1. BEHAVIORAL AND OPERATIONAL WASTE:
AN ANALYSIS OF PROGRAM OPPORTUNITIES
Presented at the International Energy Program
Evaluation Conference – Chicago 2013
August 15, 2013
Adam Burke, Opinion Dynamics Corporation
Roger Baker, Commonwealth Edison

2. Overview
2
 Purpose and objectives
 Approach
 Summary of results
 Applying the results

3. What were we looking for?
3
 ComEd commissioned study to address two primary
research objectives
 Provide real primary data to inform program planning -
identify gaps in current program offerings and
opportunities to acquire deeper savings
 Examine technological and behavioral waste together

4. What did we find?
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End Use Profile
Percentage of Total Annual
GWh
Waste Profile
Percentage of Annual
Lighting GWh
Lighting
Efficient Usage: How little
lighting energy could be used if
all customers installed efficient
lighting and turned lights off
when not needed?
Efficient Usage: How little
lighting energy could be used if
all customers installed efficient
lighting and turned lights off
when not needed?
Waste: How much of the
remaining usage is due
to inefficient equipment
vs. wasteful behavior?
Waste: How much of the
remaining usage is due
to inefficient equipment
vs. wasteful behavior?
Current Usage: How
much electricity
actually goes to each
end use?
Current Usage: How
much electricity
actually goes to each
end use?

5. How are these results being used?
 Assess the efficacy of technology and behavioral program
options to optimize DSM investment
 Rebates vs. information/messaging
 Identify and prioritize among “opportunity pockets”
 Customer segments or end use types where significant savings
can be realized through behavioral program elements
 Determine if gaps exist in current program offerings
5

6. End-Use Approach
 Comprehensive assessment of electricity usage and waste
for six C&I end uses:
 Extensive primary data collection and metering
 Defined efficient technologies and behaviors
 Enhanced engineering analysis to assess energy usage and
waste
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 Lighting
 Cooling
 Ventilation
 Refrigeration
 Motors
 Office Equipment

7. What is Waste?
 Current usage: Incorporates actual customer behaviors and
the efficiency level of currently-installed equipment
 Technological waste: Savings opportunities associated with
upgrading to more efficient equipment – e.g., ENERGY STAR,
CEE Tier 3, or program guidelines
 Behavioral Waste: Savings opportunities associated with
using optimal settings and using equipment only when
needed
 Efficient Usage: The remainder – only “efficient” given the
waste categories that we included in our analysis
7

8. Conceptualizing Usage and Waste
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Run Time or Hours
Watts
Efficient Use
Behavioral Waste =
B+C
A
C
B
Technological
Waste = A+B
Shared Waste =
B

9. C&I Primary Data Collection
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Telephone Interviews: 1,519
Onsite Audits: 347
Metering Sites: 70
July – November 2012

10. Targeted Commercial & Industrial Customers
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 Office Buildings
 Health Services
 Retail
 Food Service
 Warehouse
 Grocery/Convenience
 Non-Public Education
 Lodging/Hospitality
 All Other Commercial
 Less than 100 kW
 100 – 400 kW
 Greater than 400 kW
Commercial Industrial

11. C&I End-Use Profile: Current Usage
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Lighting
28%
Cooling
15%
Motors
16% Office
Equipment
9%
Ventilation
8%
Refrigeration
6%All Other
19%
All C&I
Comm.
Total
Industrial
Total
Lighting 28% 31% 17%
Motors 16% 7% 38%
Cooling 15% 15% 11%
Office Equipment 9% 10% 3%
Ventilation 8% 9% 7%
Refrigeration 6% 6% 1%
All Other 19% 22% 23%
TOTAL 100% 100% 100%
Percentage of Annual Commercial & Industrial GWh

12. Overall C&I Usage and Waste Summary
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Efficient
Usage
61% 6%
12%
21%
Energy Use Classified in Baseline Study
Technology Waste
21-27%
Behavioral Waste
12-18%
Shared Waste*
* Either technology or behavioral waste, depending on which is addressed first

13. C&I Lighting Usage & Waste Results
Efficient
Usage
42%
23%
12%
23%
28%
Technology Waste: 23-35%
 Upgrade to high-efficiency lamps,
including LEDs
 Assume current lighting design
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 Turn off lights when space not
occupied
 Implement multiple methods of
lighting controls
Behavioral Waste: 23-35%
Shared Waste*
Penetration: 100%
10,926 GWh Current Usage
6,356 GWh Current Waste
* Either technology or behavioral waste, depending on which is addressed first

14. Commercial Lighting Approach
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• Adjusted customer-reported HOU
based on light logger findings
• Lighting waste when space
unoccupied for 15+ minutes
Technology Behavior
• Standardized lighting upgrades
• Savings varies by space type
Existing Lighting Type Efficient Lighting
Type
CFLs < 25 W LEDs
CFLs >= 25 W Leave as is
Halogens <= 50 W LEDs
Halogens > 50 W CFLs
Metal Halide <= 100 W Leave as is
Metal Halide > 100 W HPT8, 32 W
Example Upgrades

15. C&I Lighting Highlights: Technology
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 T12s are common
 Penetration of T12s is
higher than T8s
 Saturation of T8s slightly
higher than T12s
 Technological waste highest in
industrial and lodging
segments
 Industrial: Driven by metal
halides and T12s
 Lodging: Driven by
incandescents and T12s
Percentage of Linear
Fluorescent Fixtures
41% 45%
47%
51%
12%
4%
0%
50%
100%
Commercial Industrial
T5
T8
T12

16. C&I Lighting Highlights: Behavior
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 High penetration of
manual controls
 Behavioral waste highest
for industrial and lodging
segments
 Light logger study found
30% waste associated
with leaving lights on
when space not occupied
(with 15 minute time-out)
Percentage of Light Fixtures
with Control Types
1%
5%
4%
10%
2%
Dimmer or Dual Level
Switches
Timers
Occupancy Sensors
Energy Management
System
No controls
Manual
Controls
78%
Other
22%

17. Approach is Flexible
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 Variations are possible depending on research
objectives and existing data:
 Target a small number of end uses for
deeper analysis
 Dive deeply into key C&I segments of
interest
 Waste can be calculated using different
thresholds or definitions
 Combine usage and waste analysis with
targeting or segmentation studies to
identify new opportunities

18. Benefits to Comprehensive Program Planning
 Identify pockets of energy-savings opportunities within
segments or end-uses
 Identify gaps in program offerings
 Generate more focused targeting and effective
marketing of programs
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 Identify potential behavior-based
components to incorporate into
existing programs
 Inform decisions on how to best
apportion resources between
behavioral and technology-based