was collected from the service areas of Pacific Gas and Electric, San
Diego Gas & Electric, Southern California Edison, Southern California Gas Company, and the Sacramento
Municipal Utility District. The sample was stratified by utility service area, climate region, building type,
and energy consumption level.
For each utility service area, floor stocks, fuel shares, electric and natural gas consumption, energy-use
indices (EUIs), energy intensities, and 16-day hourly end-use load profiles were estimated for twelve
AEA End Use Study- Implementation Plan Page | 6
July 11, 2011
Brian Saylor &
Associates
common commercial building type categories. Detailed building energy simulation modeling was used to
develop the energy end uses and load profiles.
• CEUS Website
o http://www.energy.ca.gov/ceus/
• Methodology and Documentation
o Itron, “California Commercial End-Use Survey Final Report” (California Energy
Commission, March 2006), http://www.energy.ca.gov/2006publications/CEC-400-2006-
005/CEC-400-2006-005.PDF.
o Itron, “California Commercial End-Use Survey, Appendices A-J” (California Energy
Commission, March 2006), http://www.energy.ca.gov/2006publications/CEC-400-2006-
005/CEC-400-2006-005-APA.PDF.
End-Use Load Data Update Project for the Northwest Power and Conservation Council and Northeast Energy
Efficiency Partnerships
In recent decades, efforts to gather end-use load data appear to be scattered and minimal across the
Pacific Northwest and East regions. Although a significant amount of data was gathered through
Bonneville Power Administration’s End-Use Load and Consumer Assessment Program (ELCAP) in the mid-
1980’s through the early 1990’s, confidence in the data has waned as technologies change and consumer
behavior evolves.
The Northwest Power and Conservation Council’s Regional Technical Forum (RTF) and the Northeast
Energy Efficiency Partnerships (NEEP) Evaluation Measurement and Verification (EMV) Forum sponsored
a study to review existing end-use and load shape data studies relevant to the northwest and northeast
Regions to identify gaps in and problems with the existing load shape data and establish priorities and
study scopes for load shape improvements to support energy efficiency program planning, electricity
markets and environmental policy.
Elements of the work included: research and inventory the existing load shape data available, determine
what load shape data is necessary to meet utility energy efficiency program, ISO-New England and PJM
Capacity Markets (CMs), and air quality regulatory needs identify weaknesses in the existing data for use
in efficiency programs, capacity markets, and air quality regulations, evaluate the transferability and
applicability of existing load shape data to the Regions, provide a road map for meeting future short term
and long term end-use metering needs.
• Kema, “End-Use Load Data Update Project Final Report for the Northwest Power and
Conservation Council and Northeast Energy Efficiency Partnerships”, September 2009,
http://www.neep.org/uploads/EMV%20Forum/EMV%20Products/KEMA%20End%20Use%20Cata
log%20Report%20Executive%20Summary%20for%20Web.pdf.
Northwestern Energy (Montana) End Use and Load Profile Study
An end use and load profile study was conducted for NorthWestern Energy (NWE)’s Montana service
territory. This study was conducted in 2009 by Kema and the Cadmus Group. This study covers NWE's
residential and non-residential customers with a particular focus on the residential and commercial
sectors. The results of this study rely mainly upon primary research conducted in the form of onsite
customer surveys, designed to inventory the current energy using equipment with regards to type, fuel,
AEA End Use Study- Implementation Plan Page | 7
July 11, 2011
Brian Saylor &
Associates
efficiency, and operating conditions, as well as document the characteristics of the buildings themselves.
This study also drew from historical audit data performed on NWE' s customers; however, significant
effort was involved in the verification and calibration of this data to ensure that it was representative of
NWE's customer base as a whole. Nexant’s team of engineers performed site visits for 225 residential and
commercial customers with survey instruments formulated specifically for this study. High importance
was placed on this data to ensure that the findings are specific to NWE's service territory and not the
entire Pacific Northwest region.
• Nexant and Cadmus, “Energy End Use and Load Profile Study for Northwest Energy (NEW)”,
December 16, 2009,
http://www.northwesternenergy.com/documents/defaultsupply/plan09/volume2/Chapter2-
EndUseLoadProfile.pdf.
Others
• Northeast
• RLW Analytics, “Coincidence Factor Study Residential and Commercial Industrial Lighting
Measures” Prepared for New England State Program Working Group (SPWG), Spring 2007
• www.puc.nh.gov/.../116_RLW_CF%20Res%20C&I%20ltg.pdf
• RLW Analytics, “New Hampshire Small Business Energy Solutions Program Impact
Evaluation”, September 2004 www.cee1.org/eval/db_pdf/549.pdf
• RLW Analytics , “NStar Electric Small Business Solutions Program Program Year 2002 Impact
Evaluation”, November 2003 http://www.cee1.org/eval/db_pdf/403.pdf
• Mid Atlantic
o BGE, “Evaluation of the Load Impacts of the Electric Water Heater Load Control Program
(Rider 6)”, Dec 2002 http://www.cee1.org/eval/db_pdf/560.pdf
• Northwest
o Seattle City Light, “Multifamily Space Heat Thermostat Metering Study, Phases 1 and 2”
Dec 2006 www.seattle.gov/light/Conserve/Reports/Evaluation_11.pdf
• Jamaica
o The Planning Institute of Jamaica and The Statistical Institute of Jamaica, “Residential
Consumer End Use Survey, Volume 1 - Household Energy & Transport”, January 2007.
AEA End Use Study- Implementation Plan Page | 8
July 11, 2011
Brian Saylor &
Associates
Annotated Review of Alaska Energy Resources
AEA End Use Study- Implementation Plan Page | 9
July 11, 2011
Brian Saylor &
Associates
Annotated Review of Alaska Energy Resources
Alaska Housing and Finance Corporation
The AHFC (http://www.ahfc.state.ak.us/home/index.cfm) is involved in a wide range of energy-related
programs, including;
o Alaska State Energy Office –
The AHFC and Rural Development Division (R2D2) is the designated Alaska State Energy
Office. It is the primary recipient of federal funds for the Weatherization Assistance
Program (WAP), State Energy Program (SEP), and the Energy Efficiency Community Block
Grants (EECBG), for renewable energy and energy efficiency in Alaska. AHFC works with
the Alaska Energy Authority (AEA) to implement these programs.
o Building Energy Efficiency Standard (BEES) –
Meeting the Alaska Building Energy Efficiency Standard (BEES) is required for all new
residential homes and community-owned buildings receiving AHFC financing. Exceeding
BEES may qualify individuals for special loans and rebates. The current standard is the
2009 International Energy Conservation Code (IECC) with Alaska Specific Amendments.
AHFC is responsible for BEES, provides technical assistance and maintains a list of
individuals who may verify BEES compliance on the required PUR-101 form.
o Energy Ratings –
AKWarmTM Energy Raters - AHFC authorized individuals perform energy ratings to show
compliance with Alaska Building Energy Efficiency Standards (BEES) and energy efficient
loans and rebates. AHFC maintains a list of AKWarmTM energy raters and training
requirements.
There are approximately 90 AHFC authorized energy raters in the state.
AKWarmTM Software - This AHFC software is used for energy efficient design, retrofit, or
to determine an energy rating. Tested and approved by the U.S. Department of Energy.
Energy use data is stored in the Alaska Retrofit Information System (ARIS) database.
o Loans for Homes-
Energy Efficiency Interest Rate Reduction Program - Home buyers purchasing existing or
newly constructed homes may receive an interest rate reduction for energy efficiency.
The maximum interest rate reduction is 0.75% on the first $200,000 of an AHFC loan. The
program may be combined with the Home Energy Rebate or New Home Rebate.
Second Mortgage Program for Energy Conservation - Homeowners may finance up to
$30,000 of energy efficient improvements, choosing from a list of upgrades included on
their home energy rating. It's often combined with a Home Energy Rebate.
Association Loan Program - Homeowners' Associations may borrow money for common
area improvements, including energy upgrades.
Small Building Material Loan - Borrowers with residential property located in "small
communities" may receive a home improvement loan of up to $100,000. Improvements
may include energy-efficiency and renewable energy.
o Loans for Buildings
Alaska Energy Efficiency Revolving Loan Fund - A loan fund established to finance energy-
efficiency improvements to State, school district, university and municipal buildings in
Alaska.
AEA End Use Study- Implementation Plan Page | 10
July 11, 2011
Brian Saylor &
Associates
o Rebates
Appliance Rebate - Alaskans with proven disabilities may receive a rebate for purchasing
certain Energy Star appliances. Rebates vary from $150 to $500 per appliance.
Home Energy Rebate Program - The rebate program assists homeowners in making the
best energy efficiency improvements to their home. The program requires an AKWarmTM
energy rater to evaluate the home before and after the improvements. The more a
home's energy efficiency improves, the greater the potential rebate. Maximum rebate is
$10,000.
New Home Rebate - A $7500 rebate for the purchase of a newly constructed 5 Star Plus
home.
o Research, Information, Education, Workshops
Cold Climate Housing Research Center - Alaska Housing Finance Corporation funds the
Cold Climate Housing Research Center (located in Fairbanks) to meet the challenge of
building energy efficient, safe, affordable homes in Alaska by providing applied research
on housing across Alaska.
Research Information Center - The Research Information Center (RIC) provides
information and technical assistance for AHFC Energy Programs via the web, phone and
visits. The RIC Library lends books, fact sheets, videos, reports, catalogs and other
resources on northern building, innovative housing construction, energy efficiency,
renewable energy and sustainable technology.
Speakers Bureau - Alaska non-profit agencies may apply for funding for guest speakers on
energy-efficiency, sustainability and renewable energy topics. Speaking events must be
open to the general public.
Speaker Request - Groups may request free presentations on AHFC Energy programs.
Workshops - AHFC offers energy and weatherization workshops, classes and seminars for
consumers, builders, energy raters, lenders, real estate professionals and others through
our training partners. AHFC also offers seminars taught by energy program staff, such as
the popular "Read Your Energy Rating" class.
o Supplemental Housing Development Grant Program (SHG) –
This grant provides funding to regional housing authorities to supplement housing
projects that may include energy-efficient design features in homes.
o Weatherization Program –
To help lower home energy use, homeowners or renters who meet income guidelines
may qualify to have their home weatherized. To apply, individuals should contact the
weatherization service provider or regional housing authority in their area. The
weatherization provider will provide program services at no cost to qualified applicants.
Alaska Home Energy Rebate Program
The Alaska Home Energy Rebate program has driven demand for energy ratings and energy efficiency in
Alaska.
o The rebate program assists homeowners statewide in making the best energy-efficiency
improvements for their home. AHFC is making every effort to provide funds in a fair and
equitable manner.
o The program requires a certified home energy rater to evaluate homes before and after the
improvements. The more a home’s energy efficiency improves, the greater the possible rebate.
AEA End Use Study- Implementation Plan Page | 11
July 11, 2011
Brian Saylor &
Associates
o Since May 2008, AHFC has been accepting AKWarmTM Energy Ratings performed by AKWarmTM
certified energy raters for the Home Energy Rebate program. The cost of these ratings are
covered by AHFC and reimbursed directly to the homeowner.
o The Home Energy Rebate program provides rebates up to $10,000 to homeowners who
improve the energy efficiency of their homes.
o Audit takes 3-4 hours. The state reimburses homeowners for up to $325 for the audit.
Auditors may charge more, with some charging up to $500.
o The State releases up to 20 audits per week. This may ramp up in July 2011 if funding is
increased. The homeowner’s name is released to the auditor, allowing the auditor to contact
them directly. Energy auditors are generally over-subscribed.
o AHFC is responsible for recruitment, waiting list management, applicant screenings, and
other program tasks. Auditors upload audit data into ARIS. There is a standard report
generated from AK Warm that is delivered to the homeowner. The home auditor is
responsible for uploading the audit data into ARIS.
o Potentially, there would be ~240 audits between the months of August – October. This could
increase if additional funding is released.
AKWarm Energy Rating Software
o AKWarm is an energy modeling software tool that was developed to analyze and rate the
total energy use, end-use, and efficiency of residential buildings in Alaska. It can calculate
building energy use, evaluate energy efficiency measures, show compliance with the State of
Alaska's Building Energy Efficiency Standard (BEES) and the Municipality of Anchorage's heat
loss calculations, and is required for the Alaska Home Energy Rebate Program.
o AKWarm was first released in 1996. The current version is limited to residential buildings, and
is particularly strong at assessing building envelope, heat-loss issues, and heating systems.
This software focuses on building thermal loads. It has limited inputs for electrical end uses.
o A beta version of AK Warm for commercial buildings has also been recently released. The
Cold Climate Housing Research Center (CCHRC) and AHFC recently identified key features that
need to be added in order for AKWarm to become a valuable tool for light commercial
building energy audits. Under this project CCHRC will expand AKWarm’s capability by
enhancing its ability to address:
Commercial envelope construction techniques and materials
Electrical loads
Commercial HVAC
Alaska Retrofit Information System (ARIS)
o Premium database - The ARIS project goal: Create a means by which to collect, manage,
access, and report on information relating to AHFC’s rebate and weatherization programs, as
well as other official uses of AKWarm as previously mentioned.
o The data collected provides ALL OF US, including CCHRC (Cold Climate Housing Research
Center) and AHFC, with valuable information about energy conservation effectiveness and
also provides a strong basis for statistical analysis and follow-up in grounded studies such as
our END-USE project.
o ARIS development in 2010 has information about energy usage and the efficiency of public
and commercial buildings throughout Alaska and is preparing for substantial retrofit work in
AEA End Use Study- Implementation Plan Page | 12
July 11, 2011
Brian Saylor &
Associates
2011 and 2012. It uses federal funds being accessed through the American Recovery and
Reinvestment Act of 2009. We understand that CCHRC is collaborating with the Alaska Native
Tribal Health Consortium so that ARIS can be used for their public building retrofit work in
approximately 16 Alaska villages.
o The ARIS system cleanly, securely and reliably integrates AKWarm, weatherization and
energy rebate program data, data processing and reporting.
o CCHRC is managing the development of ARIS on behalf of AHFC. Once development of the
ARIS database is complete, AHFC has indicated they would like CCHRC to maintain and
manage the database.
o ARIS includes information from other official uses of the AKWarm and weatherization
programs such as Building Energy Efficiency Standard (BEES) compliance certifications.
A preliminary review of the housing data available in ARIS has been conducted, and key summary results
which have informed the end use study methodology development are summarized below.
Table 1: Summary of building audit data in ARIS by region, climate zone and house type
Region Climate Zone House Type
Home Counts for "as-is"
audit data in ARIS, 2008-2011 % of Total
Railbelt
7 (Anchorage)
SFD 16,151 70.1%
Mobile Home 84 0.4%
MF - One Unit 1,372 6.0%
MF - Whole Building 333 1.4%
Subtotal 12.9%
8 (Fairbanks)
SFD 2,566 11.1%
Mobile Home 13 0.1%
MF - One Unit 102 0.4%
MF - Whole Building 71 0.3%
Subtotal 2,752 2.0%
All (7 & 8)
SFD 18,505 80.4%
Mobile Home 112 0.5%
MF - One Unit 1,548 6.7%
MF - Whole Building 417 1.8%
Total Railbelt 0 0.0%
SE All (Primarily 6)
SFD 1,633 7.1%
Mobile Home 54 0.2%
MF - One Unit 218 0.9%
MF - Whole
Building 92 0.4%
Total SE 92 0.4%
Rural
N/W All
SFD 407 1.8%
Mobile Home 7 0.0%
MF - One Unit 17 0.1%
MF - Whole
Building 12 0.1%
AEA End Use Study- Implementation Plan Page | 13
July 11, 2011
Brian Saylor &
Associates
Total Rural N/W 443 1.9%
Table 2 Building Energy Rating data in ARIS by year
Year Bldg Count %
1991 - 0%
1992 - 0%
1993 - 0%
1994 - 0%
1995 - 0%
1996 74 0%
1997 256 1%
1998 1,038 3%
1999 87 0%
2000 87 0%
2001 85 0%
2002 66 0%
2003 67 0%
2004 52 0%
2005 47 0%
2006 36 0%
2007 393 1%
2008 5,011 14%
2009 16,842 48%
2010 9,298 27%
2011 1,568 4%
2008-2011 32,719 93%
Table 3: Summary of building audit data in ARIS by audit type
Audit Type Bldg Count %
As_Is 22,696 64.3%
BEES 2,235 6.3%
Post_Improvement 9,896 28.0%
From_Plans 82 0.2%
Wx_As_Is 123 0.3%
Weatherization 152 0.4%
Wx_Post 82 0.2%
Housing_Authority 30 0.1%
Pub_Housing_Post 1 0.0%
ACHP 4 0.0%
Total 35,301
AEA End Use Study- Implementation Plan Page | 14
July 11, 2011
Brian Saylor &
Associates
Table 4: Summary of building audit data in ARIS by house type and vintage
HouseType Vintage Bldg Count % of housing type
Single Family
Pre 1980 9199 50%
1980-2000 8111 44%
2001-2011 1182 6%
Multi Family One Unit
Pre 1980 542 35%
1980-2000 901 58%
2001-2011 104 7%
Multi Family Whole Building
Pre 1980 303 73%
1980-2000 104 25%
2001-2011 10 2%
Mobile Home
Pre 1980 84 75%
1980-2000 28 25%
2001-2011 0 0%
Table 5: Rural ARIS Data by region and community size
Study Area Community Community Size Borough or Census Area
Buildings
in ARIS
Rural North-West AKUTAN 100 - 1,000 Aleutians East Census Area 30
Rural North-West KING COVE 100 - 1,000 Aleutians East Census Area 0
Rural North-West SAND POINT 100 - 1,000 Aleutians East Census Area 27
Rural North-West NELSON LAGOON Less than 100 Aleutians East Census Area 4
Subtotal 61
Rural North-West UNALASKA 1,000 - 5,000 Aleutians West Census Area 1
Rural North-West ATKA Less than 100 Aleutians West Census Area 60
Subtotal 61
Rural North-West ANIAK 100 - 1,000 Bethel Census Area 9
Rural North-West KWETHLUK 100 - 1,000 Bethel Census Area 4
Rural North-West NIGHTMUTE 100 - 1,000 Bethel Census Area 2
Rural North-West BETHEL Hub Bethel Census Area 1
Subtotal 16
Rural North-West KING SALMON 100 - 1,000 Bristol Bay Borough 20
Rural North-West NAKNEK 100 - 1,000 Bristol Bay Borough 6
Subtotal 26
Rural North-West Aleknagik 100 - 1,000 Dillingham Census Area 5
Rural North-West DILLINGHAM Hub Dillingham Census Area 8
Subtotal 13
Rural North-West ILIAMNA 100 - 1,000 Lake and Peninsula Burough 3
Rural North-West KOKHANOK 100 - 1,000 Lake and Peninsula Burough 1
Rural North-West PILOT POINT 100 - 1,000 Lake and Peninsula Burough 6
Rural North-West PORT ALSWORTH 100 - 1,000 Lake and Peninsula Burough 6
Rural North-West PORT HEIDEN 100 - 1,000 Lake and Peninsula Burough 0
Rural North-West IGIUGIG Less than 100 Lake and Peninsula Burough 5
Subtotal 21
Rural North-West UNALAKLEET 100 - 1,000 Nome Census Area 16
AEA End Use Study- Implementation Plan Page | 15
July 11, 2011
Brian Saylor &
Associates
Rural North-West NOME Hub Nome Census Area 2
Subtotal 18
Rural North-West BARROW 1,000 - 5,000 North Slope Borough 57
Subtotal 57
Rural North-West KIANA 100 - 1,000 Northwest Artic Borough 13
Rural North-West SHUNGNAK 100 - 1,000 Northwest Artic Borough 26
Rural North-West KOTZEBUE Hub Northwest Artic Borough 1
Subtotal 40
Rural North-West Hooper Bay 1,000 - 5,000 Wade Hampton Census Area 6
Rural North-West RUSSIAN MISSION 100 - 1,000 Wade Hampton Census Area 3
Subtotal 9
Rural North-West FORT YUKON 100 - 1,000 Yukon Koyukuk Census Area 16
Rural North-West GRAYLING 100 - 1,000 Yukon Koyukuk Census Area 2
Rural North-West ALATNA Less than 100 Yukon Koyukuk Census Area 69
Rural North-West TAKOTNA Less than 100 Yukon Koyukuk Census Area 28
Subtotal 115
Total Rural ARIS 437
Municipality of Anchorage “Plug at 20o” Survey conducted by Craciun Research
o Conducted for MOA, this study was completed in spring of 2011. This data may be used to
estimate energy end use for vehicle plug in. The purpose of the research was to evaluate the
effectiveness of the “Plug@20” media campaign for grant reporting purposes and to track
public opinion in terms of changes in behavior due to air quality. Please see full report as
provided by MOA.
Weatherization Program
o The Weatherization Program, along with the Home Energy Rebate Program, received $360
million in funding from the 2008 Alaska legislature. It provides upgrades for housing for
Alaska’s low- and middle-income renters and homeowners. The program provides energy-
efficient upgrades to about 500 homes in Anchorage each year and to about 4,000 homes
statewide. The focus of weatherization is to increase the safety, energy-efficiency and
comfort of the homes served. Home upgrades may include sealing windows and doors, as
well as insulating walls, floors and ceilings.
o The Weatherization Program is provided for free by specific weatherization agencies and
housing authorities.
o AHFC administers weatherization programs that have been created to award grants to non-
profit organizations for the purpose of improving the energy efficiency of low-income homes
statewide. These programs also provide for training and technical assistance in the area of
housing energy efficiency. Funds for these programs come from the U.S. Department of
Energy as well as AHFC.
RurAL CAP (Rural Alaska Community Action Program, Inc.) – Energy Wise Program
o The Energy Wise Program engages rural Alaskan communities in social marketing efforts
designed to encourage changes in behavior resulting in energy efficiency and energy
conservation. Locally hired crews are trained to educate community residents and conduct
basic energy efficiency upgrades during full-day home visits.
AEA End Use Study- Implementation Plan Page | 16
July 11, 2011
Brian Saylor &
Associates
o RurAL CAP began managing a state program administered by the Alaska Housing Finance
Corporation that offers free weatherization services for low and middle-income residents in
Anchorage on April 1, 2011. The program is being transitioned to RurAL CAP from the
Municipality of Anchorage which has managed it since 1992.
o RurAL CAP operates weatherization programs in western and northern Alaska and the city
and borough of Juneau. In 2010, RurAL CAP weatherized 309 homes in 10 communities.
o The weatherization program provides energy efficiency upgrades to about 500 homes in
Anchorage per year. The focus of weatherization is to increase the energy-efficiency, safety,
comfort and life expectancy of the homes.
o Typical improvements include the caulking and sealing of windows and doors, adding
insulation to walls, floors and ceilings, and improving the efficiency of heating systems,
depending on the assessment.
o Governed by a 24-member Board of Directors representing every region of the state, RurAL
CAP is one of the largest and most diversified nonprofit organizations in Alaska. In fiscal year
2010, RurAL CAP employed 1,048 Alaskans in 91 communities statewide and expended more
than $40 million in conjunction with its for-profit subsidiary, Rural Energy Enterprises.
Energy Wise Program (RuralCap), Existing Data
o The RurAL Cap Energy Wise Program engages rural Alaskan communities in behavior
changing practices resulting in energy efficiency and energy conservation. The end result of
the service is increased energy consumption among rural Alaskans resulting in lower home
heating and electric bills.
o FY 2010 Results: Reduced electrical and home heating costs for residents in 32 Alaskan
villages. (Cost savings are currently being evaluated by University of Alaska’s Institute of
Social and Economic Research)
160 rural Alaskans trained and employed for 6-8 weeks
Energy fairs conducted in 32 communities
2,000 homes received energy use assessments, education and low-cost, efficiency
upgrades
7,500 rural Alaskans educated on energy efficiency and energy conservation strategies
o Energy Wise can be replicated to engage more rural Alaskans in these cost-saving measures.
RurAL CAP is building partnerships with interested housing authorities, regional and statewide
organizations, and local communities.
Communities receive the following:
o 10 locally hired and trained crew members
o On site ‘Launch Week’ by two RurAL CAP staff for hiring and training of local crews
o One (1) Community Energy Fair to engage community residents and organizations
o Up to 100 homes serviced (depending on community size):
Full day home visit from a trained, locally hired crew
Household energy consumption and cost assessment conducted with the resident
Education on energy cost-saving strategies
$300 worth of basic, home energy efficiency supplies installed
AEA End Use Study- Implementation Plan Page | 17
July 11, 2011
Brian Saylor &
Associates
Community Profile Maps from the State of Alaska Division of Community and Regional Affairs (DCRA)
o The DCRA maintains community profile maps2 which include data on each of the buildings in a
community. Many of the maps are current and can provide a comprehensive list of non-
residential buildings in the community. This could serve to establish the non-residential building
frame for rural Alaska. Specifically for this study, this can be used as a starting point for the village
and hub surveys. It also establishes an inventory of buildings throughout the state for which to
extrapolate results to.
Portfolio Manager Data on Facilities Receiving ARRA Funding
AEA uses the EPA portfolio manager for its program. A sampling of the participating buildings is found
below:
Table 6: AEA Commercial Energy Audit Building Listing
Facility Name Building Owner City
Adak, City Hall City of Adak Adak
AIDEA/AEA Building Anchorage
AIRPORT TERMINAL Homer
Akiachak Clinic Akiachak Native Community Akiachak
Akiachak Community Hall Akiachak Native Community Akiachak
Akiachak Day Care Akiachak Native Community Akiachak
AKIACHAK EARLY LEARNING
CHILDHOOD PROGRAM (ELCP)
BUILDING Akiachak Native Community Akiachak
Akiachak Elder and Youth
Center Akiachak Native Community Akiachak
Akiachak New IRA Office Akiachak Native Community Akiachak
Akiachak Police Station Akiachak Native Community Akiachak
Akiachak Tribal office Akiachak Native Community Akiachak
Akiachak Water Plant Akiachak Native Community Akiachak
ANIMAL SHELTER Homer
Barrow City Hall Barrow
Beaver Multi Purpose Building Beaver Village Beaver
Beaver Power Plant and Garage Beaver Village Beaver
Beaver Washeteria and Water
Plant Beaver Village Beaver
Chignik Lagoon School Lake and Peninsula Borough Chignik Lagoon
CITY HALL Homer
City of Anderson shop Anderson
2 http://www.commerce.state.ak.us/dca/profiles/profile-maps.htm)
AEA End Use Study- Implementation Plan Page | 18
July 11, 2011
Brian Saylor &
Associates
City of Barrow Youth Center City of Barrow Barrow
City of Pilot Point Office
Building City of Pilot Point, Alaska Pilot Point
City of Valdez Valdez
Craig City Hall City of Craig Craig
FISH DOCK Homer
Fort Yukon Addie Shewfelt City of Fort Yukon Fort Yukon
Fort Yukon City Hall City of Fort Yukon Fort Yukon
Fort Yukon Gwandak Public
Broadcasting and CATG City of Fort Yukon Fort Yukon
Fort Yukon Power Plant City of Fort Yukon Fort Yukon
Fort Yukon Richard C Carroll
Community center City of Fort Yukon Fort Yukon
Fort Yukon Tribal Hall City of Fort Yukon Fort Yukon
HERC-01 KPC AND BGC Homer
HERC-02 PW MAIN SHOP Homer
HIGH MAST LGT #7 @ RAMP #6 Homer
HIGH MAST LGTS #2,3, & 4 Homer
Holy Cross City Office City of Holy Cross Holy Cross
Holy Cross Community Hall City of Holy Cross Holy Cross
Holy Cross Washeteria City of Holy Cross Holy Cross
Holy Cross Water Treatment City of Holy Cross Holy Cross
Hooper Bay Water Treatment
Plant and Washeteria Hooper Bay
Icy Straits Lodge Hoonah
Kaltag City Office City of Kaltag Kaltag
Kaltag Clinic City of Kaltag Kaltag
Kaltag Fire Hall City of Kaltag Kaltag
Kaltag Washeteria and Water
Plant City of Kaltag Kaltag
Kaltag Youth & Learning Center City of Kaltag Kaltag
Kenaitze Head Start Kenai
Kevin Bell Ice Arena Homer
Koyukuk City Office City of Koyukuk Koyukuk
Koyukuk Clinic And Library City of Kaltag Koyukuk
Koyukuk Power Plant City of Kaltag Koyukuk
Koyukuk Washeteria and Water
Plant City of Koyukuk Koyukuk
Motznik Motznik Anchorage
Nome City Hall Nome
Nome NVFD Nome
Nome Public Works Building Nome
AEA End Use Study- Implementation Plan Page | 19
July 11, 2011
Brian Saylor &
Associates
Nome SCC Center Nome
PH HARBORMASTER OFFICE Homer
Pilot Station ATCO Building City of Pilot Station Pilot Station
Pilot Station Cable Building City of Pilot Station Pilot Station
Pilot Station City Office City of Pilot Station Pilot Station
Pilot Station
Garage/Maintenance Shop City of Pilot Station Pilot Station
Pilot Station Left Station City of Pilot Station Pilot Station
Pilot Station Water Plant City of Pilot Station Pilot Station
POLICE STA/FIRE HALL Homer
PORT & HARBOR MAIN SHOP Homer
PUBLIC LIBRARY Homer
PUBLIC WORKS OFFICE & SHOP Homer
RR @ FISHING LAGOON Homer
RR @ LAUNCH RAMP Homer
RR @ RAMP # 4 Homer
RR @ RAMP #6 AND SYS #4 Homer
RR BY HARBORMASTER OFFICE Homer
Sample Sample City Anchorage
Sample Facility Arlington
Saxman City Hall Saxman
SEWER TREATMENT PLANT Homer
Skagway School Skagway
St. Paul City Hall St. Paul Island
SYS #1 FLT & H MAST LGT#1 Homer
SYSTEM #5 (LIGHT/FLOAT) Homer
WATER PUMP STA @
CROSSMAN Homer
Group Total
Energy-Wise, Planned Audits in the NANA Region
o NANA is the first private organization to fund a regional rollout of this program for six
communities, committing $860,000 to the NANA region Energy Wise project in 2011. In addition
to their partnership, NANA and RurAL CAP are working in cooperation and collaboration with the
Northwest Inupiat Housing Authority (NIHA), the Northwest Arctic Borough (NAB) and individual
village tribal councils to implement the program in Northwest Alaska.
o The NANA region Energy Wise program will be carried out in three phases. Phase I of the project
is set to begin in early February and end in June. Six NANA region villages (Ambler, Buckland,
Kivalina, Noatak, Noorvik and Shungnak) have been invited to participate in the program.
o The total cost of implementing Energy Wise is estimated at approximately $1,700 per home.
Homes receive a full day home visit from a trained, locally-hired crew; assessment of household
energy/kilowatt consumption conducted with the resident; education on energy cost-saving
AEA End Use Study- Implementation Plan Page | 20
July 11, 2011
Brian Saylor &
Associates
strategies; installation of $300 worth of basic, home energy efficiency supplies; and a follow-up
visit and survey 2-3 months after initial home visit.
o Planning and development for Phases II and III are underway as of June 2011.
Alaska Building Energy Efficiency Standard (BEES)
o The Alaska Building Energy Efficiency Standard (BEES) was established by the State of Alaska to
promote the construction of energy-efficient buildings. BEES sets standards for thermal
resistance, air leakage, moisture protection, and ventilation as they relate to efficient use of
energy in buildings.
o All new residential homes and community-owned buildings which began construction on or after
January 1, 1992 must comply with BEES if AHFC or other state financial assistance of any kind is to
be used in its construction or the purchase of a loan. Thermal compliance with BEES may be met
using a prescriptive method, but is most often shown by using the Energy Rating Method
(performed by a certified AKWarmTM energy rater using AKWarmTM computer software, which
requires an energy rating of 4 Star Plus or higher.
o An Energy Efficiency Interest Rate Reduction (EEIRR) is available for homes with an energy rating
of 5 Star and 5 Star Plus.
AHFC Retrofit Energy Assessment for Loan (REAL) Program
o Initial Project Evaluation
o Energy benchmark
o Energy Audits
o Audits by Certified Energy Auditor (CEA) or Certified Energy Manager
(CEM) certification through the Association of Energy Engineers (AEE) or an
AHFC-approved equivalent.
o Energy Performance Contracts (EPCs)
o Energy Service Companies (ESCos) qualified by Alaska Housing Finance
Corporation (AHFC) and Alaska Department of Transportation and Public
Facilities (DOT/PF) may be used.
o Retrofits may be managed by qualified Energy Service Companies (ESCOs) or the
facility owner (if under $250,000).
AEA Energy Audit Program
o In an effort to help achieve the 15% energy efficiency goal set by the Governor and the Alaska
Legislature in 2010, Alaska Energy Authority has initiated a commercial energy audit program as
a first step to help the private commercial sector to achieve energy efficiency savings. The goal is
to audit 130 commercial audits in the near future.
o An open application period was provided from January 5, 2011 to February 15, 2011. AEA
received 137 applications and was able to fund 132 applicants. The program will reimburse
most or all of the cost of qualifying whole-building energy audits up to a stated limit based upon
on the size and type of building.
o Alaska has strong programs in residential and public building sectors, has a small pilot industrial
program, but is currently lacking any commercial energy efficiency programs.
GIS Data Resources
The availability of various GIS databases and resources were explored to identify potentially useful data
for this project. Although there are a few potential GIS resources that may be of use to the team, the
AEA End Use Study- Implementation Plan Page | 21
July 11, 2011
Brian Saylor &
Associates
team was unable to identify GIS databases with significant statewide coverage. Below is a summary of
GIS resources identified to date:
• Kenai Peninsula Borough has a good GIS database. Follow up TBD.
• North Slope Borough has GIS data
• Kotzebue has a GIS database, but they charge for it.
• NW Arctic Borough is starting to develop a GIS database.
• Fairbanks – Uncertain
• Anchorage – Uncertain
Golden Valley Electric Association (GVEA) Heating and Appliance Survey
GVEA conducted a heating and appliance survey of its residential market.
The basics of the survey:
• The sample included 500 Fairbanks homeowners who were also GVA subscribers. (respondents
younger than 18 were screened out)
• The survey data was collected in April and May 2010.
• MOE at +/- 4.4%.
• An “appropriate” number of both land lines and cell phone numbers were included in the sample.
• Data was weighted to match the demographics and phone use.
• The survey took about 15 minutes to administer.
This data provides appliance saturations for various related equipment, but does not provide energy end
use calculations. The table in Appendix 2 summarizes key results from the GVEA survey.
Climatic and Weather Data
Climate and weather are significantly impact building HVAC energy end use. Climate and weather are
related, but for building energy analysis, there is an important difference. Weather refers to the actual
weather conditions (e.g., temperatures, humidity, atmospheric pressure, wind, solar radiation, etc.) that
occur at a specific time and place. Historical weather data is available from different sources, including
automated weather stations, airports, etc. Weather data is highly variable, and available for limited
discrete locations. Climate refers to the average, or typical weather conditions for an area. Hourly
climatic data for key weather variables have been compiled for the state of Alaska. Alaska is broken
down into four climate zones, which have been mapped to census regions. These climate zones are
defined by the BEES and are shown in Figure 1.
Figure 1 Alaska Climactic Zones
AEA End Use Study- Implementation Plan Page | 22
July 11, 2011
Brian Saylor &
Associates
The figure identifies one major climactic zone in Southeast Alaska (Zone 6). The Railbelt Region, on the
other hand, encompasses two climactic regions (Zones 7 and 8). Table 1 provide details on heating
degree days (HDD) for each climate zone, and how these map to the older BEES climate zones.
Table 7: Alaska Climate Zones and HDD3
The US Department of Energy has processed local weather data into long-term climatic averages using
the latest “typical meteorological year” format (TMY3). These files are in Energy Plus Weather format
(.epw) at www.energyplus.gov. They contains typical hourly (8760) data for a range of climatic variables,
including dry bulb temperature, relative humidity, dewpoint temperature, wind speed, wind direction,
station pressure, etc. These files are typically used for building energy simulation. They may be useful for
energy end-use analysis. Data is available for the following locations:
Table 8: Availability of hourly Alaska climatic data (location and station ID)
3 Table from the Alaska BEES modifications to the 2009 International Energy Code,
http://www.ahfc.state.ak.us/iceimages/reference/bees_amendments.pdf.
AEA End Use Study- Implementation Plan Page | 23
July 11, 2011
Brian Saylor &
Associates
Adak NAS 704540 Gulkana 702710 Petersburg 703860
Ambler 701718 Gustavus 703670 Point Hope AWOS 701043
Anaktuvuk Pass 701625 Hayes River 702495 Port Heiden 703330
Anchorage Intl AP 702730 Healy River AP 702647 Saint Marys AWOS 702005
Anchorage-Elmendorf AFB 702720 Homer AP 703410 Sand Point 703165
Anchorage-Lake Hood Seaplane Base
702725
Hoonah 702607 Savoonga 702035
Anchorage-Merrill Field 702735 Hooper Bay 702186 Selawik 700197
Aniak AP 702320 Huslia 702225 Seward 702770
Annette Island AP 703980 Hydaburg Seaplane Base 703884 Shemya AFB 704140
Anvik 702075 Iliamna AP 703400 Shishmaref AWOS 701195
Barrow-W Post-W Rogers AP 700260 Juneau Intl AP 703810 Sitka-Japonski Island AP 703710
Bethel AP 702190 Kake Seaplane Base 703855 Skagway AP 703620
Bettles Field 701740 Kenai Muni AP 702590 Sleetmute 703407
Big Delta-Allen AAF 702670 Ketchikan Intl AP 703950 Soldotna 702595
Big River Lake 702986 King Salmon AP 703260 St Paul Island AP 703080
Birchwood 702746 Kodiak AP 703500 Talkeetna State AP 702510
Chulitna 702606 Kotzebue-Ralph Wein Mem AP 701330 Tanana-Ralph Calhoun AP 701780
Cold Bay AP 703160 McGrath AP 702310 Togiak Village AWOS 703606
Cordova 702960 Mekoryuk 702185 Unalakleet Field 702070
Gambell 702040
Deadhorse 700637 Middleton Island 703430 Unalaska-Dutch Harbor Field 704890
Dillingham AWOS 703210 Minchumina 702460 Valdez 702750
Eielson AFB 702650 Nenana Muni AP 702600 Valdez-Pioneer Field 702756
Emmonak 702084 Nome Muni AP 702000 Whittier 702757
Fairbanks Intl AP 702610 Northway AP 702910 Wrangell 703870
Fort Yukon 701940 Palmer Muni AP 702740 Yakutat State AP 703610
Building energy analysis (including AKWarm) typically uses hourly climate files for estimating building
energy use and end use. Residential HVAC energy use data in ARIS is already based on normalized
climatic data embedded in AKWarm, so no additional weather normalization is required to compare
energy use between years. Similarly, the proposed method of using AKWarm Commercial for the HVAC
energy analysis of th non-residential buildings uses normalized climatic data, so no further weather-
based analysis is needed.
AEA End Use Study- Implementation Plan Page | 24
July 11, 2011
Brian Saylor &
Associates
Municipal/Borough Parcel Data
A valuable source of building data is parcel or assessor databases maintained by municpalities and
boroughs for planning, taxation, and other purposes. A review of existing parcel data was performed to
identify which municipalities have data that is accessible. The following table summarizes parcel data
availability and the level of detail
Municipalities and Boroughs keep detailed records on property at the parcel level for taxation and other
purposes. The details of the data vary by jurisdiction, with some jurisdictions maintaining detailed data
on each parcel, and others containing minimal data. Anchorage, for example, maintains very detailed
parcel data, including information on building size, construction, etc.
The availability of the parcel data also varies by jurisdiction. Many jurisdictions maintain this data in
digital format, although some jurisdictions only maintain the data in a paper-based filing system.
The following table summarizes the digital parcel data4 that is available for this project, with a
qualitative evaluation of its level of detail and currency.
The available parcel data was obtained directly from each jurisdiction or in aggregated form from Ingens
(www.ingens.com), a commercial provider of public information in Alaska.
Table 9: Summary of Alaska Parcel Data Location Parcel Data Availability Data Detailed Current
Anchorage Y Y-Very Y Fairbanks Y N – TBD* Y Juneau Y Y - Moderate Y Kenai Y ? Y Mat-Su Y ? Y Ketchikan Y Y – “Decent” Y Kodiak Y Y – “Decent” Y Unavailable: Haines, Cordova, Everywhere else
Parcel data for the Municipality of Anchorage was obtained and reviewed to aid methodology
development. The following graphs illustrate the range of building types and characteristics in
Anchorage.
4 Parcel data maintained in paper archives would require significantly time and resources to process and is
not an option for this project
AEA End Use Study- Implementation Plan Page | 25
July 11, 2011
Brian Saylor &
Associates
Figure 2: Anchorage parcel count by structure type
859
690
608
503
299
201
199
174
173
135
133
103
74
69
64
56
55
51
48
39
39
38
35
33
33
32
28
0 200 400 600 800 1000
Warehouse
Other
Low Rise Office
Office Warehouse
Retail Single Oc
Retail Multi-Occ
Auto Service Gar
Religious
Hangar
Manufacturing
Restaurant
School
Hotel/Motel Low
Medical Office B
Mini Warehouse
Strip Shopping C
High Rise Office
Mixed Res/Comm
Convenience Food
Boarding House
Discount Dept St
Bank
Nbhd Shopping Ct
Full Svc Auto De
Bar/Lounge
Recreation/Healt
Day Care Center
Parcel Count
AEA End Use Study- Implementation Plan Page | 26
July 11, 2011
Brian Saylor &
Associates
Figure 3: Anchorage parcel count by square footage
0
200
400
600
800
1000
1200
1400
Parcel Countsquare footage
AEA End Use Study- Implementation Plan Page | 27
July 11, 2011
Brian Saylor &
Associates
Figure 4: Anchorage parcel count by year built
Table 10: GVEA Heating and Appliance Survey Summary Data
Question Single
Family
Other Total
MEAN WINTER DAY TEMPERATURE: 66.84 67.61 67.06
MEAN WINTER NIGHT TEMPERATURE: 66.41 67.7 66.79
MEAN SUMMER DAY TEMPERATURE: 62 61.23 61.81
MEAN SUMMER NIGHT TEMPERATURE: 61.77 60.85 61.53
MEAN NUMBER OF WOOD STOVES: 0.36 0.13 0.29
MEAN NUMBER OF PELLET STOVES: 0.04 0.04 0.04
MEAN NUMBER OF OPEN FIREPLACES: 0.05 0.02 0.04
MEAN NUMBER OF FIXED HEATERS: 0.25 0.15 0.22
MEAN NUMBER OF PORTABLE HEATERS: 0.23 0.26 0.24
MEAN AGE OF HEATING SYSTEM: 10.24 12.93 10.83
MEAN NUMBER OF AIR-CONDITIONING UNITS: 1.13 1.69 1.32
MEAN AGE OF AIR CONDITIONING SYSTEM: 5.31 3.68 4.74
MEAN SIZE OF WATER HEATER (GALLONS): 49.27 64.63 52.84
MEAN AGE OF WATER HEATER: 7.41 7.29 7.39
MEAN NUMBER OF MICROWAVE OVENS: 1.02 1.02 1.02
MEAN NUMBER OF REFRIGERATORS: 1.24 1.07 1.19
MEAN NUMBER OF STANDALONE FREEZERS: 0.89 0.48 0.78
MEAN NUMBER OF LARGE SCREEN PLASMA TV'S: 0.48 0.48 0.48
MEAN NUMBER OF OTHER LARGE SCREEN TV'S: 0.49 0.42 0.47
MEAN NUMBER OF OTHER SMALLER TV'S: 1.05 1.05 1.05
MEAN NUMBER OF PERSONAL COMPUTERS: 1.58 1.41 1.53
MEAN NUMBER OF PRINTERS: 0.95 0.73 0.89
MEAN NUMBER OF WIRELESS ROUTERS: 0.63 0.59 0.62
MEAN NUMBER OF GAMING CONSOLES: 0.69 0.74 0.7
MEAN NUMBER OF DVD PLAYERS: 1.32 1.31 1.32
MEAN NUMBER OF CABLE DVR OR TIVO BOXES: 0.95 0.99 0.96
MEAN NUMBER OF MUSIC PLAYING SYSTEMS: 1.23 1.19 1.22
MEAN NUMBER OF ELECTRIC WATERBEDS: 0.02 0.01 0.02
0
200
400
600
800
1000
1200
1400
< 1900 1900-1950 1950-60 1960-70 1970-80 1980-90 1990-2000 2000-11Parcel Countyear
AEA End Use Study- Implementation Plan Page | 28
July 11, 2011
Brian Saylor &
Associates
MEAN NUMBER OF ELECTRIC HOTTUBS: 0.07 0.01 0.05
MEAN NUMBER OF CLOTHES WASHERS: 0.89 0.74 0.85
MEAN NUMBER OF DISHWASHERS: 0.64 0.44 0.58
MEAN NUMBER OF CEILING FANS: 0.99 0.66 0.9
MEAN NUMBER OF WATER WELL PUMPS: 0.58 0.22 0.48
MEAN NUMBER OF DEHUMIDIFIERS: 0.1 0.13 0.11
MEAN NUMBER OF LIGHTBULBS: 26.33 18.01 23.98
MEAN NUMBER OF CFL LIGHTBULBS: 12.68 7.86 11.33
MEAN NUMBER OF OUTDOOR SECURITY LIGHTBULBS: 2.23 1.2 1.95
MEAN NUMBER OF HEADBOLT HEATERS: 1.08 1.44 1.18
MEAN AGE OF HOME: 24.8 25.83 25.06
MEAN SQUARE FOOTAGE OF HOME: 1,865 1,319 1,729
MEAN VALUE OF HOME: $205,233 $131,623 $190,582
MEAN ELECTRIC BILL IN SUMMER: $138 $112 $131
MEAN ELECTRIC BILL IN WINTER: $211 $168 $200
MEAN TOTAL HOUSEHOLD SIZE: 2.84 2.49 2.75
MEAN CHILDREN IN HOUSEHOLD: 0.8 0.63 0.75
MEAN AGE OF RESPONDENT: 49.43 43.86 47.85
AEA End Use Study- Implementation Plan Page | 29
July 11, 2011
Brian Saylor &
Associates
Utility and Municipality Data Request Forms
AEA End Use Study- Implementation Plan Page | 30
July 11, 2011
Brian Saylor &
Associates
Utility and Municipality Data Request Forms
The following data request forms will be submitted to the utilities, municipalities, boroughs, etc. to
support the energy end-use survey. These are preliminary drafts and will be refined before actual use.
Electricity Utility Aggregate Sales By Sector Data Request Form
Utility Name
Contact Name
Address
Dear _________,
The Alaska Energy Authority (AEA) is conducting and energy end-use study… [standard text describing
project here]
As a part of this effort, we need to obtain total statewide electricity sales aggregated by sector for the
20010 – 2011 calendar year. We specifically need breakouts of industrial/manufacturing energy use,
residential energy use, commercial/institutional building energy use, water/waste-water energy use,
streetlighting and military energy use. If data is available at a finer resolution (e.g., by SIC code), that
would be very valuable. We are requesting this data from all utilities. We understand that each utility’s
billing records and ability to disaggregate data are unique, so a representative of the AEA will be calling
to discuss what data you can provide that minimizes your effort while providing necessary data for this
statewide energy study.
If we have misidentified the correct person to direct this query, please let us know and forward this to
the correct person.
Thank you. We appreciate your support.
Sincerely,
Sean Skaling (??)
AEA
Requested Annual Sales Data Summary
Sector Total kWh Sales for 2010-2011 Total kWh Sales for 2009-2010
Residential
Commercial
Institutional and Municipal
Military
Industrial & manufacturing
Street-lighting (if available)
AEA End Use Study- Implementation Plan Page | 31
July 11, 2011
Brian Saylor &
Associates
Water & Waste Water
Treatment
Other
AEA End Use Study- Implementation Plan Page | 32
July 11, 2011
Brian Saylor &
Associates
Fuel Sales by Sector Data Request Form
Utility/Supplier Name
Contact Name
Address
Dear _________,
The Alaska Energy Authority (AEA) is conducting and energy end-use study… [standard text here]
As a part of this effort, we need to obtain total statewide fuel sales aggregated by sector for the 20010 –
2011 calendar year. We specifically need breakouts of industrial/manufacturing energy use, residential
energy use, commercial/institutional building energy use, water/waste-water energy use, streetlighting
and military energy use. If data is available at a finer resolution (e.g., by SIC code), that would be very
valuable. We are requesting this data from all utilities and energy suppliers. We understand that each
utility/supplier’s billing records and ability to disaggregate data are unique, so a representative of the
AEA will be calling to discuss what data you can provide that minimizes your effort while providing
necessary data for this statewide energy study.
If we have misidentified the correct person to direct this query, please let us know and forward this to
the correct person.
Thank you. We appreciate your support.
Sincerely,
Sean Skaling (??)
AEA
Requested Annual Sales Data Summary
Sector Total Fuel Sales for
2010-2011
Total Fuel Sales for
2009-2010
Units
Residential
Commercial
Institutional and Municipal
Military
Industrial & manufacturing
Street-lighting (if available)
Water & Waste Water Treatment
Other
AEA End Use Study- Implementation Plan Page | 33
July 11, 2011
Brian Saylor &
Associates
Street Lighting Data Request Form
Municipality/Agency Name
Contact Name
Address
Dear _________,
The Alaska Energy Authority (AEA) is conducting and energy end-use study… [standard text here]
As a part of this effort, we need to obtain streetlighting data, including fixture counts and bulb types.
We would also like to obtain traffic signal data too, although this is of lesser priority. This will help in a
statewide assessment of streetlighting energy use, energy savings opportunities, and aid in the
development of future lighting efficiency programs, apply for federal funding, and develop energy
efficiency programs.
We would like to request your help in providing the information in the table below. Alternately, if you
have detailed fixture/lamp counts, we can use that.
If we have misidentified the correct person to direct this query, please let us know and forward this to
the correct person.
Thank you. We appreciate your support.
Sincerely,
Sean Skaling (??)
AEA
Requested Traffic Lighting Data
Lighting
Technology
Lamp Wattage Fixture Count Specific Bulb Wattage (if
available), Notes
Incandescent < 100 W
100-250 W
251-500 W
500-750 W
> 750 W
CFL or Flourescent < 100 W
100-250 W
AEA End Use Study- Implementation Plan Page | 34
July 11, 2011
Brian Saylor &
Associates
251-500 W
500-751 W
> 750 W
h < 100 W
100-250 W
251-500 W
500-752 W
> 750 W
HID – High Pressure
Sodium (HPS)
< 100 W
100-250 W
251-500 W
500-753 W
> 750 W
HID – Low Pressure
Sodium (LPS)
< 100 W
100-250 W
251-500 W
500-754 W
> 750 W
HID – Metal Halide
(MH)
< 100 W
100-250 W
251-500 W
500-755 W
> 750 W
HID – Unknown or
Other
< 100 W
100-250 W
251-500 W
500-756 W
> 750 W
LED < 100 W
100-250 W
251-500 W
500-757 W
> 750 W
Other < 100 W
100-250 W
251-500 W
500-758 W
> 750 W
Traffic Lights Total Number of Traffic
Lights
% of Green LEDs Lamps
% of Red LED Lamps
% of Yellow LED Lamps
Notes or comments
Have you installed, evaluated or considered LED
AEA End Use Study- Implementation Plan Page | 35
July 11, 2011
Brian Saylor &
Associates
retrofits? Describe or comment.
Have you installed, evaluated or considered
dual light-level lamps with occupancy sensors?
Describe or comment
Have you installed, evaluated or considered
replacing magnetic ballasts with electronic
ballasts? Describe or comment
Describe any street lighting efficiency or retrofit
programs implemented or planned:
AEA End Use Study- Implementation Plan Page | 36
July 11, 2011
Brian Saylor &
Associates
Water and Waste Water Energy Consumption Data Request Form
Municipality or Borough Name
Contact Name
Address
Dear _________,
The Alaska Energy Authority (AEA) is conducting and energy end-use study… [standard text here]
As a part of this effort, we need to obtain annual water and waste water energy consumption data. Will
you please provide the information in the following table and return this to AEA.
If you have any questions, or if we have misidentified the correct person to direct this query, please let
us know and forward this to the correct person.
Thank you. We appreciate your support.
Sincerely,
Sean Skaling (??)
AEA
Requested Water and Waste Water Treatment Facility Energy Consumption Data
Faciltiy Name Water,
Wastewater,
Pumping Plant,
or other
Total
Electricity Use
for 2010-2011
Total Fuel
Use for
2010 - 2011
Fuel Type
and Unitsj
Total Population Served:
Notes/Comments
AEA End Use Study- Implementation Plan Page | 37
July 11, 2011
Brian Saylor &
Associates
Utility Customer Billing Record Request Form
Name _________,
Address:
Customer ID
I give my permission to and request that [UTILITY NAME] release to obtain my utility consumption
records for 2010 – 2011 to the Alaska Energy Authority for use in the statewide energy end-use study.
Signature: __________________________
Your utility records will be kept confidential. Only aggregated, average utility consumption data will be
made public.
Thank you. We appreciate your support.
Sincerely,
Sean Skaling (??)
AEA
AEA End Use Study- Implementation Plan Page | 38
July 11, 2011
Brian Saylor &
Associates
Residential Energy End Use Survey
AEA End Use Study- Implementation Plan Page | 39
July 11, 2011
Brian Saylor &
Associates
Craciun Research
Residential Energy End Use Survey – DRAFT 5
Approved by Jean Craciun, Research Director 7/8/2011
Introduction
Hi, my name is ________________from Craciun Research, an Alaskan company. I am calling on
behalf of the Alaska Energy Authority to conduct a survey with homeowners [AS NEEDED AND
PROPERTY MANAGERS] about appliances and equipment to understand energy use. As part of
Energy Awareness month this October we have randomly selected buildings to study and we
would like to pay you $50 for your time to assist us with this FIRST-EVER ENERGY END USE
project in Alaska. May I speak with someone who is familiar with your home's energy systems
and appliances?
[AS NEEDED: We are working with the Alaska Energy Authority to gather information on the
energy using equipment installed in homes such as lighting and computers as well as appliances
like your refrigerator. We are conducting this research to understand energy use by consumers;
the State of Alaska goal is to reach 15% energy efficiency by 2020. For your involvement we will
be happy to send you a report regarding your homes energy efficiency. (Jay this was discussed
in our meeting but needs to be determined as to what we would give those who need a little
more incentive. Jean Craciun)
We would like to know what would work best for you to participate; the survey can be
completed online as well as over the phone. While on the phone you can also go online to see
illustrations that may be helpful in completing the survey. If we start now it will take
approximately 30 minutes and we would be happy to answer any questions you have at the end
of the survey. All of your answers are kept strictly confidential; let’s get started.
[SCHEDULE CALL BACK] Would you like to schedule a callback at a more convenient time?
SCREENER QUESTIONS:
1. Before we begin, I would like to confirm your name and contact information. I have you
listed as [READ CONTACT NAME AND ADDRESS]. Is this correct?
1, Yes [PROCEED TO Q2]
2, No [RECORD NAME AND ADDRESS]
2. How would you describe this residence? [READ LIST]
1, Single Family Home [THANK AND TERMINATE]
2, Multi-family Home
3, Manufactured Home or Mobile Home
3. How many people occupy this home?
AEA End Use Study- Implementation Plan Page | 40
July 11, 2011
Brian Saylor &
Associates
4. Do you own or rent this home?
5. How long have you occupied this home?
BUILDING INFORMATION
6. How old is this home?
7. How large is this home in square feet, not including unfinished basements, attics, or
garages?
8. How many floors is this home, including finished basements and attics, but not including
unfinished basements or attics?
9. What is the primary type of heating system in this home? (furnace/forced air, boiler/hot
water, electric baseboard, radiant ceiling/floor, wall unit, floor furnace)
10. What type of fuel does the heating system in this home use?
1, Natural Gas
2, Electric
3, Propane
4, Wood
5, Oil
6, Coal
7, Other
11. Does your home produce any renewable energy?
INTERIOR LIGHTING
1. What is the number, by type, of interior lights used?
1, Incandescent bulbs
2, Compact Fluorescent Lamp (CFL)
3, Halogen
4, LED
5, Fluorescent
6, Lighting Control types (motion sensor?)
7, Other specify___________________
[ASK Q2 – Q3 FOR EACH TYPE OF LIGHT BULB THEY HAVE]
2. What is the wattage?
AEA End Use Study- Implementation Plan Page | 41
July 11, 2011
Brian Saylor &
Associates
3. How many hours per (day) week are they used?
AEA End Use Study- Implementation Plan Page | 42
July 11, 2011
Brian Saylor &
Associates
4. Which types of controls are used on interior lights? [CHECK ALL THAT APPLY]
1, Timers
2, Occupancy Sensors
3, Day lighting controls
4, Other Specify [OTHER THAN MANUAL SWITCHES]
5. [ASK IF THEY HAVE A OCCUPANCY SENSOR] How many Occupancy Sensors are located in
your home?
[ASK Q13 FOR EACH TYPE OF CONTROLS THEY USE]
6. Where in your home are they located?
EXTERIOR LIGHTING
7. Are any exterior lights present at this home?
1, Yes
2, No
8. What is the number, by type, of exterior lights used?
1, Incandescent blubs
2, Compact Fluorescent Lamp (CFL)
3, Halogen
4, LED
5, Fluorescent
6, High Intensity Discharge (Mercury vapor, metal halide, high pressure sodium,
low pressure sodium)
[ASK Q9 – Q10 FOR EACH TYPE OF LIGHT BULB THEY HAVE]
9. What is the wattage?
10. How many hours per week are they used?
11. What types of controls are used on the exterior lights? [Check all that apply]
1, Timers
2, Motion Sensors
3, Day lighting controls
12. How many Occupancy Sensors are located outside your home?
[ASK Q13 FOR EACH TYPE OF CONTROLS THEY USE]
13. Where in your home are they located?
AEA End Use Study- Implementation Plan Page | 43
July 11, 2011
Brian Saylor &
Associates
MAJOR APPLIANCES
14. How many refrigerators are in your home?
[Answer for all Refrigerators]
15. Size
1, Small
2, Standard
3, large
16. What door style of refrigerators?
1, Single door
2, Top mount freezer
3, Bottom mount freezer
4, Side by side
17. Age (years)
18. Is it Energy Star rated?
1, Yes
2, No
3, Don’t know
19. How many standalone freezers are in your home?
[Answer for all standalone freezers]
20. Size
1, Small
2, Standard
3, Large
21. Style
1, Chest
2, Upright
22. Location
1, Inside
2, Outside
3, Garage
23. Age (years)
24. Is it Energy Star rated?
1, Yes
2, No
25. Does this home have an automatic dishwasher?
AEA End Use Study- Implementation Plan Page | 44
July 11, 2011
Brian Saylor &
Associates
1, Yes
2, No
26. How many loads are run in an average week?
27. Is it Energy Star rated?
1, Yes
2, No
3, Don’t know
28. Age of your dishwasher (In years?)
29. Does this home have a clothes washer?
30. Does this home have a clothes dryer?
Washer details:
31. Type
1, Front load
2, Top load
32. Approximate Age (in years)
33. Size:
1, Small
2, Medium
3, Large
34. Loads per week?
35. Is it Energy Star rated?
1, Yes
2, No
Dryer details:
36. Fuel Type (natural gas, propane, fuel oil)
37. Age (years)
38. Do you also use a clothes line or other method for drying your clothes?
39. [IF YES] What percentage of time do you use the clothes dryer?
40. Loads per week
41. Is it Energy Star rated?
1, Yes
2, No
42. Dryer fuel type
1, Electric
2, Natural gas
AEA End Use Study- Implementation Plan Page | 45
July 11, 2011
Brian Saylor &
Associates
3, Propane
43. Do you also use a clothes line or other method for drying your clothes?
1, Yes
2, No
44. [IF YES] What percent of the time do you use the clothes dryer?
PRIMARY COOKING
45. What type(s) of stove do you presently have?
1, Electric
2, Natural Gas/Propane
3, Other
46. Hours per day the stove is used? [ADD HOURS FOR AL BURNERS TOGETHER]
47. What type(s) of oven do you have?
1, Electric
2, Natural Gas/Propane
3, Other
48. Hours per week is the stove used?
49. Do you have a microwave?
50. How many days per week do you use the microwave?
51. When you use the microwave, what is your typical use?
1, Warming drinks & reheating meals
2, Primary cooking
OTHER KITCHEN EQUIPMENT
52. What type of coffeemaker(s) do you own?
1, Drip
2, Percolator
3, Espresso maker
4, Don’t have
53. How many days a week on average do you use that coffeemaker?
54. When you make coffee, how many hours do you typically leave the warmer on?
55. Do you own any of the following? [ACCEPT MULTIPLE ANSWERS]
AEA End Use Study- Implementation Plan Page | 46
July 11, 2011
Brian Saylor &
Associates
1, Broiler
2, Deep Fryer
3, Electric
4, Fry Pan
5, Slow Cooker
6, Toaster
7, Toaster Oven
8, Other TBD
INFORMATION TECHNOLOGY
56. How many __________ are located in your home?
1, Desktop Computers
2, Monitors
3, Laptop Computer
4, Printers/ Multi-Function Device (MFD – Copier, Scanner, Printer)
5, Router/DSL/Cable Modem
6, Fax Machine
7, Home Copy Machine
8, Other Network Equipment
57. What type of printer(s) do you own?
1, Laser
2, Inkjet
ENTERTAINMENT
58. How many TV’s are located in your home?
[ASK Q59-60 FOR EACH TV IN THE HOME]
59. What type of TV?
1, Tube-type (CRT)
2, Projection
3, Plasma
4, LCD
5, DLP
6, LED
7, Other
60. How many hours per week is it in use?
61. How many ________ do you have in your home?
1, Gaming Console
AEA End Use Study- Implementation Plan Page | 47
July 11, 2011
Brian Saylor &
Associates
2, DVD Player
3, Digital Video Recorder (DVR) or TIVO
4, VCR/DVD
5, Cable Box
6, Music playing system
OTHER TBD
62. How many ________ do you have in your home?
1, Transformers
2, Garage door opener
3, Electric waterbed
4, Hot tub
5, Water well pumps
6, Sewage lift pump
7, Sump pump
63. How many headbolt or Engine Block Heaters?
[ASK FOR EACH HEADBOLT/ENGINE BLOCK HEATER THEY OWN]
64. What is the average use?
65. How many months per year is it used?
66. Do you own any Heat Trace?
1, Yes
2, No
67. What is the average length?
68. Do you use decorative lights?
3, Yes
4, No
69. What is the average number of strands?
1, 1-5
2, 5-10
3, 10 +
70. Do you use grow lights?
5, Yes
6, No
71. What is the total wattage?
AEA End Use Study- Implementation Plan Page | 48
July 11, 2011
Brian Saylor &
Associates
72. Do you have any electric vehicles charging?
1, Full-size electric car
2, Golf-cart/neighborhood electric vehicle charging
3, Other
4, None
73. Do you have any other significant electrical consuming appliances?
That was our last question. Thank you for your time and consideration. If you would like
to talk to someone about this project please contact ????AEA????
AEA End Use Study- Implementation Plan Page | 49
July 11, 2011
Brian Saylor &
Associates
Non-Residential Energy Use Survey Instrument
AEA End Use Study- Implementation Plan Page | 50
July 11, 2011
Brian Saylor &
Associates
Craciun Research
Non-Residential Energy Use Survey – DRAFT 7
Approved by Jean Craciun, Research Director 7/8/2011
Introduction
Hi, my name is _______________from Craciun Research, an Alaskan company. I am calling on
behalf of the Alaska Energy Authority to conduct a survey of commercial buildings about energy
usage and equipment. As part of Energy Awareness month this October we have randomly
selected buildings to study and we would like to pay you $75 to assist us with this FIRST-EVER
ENERGY END USE project in Alaska. May I speak with someone who is familiar with your
building's energy using equipment and systems? [LOCATE OWNER: BUILDING MANAGER AND
CONTINUE]
[AS NEEDED: We are working with the Alaska Energy Authority to gather information on the
energy using equipment installed in commercial businesses. We are conducting this research to
understand energy use by consumption; the State of Alaska goal is to reach 15% energy
efficiency by 2020. We would be happy to provide you with an energy efficiency report about
your building. (Jay this was discussed in our meeting but needs to be determined as to what we
would give those who need a little more incentive. Jean Craciun)
We would like to know what would work best for you to participate; the survey can be
completed online as well as over the phone. While on the phone you can also go online to see
illustrations that may be helpful in completing the survey. If we start now the entire survey
could take about 45 minutes. Your business will receive recognition in AEA’s October Energy
Awareness Month newsletter. All of your answers are kept strictly confidential; let’s get started.
[IF NOT NOW SCHEDULE CALL BACK] What would be a better time to start?
[SCREENING QUESTIONS TO BE ADDED]
1. What is the gross or total square footage of all the space in the building, both finished
and unfinished, including basements, hallways, lobbies, stairways, elevator shafts and
indoor parking levels?
1, Building square footage less than 1,000 [less than 1,000 square feet GO TO Q]
2, 1,001 to 5,000 square feet
3, 5,001 to 10,000 square feet
4, 10,001 to 25,000 square feet
5, 25,001 to 50,000 square feet
6, 50,001 to 100,000 square feet
7, 100,001 to 200,000 square feet
8, 200,001 to 500,000 square feet
9, 500,001 to 1 million square feet
AEA End Use Study- Implementation Plan Page | 51
July 11, 2011
Brian Saylor &
Associates
10, Over 1 million square feet
2. What year was this building/property constructed? If there have been major additions,
give us the year the largest portion of the building was completed.
1, Before 1990
2, 1980 to 2000
3, Since 2000
4, Don’t know
3. [ASK IF BEFORE 1980] Has any portion of this building undergone major renovations
since 1980?
1, Yes
2, No
4. [IF YES] Which types of renovations have been done since 1980. [READ LIST. ACCEPT
MULTIPLE ANSWERS]
1, Addition or annex
2, Reduction of enclosed floor space
3, Interior or exterior cosmetic improvements
4, Exterior replacement
5, Interior wall re-configuration
6, HVAC equipment upgrade
7, Lighting upgrade
8, Window replacement
9, Plumbing system upgrade
10, Insulation upgrade
11, Other specify______________
5. Which one of the activities listed accounts for 75% or more of the floor space in this
property?
1, All Commercial
2, All Office/Professional
3, All Warehouse/Storage
4, Colleges
5, Grocery
6, Health
7, Large Office
8, Lodging
9, Miscellaneous
10, Refrigerated Warehouse
11, Restaurant
12, Retail
AEA End Use Study- Implementation Plan Page | 52
July 11, 2011
Brian Saylor &
Associates
13, School
14, Small Office
15, Other Warehouse
16, Vacant (Jon?)
17, Other specify:
6. Looking at this list, please tell me which of these best describes the owner of this
property.
1, Government
2, Property management company
3, Other corporation, partnership, or LLC
4, Religious organization
5, Other non-profit organization
6, Privately-owned school
7, Individual owner(s)
8, Other specify ___________________
7. How many businesses or organizations are there in the property?
8. Now I have some questions about the hours that this building is normally open.
“Normally open” means the hours when the usual activities occur. Do not consider the
building “to be open” if only maintenance, housekeeping, or security personnel are
present.
8a. Is this building normally open 24 hours a day, seven days a week?
1, Yes
2, No
9. Thinking of Monday through Friday, is this building open all five days, open some of
these days, or is it not open at all Monday through Friday?
1, All five days
2, Some of these days
3, Not open at all Monday through Friday
10. Is this building normally open at all on the weekend?
[IF ONLY OPEN ONE DAY OF THE WEEKEND; CONSIDER IT TO BE OPEN.]
1, Yes
2, No
11. Please tell me if energy was used for any of these purposes in this building in the past
12 months.
AEA End Use Study- Implementation Plan Page | 53
July 11, 2011
Brian Saylor &
Associates
[PROBE FOR ANY OTHERS; PLEASE ENTER ALL THAT APPLY]
1, Heating the building
2, Cooling the building
3, Ventilation
4, Water Heating
5, Commercial or institutional cooking or food serving
6, Refrigeration
7, Exterior Lighting
8, Interior Lighting
9, Office Equipment
10, Miscellaneous
11, Process
12, Motors
13, Air Compressors
14, ** IF VOLUNTEERED ** None of these uses, but some energy used
15, ** IF VOLUNTEERED ** No energy used in the past 12 months
12. Does this building have the ability to generate electricity, including for emergency
backup?
1, Yes
2, No
13. Please tell me which of these energy sources were used in this building for any purpose
in the past 12 months.
1, Electricity
2, Natural gas
3, Fuel oil, diesel or kerosene
4, Bottled gas, also known as LPG or propane
5, Steam piped in from a separate building or utility
6, Hot water piped in from a separate building or utility
7, Wood, coal, or solar thermal panels
8, Other source or sources [SPECIFY]
14. What was the main energy source for heating? [SAME ANSWERS AS ABOVE]
[THE MAIN ENERGY SOURCE FOR HEATING IS THE ENERGY SOURCE USED TO HEAT
MOST OF THE SQUARE FOOTAGE IN THE BUILDING MOST OF THE TIME. DO NOT
INCLUDE ELECTRICITY IF IT IS USED ONLY TO RUN FAN MOTORS.]
1, Electricity
2, Natural gas
3, Fuel oil, Diesel, or Kerosene
4, Bottled gas
AEA End Use Study- Implementation Plan Page | 54
July 11, 2011
Brian Saylor &
Associates
5, District steam
6, District hot water
7, Wood
8, Coal
9, Solar
10, Other specify________________
15. Was any of the building heated to less than 50 degrees Fahrenheit?
[AREAS MAY BE HEATED TO LESS THAN 50 DEGREES TO PREVENT PIPES FROM
FREEZING.]
1, Yes
2, No
16. Please tell me which heating equipment types are used in this building.
1, Furnaces that heat air directly, without using steam or hot water
2, Boilers inside the building that produce steam or hot water
3, Packaged heating units, other than heat pumps
4, Individual space heaters, other than heat pumps
5, Heat pumps
6, District steam or hot water piped in from outside the building
7, Other heating equipment
17. Which of these best describes how the temperature for heating is usually changed?
1, Time-clock thermostat
2, Thermostat is manually reset
3, Part of “Energy Management and Control System (EMCS)”
18. Now I have some questions about uses of space and equipment in this Building. Please
tell me if the property includes any of the following? [ACCEPT MULTIPLE ANSWERS]
1, A data center/ computer server “farm”
2, Agricultural [GREENHOUSE]
3, Education
4, Food sales or services
5, Health care
6, Industrial manufacturing
7, Laundry/dry cleaning
8, Offices or professional offices
9, Public assembly
10, Public order or safety
11, Religious worship
12, Residential [APARMENTS, ETC]
13, Retail
14, Service
AEA End Use Study- Implementation Plan Page | 55
July 11, 2011
Brian Saylor &
Associates
15, Lodging, [AS IN A HOTEL OR ROOMING HOUSE]
16, Warehouse/ storage
17, Vacant [TBD]
18. Other [SPECIFY]
19. Is any space used for institutional or commercial food preparation and serving, such as
kitchens, restaurants, snack bars, cafeterias, steam tables, or warming areas?
1, Yes
2, No
20. [IF NO] Is any space used for incidental food preparation by the tenants?
1, Yes
2, No
3, Don’t know
[IF ANY SPACE IS USED FOR FOOD PREP OR FOOD SALES OR SEFVICES ASK THIS SET –
THROUGH END FOOD SERVICE]
** Refrigerator section
21. How many residential-type refrigerators are in the building?
0, SKIP TO FREEZER SECTION]
[USE 999 FOR DON’T KNOW
22. [IF ONE] What size is it – small, standard or large?
23. [IF MORE THAN ONE] How many are small, how many are standard and how many
large?
24. Small (LIST NUMERICAL)
25. Standard (LIST NUMERICAL)
26. Large (LIST NUMERICAL)
27. Is it/Are they all/ located in a heated area, in a semi-heated area such as a garage or in
an unheated area?
1, All in a heated area
2, All in semi-heated areas
3, All in unheated areas
4, Mixed
28. [IF MIXED] How many are in a heated area?
29. [IF MIXED] How many are in a semi heated area?
30. [IF MIXED] How many are in an unheated area?
31. [ASK IF MORE THAN ONE] Are they all the same age?
AEA End Use Study- Implementation Plan Page | 56
July 11, 2011
Brian Saylor &
Associates
1, Yes
2, No
3, Don’t know
32. [IF YES OR IF THERE IS ONLYONE] How old are they?
33. [IF NOT THE SAME AGE] Can you estimate the ages?
___ at ____ years
___ at ____ years
___ at ____ years
___ at ____ years
___ at ____ years
34. Does it/Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
35. [IF SOME] How many?
End Refrigerator section
** Freezer section
36. How many residential-type freezers are in the building?
0, SKIP TO COMMERCIAL REFRIGERATOR SECTION]
[USE 999 FOR DON’T KNOW
37. [IF ONE] What size is it – small, standard or large?
38. [IF MORE THAN ONE] How many are small, how many are standard and how many
large?
39. Small (LIST NUMERICAL)
40. Standard (LIST NUMERICAL)
41. Large (LIST NUMERICAL)
42. Is it/Are they all/ located in a heated area, in a semi-heated area such as a garage or in
an unheated area?
1, All in a heated area
AEA End Use Study- Implementation Plan Page | 57
July 11, 2011
Brian Saylor &
Associates
2, All in semi-heated areas
3, All in unheated areas
4, Mixed
43. [IF MIXED] How many are in a heated area?
44. [IF MIXED] How many are in a semi heated area?
45. [IF MIXED] How many are in an unheated area?
46. [ASK IF MORE THAN ONE] Are they all the same age?
1, Yes
2, No
3, Don’t know
47. [IF YES OR IF THERE IS ONLY ONE] How old are they? __ __
48. [IF NOT THE SAME AGE] Can you estimate the ages?
___ at ____ years
___ at ____ years
___ at ____ years
___ at ____ years
___ at ____ years
49. Does it/Do they all have Energy Star Ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
50. [IF SOME] How many?
End Freezer section
Begin commercial refrigeration section
51. Does the property have any commercial-sized refrigerator cases and freezer cases?
1, Yes
2, No [SKIP TO NEXT SECTION]
52. How many?
AEA End Use Study- Implementation Plan Page | 58
July 11, 2011
Brian Saylor &
Associates
53. [IF MORE THAN ONE] I need to ask some questions about each unit. Bear with me.
Starting from the largest one
[FOR EACH ASK]
54. What is the condensing unit type -- self-contained or remote condensing?
1, Self contained
2, Remote condensing
55. At what operating temperature is it kept? [READ LIST]
1, Medium (38 F)
2, Low (0 F)
3, Ice cream ( -15 F)
56. Does it have transparent doors, solid doors or no doors?
1, Transparent
2, Solid
3, No door
57. Is it horizontal, semi vertical or vertical?
1, Horizontal
2, Semi-vertical
3, Vertical
58. How many units are there or how many linear feet does it or they occupy?
End commercial refrigeration section
Begin Dishwasher section
59. How many small, residential-size dishwashers are there in the building? [IF NEEDED] –
for example in break rooms or residential units
60. Are they Energy Star rated?
1, All are
2, Some are
3, None are
4, Don’t know
61. About how old are they? [ENTER SINGLE NUMBER OR RANGE]
End Dishwasher section
Begin Commercial Cooking section
62. Does the building have a commercial scale kitchen?
1 Yes
2, No
AEA End Use Study- Implementation Plan Page | 59
July 11, 2011
Brian Saylor &
Associates
63. [IF YES] What is it used for? [READ LIST]
1, Quick serve restaurant
2, Restaurant
3, Dining hall
4, Occasional food service for clubs and parties
5. Other [SPECIFY]
64. How many meals are served per weekday?
65. How many on weekends?
66. What type of cooking fuel is used?
1, Gas
2, Fuel oil
3, Electric
4, Wood, charcoal
5, Other
End Commercial Cooking section
Begin Office equipment section
67. How many desk-top computers are there in the property?
0, None [SKIP TO LAP-TOP SECTION]
___ ___ ___
999, Don’t know
68. What percentage, roughly, are turned off at night?
0 None
___ __
999 [Don’t know
69. What percent are sleep-enabled? [IF NEEDED, “TURN THEMSELVES DOWN
AUTOMATICALLY WHEN NOT IN USE FOR A WHILE”]
0 None
___ __
999 [Don’t know
70. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
AEA End Use Study- Implementation Plan Page | 60
July 11, 2011
Brian Saylor &
Associates
71. [IF SOME] About what percentage?
72. How many lap-top computers are there in the property?
0, None [SKIP TO MONITOR SECTION]
___ ___ ___
999, Don’t know
73. What percentage, roughly, are turned off at night?
0 None
___ __
999 [Don’t know
74. What percent are sleep-enabled? [IF NEEDED, “TURN THEMSELVES DOWN
AUTOMATICALLY WHEN NOT IN USE FOR A WHILE”]
0 None
___ __
999 [Don’t know
75. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
76. [IF SOME] About what percentage?
77. How many Monitors [COMPUTER SCREENS] are there in the property?
0, None [SKIP TO SACANNERSECTION]
___ ___ ___
999, Don’t know
78. What percentage, roughly, are turned off at night?
AEA End Use Study- Implementation Plan Page | 61
July 11, 2011
Brian Saylor &
Associates
0 None
___ __
999 [Don’t know
79. What percent are sleep-enabled? [IF NEEDED, “TURN THEMSELVES DOWN
AUTOMATICALLY WHEN NOT IN USE FOR A WHILE”]
0 None
___ __
999 [Don’t know
80. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
81. [IF SOME] About what percentage?
82. How many scanners are there in the property?
0, None [SKIP TO COPIER SECTION]
___ ___ ___
999, Don’t know
83. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
84. [IF SOME] About what percentage?
85. How many copiers are there in the property?
0, None [SKIP TO FAX SECTION]
___ ___ ___
999, Don’t know
86. How many are black and white laser?
87. How many Color laser?
88. How many other copiers?
89. How many are small – 1 to 25 pages per minute?
90. How many medium – 26 to 50 pages per minute?
91. How many large – 51 pages or faster?
AEA End Use Study- Implementation Plan Page | 62
July 11, 2011
Brian Saylor &
Associates
92. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
93. [IF SOME] About what percentage?
94. How many fax machines?
0, None [SKIP TO MULTI-FUNCTION DEVICE SECTION]
___ ___ ___
999, Don’t know
95. How many inkjet?
96. How many laser?
97. How many other?
98. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
99. [IF SOME] About what percentage?
100. How many multi-function devices are there – machines that copy, scan and print?
0, None [SKIP TO MULTI-FUNCTION DEVICE SECTION]
___ ___ ___
999, Don’t know
101. How many are inkjet?
102. How many black and white laser?
103. How many color laser?
104. How many other?
105. [ASK IF LASER] What sizes are your laser devices? [ACCEPT MULTIPLE ANSWERS]
How many home size __ __
How many small office __ __
How many large office __ __
106. What are the speeds in images per minute?
___ at ____
AEA End Use Study- Implementation Plan Page | 63
July 11, 2011
Brian Saylor &
Associates
___ at ____
___ at ____
___ at ____
___ at ____
107. Do they all have Energy Star ratings?
1, Yes, all
2, Some
3, No, none
4, Don’t know
108. [IF SOME] About what percentage?
109. How many routers do you have?
0, None
___ ___ ___
999, Don’t know
110. Are there any computer servers in the building?
1, Yes
2, No [GO TO LAUNDRY SECTION]
3, Don’t know [GO TO LAUNDRY SECTION]
111. Do you have any of the following?
1, A server closet with 1 or 2 servers
2, A server room, 3 to 24 servers
3, A localized data center, 25 to 199 servers
4, A mid-tier data center 200 to 899 servers
5, an Enterprise class data center, 900 or more servers
6, None of the above
End computer section
Begin residential laundry section
112. In the building, are there shared laundry facilities for all residential units or facilities in
each unit?
1, Shared laundry facilities
2, Facilities in all residential units
3, Both
AEA End Use Study- Implementation Plan Page | 64
July 11, 2011
Brian Saylor &
Associates
113. [IF FACILITIES IN EACH UNIT] Do you supply the washers and dryers, or do the tenants
supply their own?
1, Landlord supplied machines
2, Tenant supplied
3, Some of each
114. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPLIED MACHINES] What types of
washing machines are provided? [READ LIST]
1, Front loader
2, Top loader
3, Some of each
115. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPLIED MACHINES] About how old
are the machines?
116. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPLIED MACHINES] Are they Energy
Star rated?
1, Yes, all
2, Some
3, No, none
4, Don’t know
117. [IF SOME] About what percentage?
118. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPIED MACHINES] Are the machines
small, medium or large?
1, Small (LIST NUMERICAL)
2, Medium (LIST NUMERICAL)
3, Large (LIST NUMERICAL)
119. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPIED MACHINES] Could you make a
guess at the number of loads per week?
120. [IF SHARED LAUNDRY FACILITIES OR LANDLORD SUPPIED MACHINES] What do your
dryers use for fuel?
1, Natural gas
2, Propane
3, Electric
4, Fuel oil
5, Don’t know
End residential laundry section
AEA End Use Study- Implementation Plan Page | 65
July 11, 2011
Brian Saylor &
Associates
Begin commercial laundry section
121. Which of the following services do you offer the public? [ACCEPT MULTIPLE ANSWERS –
READ LIST]
1, Commercial Laundromat or Washeteria
2, Commercial laundry
3, Commercial dry cleaning
4, Hotel/motel linens
5, Hospital linens
6, Uniforms
7, Other [SPECIFY]
122. About how many pounds per year of laundry do you do?
123. What percent of your loads use hot water?
124. How many large commercial washers do you have
125. How many residential-scale washers?
126. What kind of fuel do you use to for your dryer(s)?
1, Natural gas
2, Propane
3, Electric
4, Fuel oil
5, Don’t know
127. Does your facility use an extractor to spin water out of the laundry before it goes to the
dryer?
1, Yes,
2, No
3, Don’t know
128. Does your laundry have a heat recovery system?
1, Yes
2, No
3, Don’t know
129. [IF YES] Please describe it.
130. Do you use recovered heat from a generator or manufacturing process?
1, Yes
2, No
3, Don’t know
AEA End Use Study- Implementation Plan Page | 66
July 11, 2011
Brian Saylor &
Associates
131. Do you use an ozone system?
1, Yes
2, No
3, Don’t know
End commercial laundry section
Begin Lighting section
132. First I’d like to ask about interior lighting in your building, beginning with incandescent
bulbs. What percentage of the total building is lit by incandescent bulbs?
133. What is the average wattage of these lights?
134. About how many hours a week are they on [READ LIST]
1, On 24 hours a day, 7 days a week
2, On dark hours when building is occupied 7 days a week
3, On dark hours when building is occupied 5 days a week
4, On all dark hours, 7 days a week
5, On all dark hours 5 days a week
6, Off and on as needed
135. Halogen bulbs? What percentage of the total building is lit by them?
136. What is the average wattage?
137. About how many hours a week are they on?
1, On 24 hours a day, 7 days a week
2, On dark hours when building is occupied 7 days a week
3, On dark hours when building is occupied 5 days a week
4, On all dark hours, 7 days a week
5, On all dark hours 5 days a week
6, Off and on as needed
138. Next, CFL – compact fluorescent lights– what percentage of the total building is lit by
them?
139. What is the average wattage?
140. About how many hours a week are they on?
1, On 24 hours a day, 7 days a week
2, On dark hours when building is occupied 7 days a week
3, On dark hours when building is occupied 5 days a week
4, On all dark hours, 7 days a week
5, On all dark hours 5 days a week
6, Off and on as needed
141. Fluorescent? What percentage of the total building is lit by them?
142. What is the average wattage?
143. About how many hours a week are they on?
AEA End Use Study- Implementation Plan Page | 67
July 11, 2011
Brian Saylor &
Associates
1, On 24 hours a day, 7 days a week
2, On dark hours when building is occupied 7 days a week
3, On dark hours when building is occupied 5 days a week
4, On all dark hours, 7 days a week
5, On all dark hours 5 days a week
6, Off and on as needed
144. LED? What percentage of the total building is lit by them?
145. What is the average wattage?
146. About how many hours a week are they on?
1, On 24 hours a day, 7 days a week
2, On dark hours when building is occupied 7 days a week
3, On dark hours when building is occupied 5 days a week
4, On all dark hours, 7 days a week
5, On all dark hours 5 days a week
6, Off and on as needed
147. Now, about exterior lighting. Do you have exterior lighting on the building that is
activated automatically or is always on?
1, Yes, activated automatically
2, Yes, always on
3, Some of each
4, No [GO TO NEXT SECTION]
148. [ASK IF ACTIVATED AUTOMATICALLY] What activates the lights [READ LIST ACCEPT
MULTIPLE ANSWERS]
1, The light automatic goes on at twilight
2, Motion sensor
3, A timer is programmed to go on regularly
4, Other [SPECIFY]
149. What percentage of it is lit by LED lighting?
150. By High Density Discharge – mercury vapor and the like?
151. [IF USES HIGH DENSITY ASK] What kind do you have? [READ LIST]
1, Mercury vapor
2, Metal Halide,
3, High pressure sodium
4, Low pressure Sodium
5, Don’t know
152. What is the total wattage of your exterior lighting?
End Lighting section
AEA End Use Study- Implementation Plan Page | 68
July 11, 2011
Brian Saylor &
Associates
Begin Other section
153. How many of the following does your property include?
154. Hot Tubs
155. Saunas
156. Water well pumps
157. Sewage lift pump
158. Sump pump
159. Head bolt/ Engine block heaters
160. Heat trace
161. Grow lights
162. Greenhouse
163. Security systems
164. OTHER TBD
165. Does the building contain any manufacturing, industrial processing or similar end uses?
166. Does the building contain any other large end-uses we have not asked about?
That was our last question. Thank you for your time and consideration. If you would
like to talk to someone about this project please contact AEA?????
AEA End Use Study- Implementation Plan Page | 69
July 11, 2011
Brian Saylor &
Associates
Preliminary Sample Size Southeast Alaska & Railbelt
AEA End Use Study- Implementation Plan Page | 70
July 11, 2011
Brian Saylor &
Associates
Table 11 Preliminary Sample Size- Southeast Alaska-10% MOE
10% Margin of Error with a 90% confidence level, with the exception of warehouse structure(18.2%
MOE)
Southeast Alaska
Juneau Kodiak/Cordova Ketchikan Sitka Other Southeast Total Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food Service 49 20 17 7 11 5 8 3 14 6 99 41 10.0%
Warehouse 386 14 134 3 90 2 62 2 109 3 781 20 18.2%
Institutional 63 24 22 8 15 6 3 1 6 2 109 42 10.0%
Health Care 16 11 6 4 4 3 3 2 5 3 34 23 10.0%
Lodging 26 13 9 4 6 3 10 5 20 10 71 35 10.0%
office 159 29 55 10 37 7 25 5 35 6 311 56 10.0%
Mercantile/ retail 154 28 54 10 36 6 25 4 43 8 312 56 10.0%
Service 59 22 21 8 14 5 9 3 17 6 120 44 10.0%
Other 164 9 57 10 38 7 26 5 40 7 325 57 10.0%
Total 1,076 170 374 65
25
1 43
17
1 30 289 51 2,162
37
4 3.6%
AEA End Use Study- Implementation Plan Page | 71
July 11, 2011
Brian Saylor &
Associates
Table 12 Preliminary Sample Size- Southeast Alaska 15% MOE
15% Margin of Error with a 90% confidence level, with the exception of warehouse
structure(18.2% MOE)
Southeast Alaska Juneau Kodiak/Cordova Ketchikan Sitka Other Southeast Total Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food Service 49 12 17 4 11 3 8 2 14 3 99 24 15.0%
Warehouse 386 10 134 3 90 2 62 2 109 3 781 20 18.2%
Institutional 63 14 22 5 15 3 3 1 6 1 109 24 15.0%
Health Care 16 8 6 3 4 2 3 1 5 3 34 17 15.0%
Lodging 26 8 9 3 6 2 10 3 20 6 71 22 15.0%
office 159 15 55 5 37 3 25 2 35 3 311 28 15.0%
Mercantile/ retail 154 14 54 5 36 3 25 2 43 4 312 28 15.0%
Service 59 12 21 4 14 3 9 2 17 4 120 25 15.0%
Other 164 15 57 5 38 3 26 2 40 3 325 28 15.0%
Total 1,07
6 108 374 37 251 25
17
1 17 289 30
2,16
2 216 5.2%
AEA End Use Study- Implementation Plan Page | 72
July 11, 2011
Brian Saylor &
Associates
Table 13 Preliminary Sample Size- 10% MOE
10% Margin of Error with a 90% confidence level, with the exception
of warehouse structure(18.4% MOE)
Railbelt
Total
Railbelt
Anchorag
e
Mat-
Su Kenai
Valdez/Ot
her Fairbanks
Denali/SE
FBKS
Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food Service 351 30 116 10 112 10 12 1 116 10 20 2 727 62 10.0%
Warehouse 2,797 10 807 3 892 3 93 0 922 3 75 0 5586 20 18.4%
Institutional 455 26 335 19 145 8 15 1 150 9 12 1 1112 64
10.0
% Health Care 113 23 80 16 36 7 4 1 37 7 3 1 273 55 10.0%
Lodging 185 22 193 22 59 7 6 1 61 7 20 2 524 61 10.0%
office 1,15
1 38 44 1 367 12 38 1 379 12 27 1 2006 66
10.0
%
Mercantile/ retail 1,118 33 308 9 356 11 37 1 368 11 30 1 2217 66 10.0%
Service 427 24 414 23 136 8 14 1 141 8 11 1 1143 64 10.0%
Other 1,18
5 37 71 2 378 12 39 1 390 12 25 1 2088 66
10.0
%
Total 7,782 243 2368 107 2,481 77 259 8 2,564 80 223 9 15,676 524 3.5%
AEA End Use Study- Implementation Plan Page | 73
July 11, 2011
Brian Saylor &
Associates
Table 14-Preliminary Sample Size Railbelt- 15% MOE
15% Margin of Error with a 90% confidence level, with the exception of warehouse
structure(18.4% MOE)
Railbelt
Total Zone
Anchorage
Mat-Su Kenai
Valdez/ Other
Railbelt Fairbanks
Denali/SE
FBKS
Location Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample Units Sample MOE Bldg Type
Food Service 351 14 116 5 112 4 12 0 116 5 20 1 727 29 15.%
Warehouse 2,797 10 807 3 892 3 93 0 922 3 75 1 5586 20 18.%
Institutional 455 12 335 9 145 4 15 1 150 4 12 0 1112 30 15.%
Health Care 113 12 80 8 36 4 4 0 37 4 3 0 273 28 15.%
Lodging 185 10 193 11 59 3 6 1 61 3 20 1 524 29 15%
office 1,151 17 44 1 367 5 38 1 379 6 27 0 2006 30 15%
Mercantile/
retail 1,118 15 308 4 356 5 37 1 368 5 30 0 2217 30 15%
Service 427 11 414 11 136 4 14 0 141 4 11 0 1143 30 15%
Other 1,185 17 71 1 378 5 39 1 390 6 25 0 2088 30 15%
Total 7,782
11
9 2368 52
2,48
1 38 259 5 2,564 39 223 5
15,67
6
25
6 5.1%