NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Plug Load Behavioral Change 

Demonstration Project 

Ian Metzger, Alicen Kandt, and Otto VanGeet

Produced under the direction of Pacific Northwest National Laboratory by the 

National Renewable Energy Laboratory (NREL) under Interagency Agreement 

IWO-138785 and Task No. WUR1.1000

Technical Report

NREL/TP-7A40-52248

August 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Plug Load Behavioral Change 

Demonstration Project 

Ian Metzger, Alicen Kandt, and Otto VanGeet

Prepared under Task No. WUR1.1000

Technical Report

NREL/TP-7A40-52248

August 2011 
NOTICE

This manuscript has been authored by employees of the Alliance for Sustainable Energy, LLC (“Alliance”) under 

Contract No. DE-AC36-08GO28308 with the U.S. Department of Energy (“DOE”).

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
iii 

Contacts and Team Members for the Plug Load Behavioral Change 

Demonstration Project

Pacific Northwest National 

Laboratory

Kim Fowler

Research Engineer

Phone: 509-372-4233 

E-mail: kim.fowler@pnnl.gov

Will Gorrissen

Research Engineer 

Phone: 509-372-6447 

E-mail: Will.Gorrissen@pnnl.gov

General Service Administration

Joni Teter 

Office of Federal High Performance Green 

Buildings 

Phone: 303-236-2210 

E-mail: joni.teter@gsa.gov

Judi Heerwagen

Office of Federal High Performance Green 

Buildings 

Phone: 206-522-0354 

E-mail: judith.heerwagen@gsa.gov 

EPA Region 8 Headquarters

Craig Greenwell

Facilities Manager

Phone: 303-312-7087 

E-mail: Greenwell.Craig@epamail.epa.gov 

Bill Daniels

Infrastructure Program Manager

Phone: 303-312-6074 

E-mail: Daniels.William@epamail.epa.gov

Strategy Group, LLC

Charlie Johnson

Strategy Group, LLC. 

Phone: 303-295-1918 

E-mail: charlie.johnson@strategygroups.com 

Jim Blackledge

Strategy Group, LLC 

Phone: 303-916-9270 

E-mail: jim.blackledge@strategygroups.com

National Renewable Energy 

Laboratory

Ian Metzger*

Lead - Mechanical Engineer

Phone: 303-384-7415 

E-mail: ian.metzger@nrel.gov

Alicen Kandt*

Senior Engineer 

Phone: 303-384-7518 

E-mail: alicen.kandt@nrel.gov

Otto VanGeet

Senior Engineer 

Phone: 303-384-7369 

E-mail: otto.vangeet@nrel.gov

*Report authors to whom any questions 

should be directed 
iv 

Acknowledgements

The National Renewable Energy Laboratory engineers are grateful to the facility 

managers/directors, engineers and operators for their generous assistance and cooperation. 

Special thanks to Craig Greenwell and Bill Daniels from EPA and Charlie Johnson and Jim 

Blackledge from Strategy Group for hosting the research demonstration project and providing 

documentation. Special thanks to Kim Fowler and Will Gorrissen from PNNL, and Joni Teter 

and Judi Heerwagen from GSA for their participation and guidance throughout the project. Also, 

special thanks to all the EPA staff who participated in the demonstration project. 
v 

Executive Summary

The EPA’s Region 8 Headquarters is located in a LEED Gold certified building in Denver, 

Colorado. Fully occupied in January 2007, the 420,000 ft2, nine-story building houses 

approximately 775 employees.1

Any device that plugs into wall outlets distributed throughout a building is a plug load. These 

loads do not relate to general lighting, heating, ventilation, cooling, or water heating, and 

typically do not provide comfort to the occupants

In addition to earning numerous design awards and a LEED 

Gold rating for the building, EPA, GSA (the government leaseholder), and All Capital/GPT (the 

building’s owner) have made sustainable operations a priority. The EPA building participated in 

a research study quantifying the effect of different mechanical and behavioral change approaches 

on plug load energy reduction. 

2

. Plug loads account for an average of 9%3 but 

as much as 28% of the electricity consumption in office buildings depending upon the nature of 

the work.

4

This research study was undertaken in an effort to identify the most effective way to reduce plug 

load energy, using three primary approaches. The first was an automated energy management 

system which turns off equipment when a pod was unoccupied for a given period of time. The 

second method involved behavioral change using information feedback and selected messaging. 

The third involved behavioral change by encouraging competition amongst occupants. 

Plug loads can also affect cooling and heating loads and associated cooling energy 

use, but these affects are not considered in this report. An entity that strives to reduce energy use, 

energy costs, and greenhouse gas emissions in an effort to operate sustainably must devise a 

strategy to reduce plug load electricity consumption. 

The research was conducted by establishing a baseline, testing experimental applications, and a 

returning to baseline to see if behaviors “stick” when messaging is no longer deployed. Each 

condition was applied for four weeks. The study ran from February 2011 to June 2011 and was 

conducted with 126 occupants on four different floors of the building. Occupants sit in clusters 

of six to eight people, called pods. Energy usage data were collected per pod. 

In the first experimental phase, a control system automatically turned off plug load devices after 

15 minutes of no occupancy in a pod. In the second experimental phase, occupants were sent 

weekly letters with information on the energy use associated with a variety of plug load devices 

 

1 “Greening EPA,” U.S. Environmental Protection Agency: http://www.epa.gov/greeningepa/facilities/denverhq.htm. Accessed July 26, 2011. 2 Lobato, C., Pless, S., Sheppy, M., and Torcellinin, P. “Reducing Plug and Process Loads for a Large Scale, Low 

Energy Office Building: NREL’s Research Support Facility.” NREL/CP-5500-49002, February 2011. 3 “Buildings Energy Data Book,” Energy Efficiency and Renewable Energy: 

http://buildingsdatabook.eren.doe.gov/TableView.aspx?table=3.1.13. Accessed July 26, 2011. 4 “Survey of Plug Loads,” Energy Efficient Products: http://www.efficientproducts.org/product.php?productID=11. 

Accessed July 26, 2011. 
vi

as well as conservation tips. All letters are included in Appendix A. In the final phase, occupants 

were sent notifications comparing carbon dioxide equivalent (CO2e) accountability per person 

and encouraging occupants to participate in a competition to reduce their energy consumption. 

All notifications are included in Appendix B. 

The study found that the most effective method for reducing plug loads was through the control 

system, which turned off plug load devices after 15 minutes of no occupancy in a pod. The 

competition among pods was also effective at reducing plug load energy consumption, although 

less so than the control system. The letters sent to occupants educating them about plug load 

energy use and opportunities for conservation had negligible savings. Extrapolated annual 

energy savings estimates and associated cost savings for the 126-person test group are presented 

in Table 1. 

Table 1: Cost Savings Analysis for Each Experimental Method

Experimental 

Method 

Total Annual 

Energy Savings 

(Extrapolated 

for 775 People) 

(kWh/yr) 

Percent 

Energy 

Reduction 

from 

Baseline

Percent of Whole 

Building Electricity 

Reduction 

(Extrapolated for 775 

People) 

Total 

Annual 

Cost 

Savings 

($/yr) 

Total 

CO2e 

Savings 

(tons) 

Control 

System 34,757 21% 0.9% $3,476 30 

Letters -407 0% 0.0% $-41 0

Competition 9,912 6% 0.3% $991 9

This study shows that energy control systems have a potential for large plug load-related energy 

savings, as does social norming through competitions. This study did not combine the three 

methods (energy control system, educational letters, and competition) at the same time to 

quantify energy savings. Based on these findings the best case scenario for energy savings would 

include a control system and occupant competition with significant promotion for occupant 

education. Alternatively, implementing a competition as a behavioral change mechanism 

without a control system may be the most cost effective. However, without the submetering 

system the savings could not be verified and normalized comparisons of occupant energy 

consumption would not be possible. 

Lessons learned stemmed from all aspects of this project. 

• Site support and occupant participation and interest are critical to the outcome of 

behavioral change research. 

• Approval from the EPA Union required additional time and planning for all interactions 

with the occupants and required the research to comply with protocols that would ensure

that occupant anonymity could be maintained. Anonymity is typically required for field 

research and should be included in dashboard interfaces for displaying data. 
vii 

• Installation of the control and submetering system took longer and was more costly than 

expected. However, costs are expected to decrease significantly with scale and 

experience working in federal facilities. 

• The wired installation of the control system and communications were very cumbersome

and complex. Wireless communications and controls with “plug and play” installation 

are expected to have less complexity, quoted at lower costs, and are currently 

commercially available. 

• Cybersecurity created a hurdle for the dashboard and data storage of the submetering 

system. 

• Several generalizing assumptions are required to account for all of the uncontrolled 

factors in behavioral change research. 

• Occupant participation and willingness to take actions to reduce energy was met with 

some resistance 

• The control system had significantly higher energy and cost savings compared to 

behavioral change methods. However, it is expected that incentivizing behavioral change 

could significantly improve occupant participation and energy reductions. Previous 

studies have indicated that the level of occupant involvement usually correlates with the 

incentive.5

• Implementing behavioral change mechanisms without a control system would 

significantly improve the cost effectiveness. However, energy and cost savings could not 

be verified and normalized occupant energy comparisons could not be generated without 

a submetering system.

 Incentives can be in many forms including money, prizes, food, public 

recognition, etc. 

• Developing the appropriate plug load management process can have a significant 

influence on the success of energy reduction goals. This may include behavioral change 

mechanisms, controls systems, or other policies. Establishing a program champion, 

developing a business case, benchmarking, identifying occupant needs, equipment 

selection/replacement, controlling equipment schedules, institutionalizing reduction 

measures, and promoting occupant awareness can all be critical steps in the process.

6

• Higher energy savings from plug loads may be accomplished by expanding the research 

beyond just workstations. This study focuses on the workstation and did not include 

equipment typically found in shared spaces such as large multifunction printers, 

refrigerators, microwaves, water-coolers, and other equipment. 

 

• Prolonged energy savings were found to be significant during the occupied period and 

weekend periods. However, longer term monitoring is recommended to verify the 

estimated prolonged savings. 

 

5 Birch, D., and Veroff, J. (1966). Motivation: A Study of Action. Belmont, CA: Brooks/Cole 6 Lobato, C., Sheppy, M., Bracknet, L., and Torcellinin, P. MELs Management Selection Process and Lessons Learned. 

NREL/TP-5500-51708, July 2011. 
viii 

Next steps for additional research may include: 

• Combining multiple methods to determine if even greater savings could be achieved

• Comparing the energy savings between non-incentivized and incentivized competition 

• Evaluating the results of institutionalizing reduction measures through information 

technology (IT) policy7

• Achieving occupancy-based control at the cubicle level rather than at the pod level

• Install and monitor a wireless monitoring and control system and compare to the current 

system.

The GSA Green Proving Grounds program is pursuing future work to test different plug load 

submetering and control systems for ease of installation, scalability, energy savings, and costeffectiveness. The lessons learned from this research should be incorporated into the future 

work. 

 

7 Assessing and Reducing Plug and Process Loads in Office Buildings. NREL/BR-5500-51199, June 2011. 
ix 

Table of Contents

Contacts and Team Members for the Plug Load Behavioral Change Demonstration Project..................... iii

Acknowledgements...................................................................................................................................... iv

Executive Summary ....................................................................................................................................... v

Table of Contents......................................................................................................................................... ix

1. Background............................................................................................................................................. 1

2. Introduction............................................................................................................................................ 2

3. Previous Related Work ........................................................................................................................... 3

4. Building Overview................................................................................................................................... 4

4.1 Utility Analysis.................................................................................................................................. 8

5. Research Methodology .......................................................................................................................... 9

5.1 Inventory Analysis............................................................................................................................ 9

5.2 Baseline Periods............................................................................................................................. 10

5.3 Experimental Phase ....................................................................................................................... 10

5.3.1 Control System...................................................................................................................... 10

5.3.2 Letters................................................................................................................................... 10

5.3.3 Competition .......................................................................................................................... 11

6. Results .................................................................................................................................................. 13

6.1 Inventory and ASHRAE Schedules.................................................................................................. 13

6.2 Data Conditioning and Development of the Baseline ................................................................... 15

6.2.1 Submetered and ASHRAE Comparison ................................................................................. 17

6.3 Average Energy Profiles................................................................................................................. 19

6.4 Statistical Results........................................................................................................................... 22

6.4.1 Totals..................................................................................................................................... 23

6.4.2 Occupied Period .................................................................................................................... 24

6.4.3 Unoccupied Period................................................................................................................ 25

6.5 Energy and Cost Savings ................................................................................................................ 26

6.5.1 Energy Savings ...................................................................................................................... 26

6.5.2 Cost Savings .......................................................................................................................... 29

6.6 Occupant Feedback........................................................................................................................ 30

6.6.1 Experimental Phase .............................................................................................................. 30

6.6.2 Exit Survey............................................................................................................................. 31

6.7 Prolonged Energy Savings: Post-Experiment Baseline.................................................................. 32

7. Conclusion ............................................................................................................................................ 34

8. Appendix............................................................................................................................................... 37

Appendix A: Letters to Occupants........................................................................................................ 37

Appendix B: Competition Notifications to Occupants ......................................................................... 41

Appendix C: Exit Survey ...................................................................................................................... 43

Appendix D: Summary of Applicable Legislation.................................................................................. 44
1 

1. Background

The Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL) is one of the 

ten national laboratories managed by the DOE Office of Science. PNNL also performs research 

for other DOE offices as well as government agencies, universities, and industry to deliver 

breakthroughs in science and technology. PNNL has been tasked by GSA’s Office of Federal 

High-Performance Green Buildings, under the American Recovery and Reinvestment Act 

(ARRA), to provide energy efficiency and research support. PNNL, in collaboration with the 

National Renewable Energy Laboratory (NREL), is providing energy efficiency and research 

support for the U.S. General Services Administration (GSA) and the Environmental Protection 

Agency (EPA). 

The GSA is a leader among federal agencies in aggressively pursuing energy efficiency (EE) 

opportunities for its facilities and installing renewable energy (RE) systems to heat and power

these facilities. Since the enactment of the Energy Policy Act of 2005 (EPAct 2005), Executive 

Order (EO) 13423 (2007), the Energy Independence and Security Act (EISA 2007), and 

Executive Order 13514 (2009), other federal agencies are looking to GSA for strategies for 

meeting the EE and RE goals laid out by these pieces of legislation. 

The EPA is a federal agency established to protect human health and the environment. EPA’s

main priorities under the current administration include: taking action on climate change, 

improving air quality, assuring safety of chemicals, cleaning up our communities, protecting 

America’s water, expanding the conversation on environmentalism and working for 

environmental justice, and building stronger state and tribal partnerships. EPA strives to meet 

and exceed the energy efficiency and renewable energy goals laid out by federal legislation. 

NREL is the only DOE national laboratory solely dedicated to advancing EE and RE 

technologies and applications. Since its inception, NREL has supported both federal and private 

sectors in implementing EE and RE systems and strategies to lower energy use and to meet 

remaining energy needs with resources having minimal environmental impact. 

The EPA Region 8 Headquarters located at the Wynkoop Building in downtown Denver, 

Colorado participated in a research study to quantify the effect of different control methods and 

behavioral change approaches on plug load energy reduction. Through active participation by the 

site to implement the submetering and behavioral change demonstration projects, EPA will be 

closer to meeting and exceeding the goals set forth in the applicable legislation. Applicable 

legislation includes but is not limited to the EPAct 2005, EO 13423, EISA 2007, EO 13514, and 

other mandates. The specific goals set forth in the applicable legislation are summarized in 

Appendix D. 
2 

2. Introduction

Any device that plugs into wall outlets distributed throughout a building is a plug load. These 

loads do not relate to general lighting, heating, ventilation, cooling, or water heating, and 

typically do not provide comfort to the occupants8. Plug loads account for an average of 9%9 but 

as much as 28% of the electricity consumption in office buildings depending upon the nature of 

the work.

10 Standby power–electricity used by appliances and equipment while they are switched 

off or not performing their primary function–associated with plug loads presents a large 

opportunity for energy savings. This power is consumed by power supplies, the circuits and 

sensors needed to receive a remote signal, soft keypads and displays including miscellaneous 

light-emitting diode (LED) status lights. Standby power use is also caused by circuits that 

continue to be energized even when the device is off. Standby power use is responsible for 

approximately 1% of global carbon dioxide (CO2) emissions.11

Plug loads can also increase cooling loads, and decrease heating needs, and affect the associated 

HVAC energy use. An entity that strives to reduce energy use, energy costs, and greenhouse gas 

emissions in an effort to operate sustainably must devise a strategy to reduce plug load electricity 

consumption. 

This research was conducted at the EPA Region 8 Headquarters building to examine methods of 

decreasing energy consumption associated with plug loads. Desktop computers (permanent 

computer towers), desktop printers, laptop computers, under-cabinet task lights, and computer 

monitors are the top energy-consuming workstation plug load equipment in the EPA Region 8 

Headquarters building. 

This study aimed to identify the most effective way to reduce plug load energy, using three

primary approaches. The first is an automated energy management system which turns off 

equipment when it is unused for a certain period of time. The second involves behavioral change 

using information feedback and selected messaging. The third involves behavioral change using 

a competition to encourage occupants to reduce their energy consumption. 

This study, conducted over five four-week study periods, included a baseline, testing 

experimental applications, and a returning to baseline to see if behaviors “stick” when messaging 

is no longer deployed. 

 

8 Lobato, C., Pless, S., Sheppy, M., and Torcellinin, P. “Reducing Plug and Process Loads for a Large Scale, Low 

Energy Office Building: NREL’s Research Support Facility.” NREL/CP-5500-49002, February 2011. 9 “Buildings Energy Data Book,” Energy Efficiency and Renewable Energy: 

http://buildingsdatabook.eren.doe.gov/TableView.aspx?table=3.1.13. Accessed July 26, 2011. 10 “Survey of Plug Loads,” Energy Efficient Products: http://www.efficientproducts.org/product.php?productID=11. 

Accessed July 26, 2011. 

11“Frequently Asked Questions,” Standby Power, Lawrence Berkeley National Laboratory: 

http://standby.lbl.gov/faq.html. Accessed July 26, 2011. 
3 

3. Previous Related Work

There have been many studies using submetering systems to quantify and understand how plug 

loads consume energy and the amount of time spent in active mode or sleep mode. There have 

also been many psychology studies about how to influence human behavior. However, not many 

studies have attempted to implement behavioral change as it relates to plug load energy 

consumption. While reviewing previous work, lessons can be learned from various sources that 

are often seeking to accomplish different goals. However, understanding both the psychology of 

behavioral change and the energy implications of plug loads is essential to this project. 

Previous research using submetering systems to monitor plug loads have been very successful in 

quantifying various metrics about plug load energy consumption. For example, the American 

Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) has published a 

great deal of information about diversity factors and power consumption in active, standby, and 

off modes.

12 Submetering systems have also been used to examine how specific pieces of 

equipment contribute to the bigger picture energy consumption of the building and metrics such 

as kilowatt-hours per square foot (kWh/ft2) have been used to extrapolate annual energy 

consumption.13 Other studies have used submetering to establish baselines and have 

extrapolated market analysis for plug load-related policies.

14

The psychology of behavioral change is a very important topic that needs further examination in 

the context of energy consumptions. A study entitled Changing Behavior and Making It Stick: 

The Conceptualization and Management of Conservation Behavior by Raymond De Young has 

provided an excellent resource for this project. De Young’s study examines conservation 

behavior in order to change individual behavior while reducing the need for repeated 

intervention. It also categorizes and evaluates behavioral change techniques. This previous 

work has found informational prompting to be untrustworthy and nondurable, with varied 

effectiveness. Material incentives initiate rapid change and effective results but are nondurable 

once the material incentive has been obtained. Social pressure and material disincentives were 

found to initiate rapid change and effective results but also create negative psychological 

resistance from individuals. Commitment techniques encouraging individuals to “pledge” their 

commitment to behavioral change for a specific amount of time were found to be the most 

durable and effective. However, securing individual commitment has proven to be difficult to 

accomplish.

 These publications are extremely 

useful for understanding how and when energy is being consumed by various office plug load 

equipment. 

15

 

12 ASHRAE Fundamentals, 2005.

 

13 Moorfield, L., Frazer, B., and Bendt, P. Office Plug Load Field Monitoring Report. ECOS: December, 2008. 14 Porter, S., Moorfield, L., and May-Ostendorp, P. Final Field Research Report. ECOS: October, 2006. 15 Young, R. “Changing Behavior and Making It Stick: The Conceptualization and Management of Conservation 

Behavior,” Environment and Behavior, 25:4, p. 485: July, 1993.
4 

4. Building Overview

The EPA Region 8 Headquarters facility (Wynkoop Building) was designed and constructed as a 

high-performance building through a public-private partnership comprised of GSA Region 8 

(R8), EPA R8, EPA’s Office of Administration and Resource Management, EPA’s Sustainable 

Facilities Practices Branch, and Opus Northwest with Zimmer Gunsul Frasca Architects. The 

design and construction process was well-documented and all partners are still actively engaged 

in activities aimed at better understanding facility operations and energy consumption. The EPA 

Region 8 Headquarters Building was selected to serve as the site for several ongoing research 

activities, including plug load behavioral change. 

EPA’s R8 Headquarters is located in lower downtown Denver. It is a privately owned facility 

built to federal specifications and leased on behalf of EPA by the General Services 

Administration (GSA).16 Occupied in early 2007, the 420,000 ft2

, nine-story building houses 

approximately 775 employees.17 In addition to earning numerous design awards, the building 

achieved a Gold rating under the U.S. Green Building Council’s Leadership in Energy and 

Environmental Design (LEED) for new construction.18 The building, located at 1595 Wynkoop 

Street, includes nine floors of office space, two levels of below-grade parking, and ground-level 

retail spaces.19

 

 

16 “Sustainable Design and Operations,” EPA: http://www.epa.gov/region8/building/design.html. Accessed July 26, 

2011

17 “Green Building and Green Operations,” EPA: http://www.epa.gov/region8/building/index.html. Accessed July 

26, 2011 18 “1595 Wynkoop Building Awards and Recognition,” EPA: http://www.epa.gov/region8/building/awards.html. 

Accessed July 26, 2011 19 “EPA Region 8 Headquarters,” EPA: http://www.epa.gov/region8/building/pdf/EPApressbook.pdf. Accessed July 

26, 2011.
5 

Figure 1: EPA's Region 8 Headquarters Building (Photo by Alicen Kandt, NREL/PIX 19447)

The building features a variety of sustainable and efficient design features. These include:

• Siting. The building is located on an urban brownfield that previously housed a United 

States postal annex.20 

• Construction management and materials. A variety of sustainable materials were used 

throughout the building, including corn-based fabric and wheatboard, recycled glass tile, 

recycled-content carpets, recycled steel, cork floors, bamboo wall panels and doors made 

with rice hull cores. Additionally, fly ash was used in the concrete portions of the 

building and regional materials–those manufactured, produced or harvested within 500 

miles of the building–were used for more than 50% of the structure’s manufactured 

materials. Construction waste was also reduced, with as much as 80% of the total waste 

generated recycled or diverted from local landfills. 

• Water efficiency and water management. High-efficiency and waterless plumbing 

fixtures are employed throughout the building and are estimated to achieve a 44% water 

savings over standard buildings. Moreover, a stormwater management system removes 

80% of total suspended solids and 40% of total phosphorus from the water running off 

the building. 

• Green roof. A green roof was installed as a means of both removing pollutants from 

stormwater and reducing the rate and quantity of stormwater runoff. Populated with 

 

20“EPA Region 8 Headquarters,” EPA: http://www.epa.gov/region8/building/pdf/EPApressbook.pdf. Accessed July 

26, 2011. 
6 

native, drought-tolerant plant species that minimize irrigation requirements, the 20,000 ft2 

vegetated roof covers three terrace levels and filters stormwater while reducing the urban 

heat-island effect of the building. 

• Energy efficiency. To reduce heating, ventilation, and air conditioning (HVAC) energy 

consumption, an under-floor air distribution system with individual controls is used 

throughout all office floors, as are occupancy sensors, energy-efficient lighting, 

optimized insulation levels, daylight-responsive lighting controls, and daylight 

redirection and control devices optimized for daylight harvesting. 

Variations of a glazed curtain-wall system were designed for the different façades–the 

sunward (southeast and southwest) façades were designed with horizontal exterior 

sunshades and a system of internal light shelves designed to cut glare and solar gain. The 

windward (northeast and northwest) façades have a series of exterior vertical shades to 

cut glare from low-angle winter sun while simultaneously harvesting diffused light from 

the clear north sky. As the net result, 75% of workstations receive significant daylight. 

• Green power. A 10-kilowatt (kW) rooftop photovoltaic (PV) system generates electricity 

for the facility. 

Additionally, EPA offsets 100% of the electricity consumption of this building with 

renewable energy certificates (RECs).21 

• Occupancy awareness. The EPA realizes that building a green building is not the only 

component in sustainable and efficient operations. Important factors include how the 

building is operated and the behavior of the occupants. Region 8’s Environmental 

Management System helps the EPA improve environmental performance by quantifying 

the impact of operations (e.g., electricity, water, materials management and 

transportation) and taking actions to reduce those impacts. Region 8 is working toward a 

project Gold rating in LEED for Existing Buildings: Operations and Maintenance (LEED 

EBOM) to ensure continued high performance in building design and operations.

Additionally, many employees are allowed to work alternative schedules, such as four 

ten-hour days, or telecommute. There are abundant bicycle parking racks in the parking 

garage, and the building is located in close proximity to buses and light-rail lines. 

Employees are given EcoPasses or commuter checks for public transit.22

 

21 “Denver, Colorado, Region 8 Office,” EPA: 

 

http://www.epa.gov/greeningepa/facilities/denver-hq.htm. Accessed 

July 26, 2011. 

22 “Benefits Designed with Today’s Commuter in Mind,” Commuter Check: 

http://www.commutercheck.com/Home.aspx. Accessed July 26, 2011. 
7 

• Plug loads. EPA management created guidelines associated with what types of plug load 

equipment are or are not allowed at workstations. Some equipment, such as space 

heaters, fans, and humidifiers, require approval. The policy states that approval will be 

granted after attempts to make the employee more comfortable using building controls 

have not been successful. The building is required by the lease to provide tempered air 

within comfort range guidelines. Only in cases in which the building HVAC systems 

can’t provide acceptable temperature control will the use of space heaters, fans, or 

humidifiers be approved.23

These sustainable design features were modeled to use 39% less energy than the ASHRAE 

Standard 90.1-1999 baseline. The building’s modeled energy usage was 47,500 British Thermal 

Units (Btu) per square-foot per year.

24

Figure 2

Based on the electricity and steam consumption over the 

past four years the building is operating around 52.7 kBtu/ft2. An overview of the incremental 

energy savings attributed to energy use categories is displayed below in . 

Figure 2: Incremental Energy Savings Pie Chart25

 

23 Final Decision, 9/13/2006: Personal Property Decisions and Procedures for 1595 Wynkoop 24“EPA Region 8 Headquarters, “Whole Building Design Guide”: 

http://www.wbdg.org/references/cs_epadenver.php. Accessed July 26, 2011. 25“Denver, Colorado, Region 8 Office,” EPA: http://www.epa.gov/greeningepa/facilities/denver-hq.htm. Accessed 

July 26, 2011. 
8 

4.1 Utility Analysis

The EPA Region 8 Headquarters Building has two primary panels providing electrical service to 

the building. The annual electricity consumption was analyzed for the past four years (2007-

2010). The average total electricity consumption between 2007 and 2010 was 4,034,397 

kilowatt-hours per year (kWh/yr). The average blended electric rate for the period analyzed was 

$0.10/kWh. Figure 3 shows the total (meter #1 plus meter #2) monthly electricity consumption 

and Figure 4 shows the total (meter #1 plus meter #2) monthly electricity cost. 

Figure 3: Total Electricity Consumption for EPA Region 8 Headquarters Building (Including Meters #1 and #2)

Figure 4: Total Electricity Costs for EPA Region 8 Headquarters Building (Including Meters #1 and #2)

0 

100,000 

200,000 

300,000 

400,000 

500,000 

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Electricity (kWh)

GSA - EPA

Electricity Consumption

2007

2008

2009

2010

$-

$5,000 

$10,000 

$15,000 

$20,000 

$25,000 

$30,000 

$35,000 

$40,000 

$45,000 

$50,000 

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

GSA - EPA

Electricity Cost

2007

2008

2009

2010
9 

5. Research Methodology

This research study was undertaken in an effort to identify the most effective way to reduce plug 

load energy use, using three primary approaches. The first was an automated energy 

management system which turns off equipment when a pod of cubicles is unoccupied for a 

certain period of time. The second method involved behavioral change using information 

feedback and selected messaging. The third involved behavioral change using an occupant 

competition to reduce energy relative to their coworkers. The data analysis calculates the 

average amount of energy saved. 

The research method included establishing a baseline, testing experimental applications, and a 

returning to baseline to see if behaviors “stick” when messaging is no longer deployed. Each 

condition was applied for four weeks. The study began in early 2011 and was conducted with 

126 occupants on four different floors of the building. Occupants sit in clusters of six to eight 

people, called pods. Energy usage data were collected on a pod basis. 

Plug loads were monitored with a submetering and control system. This device monitors 

occupancy and electricity use and is capable of shutting off non-critical equipment after an area 

has been unoccupied for a preset amount of time. Up to four separate circuits can be attached to 

the system, with four output zones (A, B, C and D). Two of the output zones (usually C and D) 

remain energized in order to power critical equipment, such as computer central processing units 

(CPUs) and clocks. The other two output zones (A and B) are switched off based on whether the 

entire pod is unoccupied for a pre-set amount of time, which in this case was 15 minutes. This is 

accomplished by mounting a passive infrared (PIR) motion sensor to the underside of the work 

surface of each workstation. The submetering system reports the energy usage data to an online 

management system and dashboard. 

This provides a technology platform that was used in this study to monitor plug load usage. The 

occupancy information was stored and used for space utilization reporting and the actual 

electrical power consumption, including time of use, of each of the output zones was collected

and used in this study. Energy use data were available to study participants online via a secure 

website. 

5.1 Inventory Analysis

An inventory of all plug load equipment included in this analysis was taken at the beginning and 

end of the study. A walk-through of the pods was conducted in January and July, preceding and 

following the experimental phase. Detailed data regarding the inventory can be found in Section 

7.1.1 Inventory and ASHRAE Schedules. 
10

5.2 Baseline Periods

The first baseline period ran for four weeks at the beginning of the study, during February 2011. 

In this period no messaging, education or outreach was performed and no automated controls 

were deployed. 

A second baseline period ran for four weeks following the experimental phase, during Jun 2011. 

The goal of this second baseline period was to evaluate the extent to which behavioral changes 

continue when messages are no longer delivered to occupants. Therefore, no messaging was 

conducted during this baseline period; however, occupants could use the online dashboard by 

visiting the Web page. Also during this time period, no automated controls were deployed. 

5.3 Experimental Phase

Three experimental phases were conducted for four weeks each, during March-May 2011. The 

goal of these phases was to quantify how much energy was saved due to different energy 

conservation strategies. 

5.3.1 Control System

In the first experimental phase, during March 2011, the control system was enabled and 

controlled the non-critical circuits based on pod (group of cubicles) occupancy. When the entire 

pod was unoccupied for a period of 15 minutes, all non-critical circuits serving that pod were

turned off. Occupants were made aware of this control and were informed to plug the 

appropriate equipment into specific outlets. Also, facilities staff surveyed the test group and 

informed participants on how to comply. However, it was observed that some occupants 

plugged all of their equipment into the critical, non-interruptible circuits so that none of their 

equipment would be turned off by the control system. This could have been a result of issues 

with power outages during the installation of the submetering and control system. This action 

reduced the energy savings, but is important to incorporate in the findings as a negative occupant 

reaction.

5.3.2 Letters

In the second experimental phase, during April 2011, occupants were given information on the 

energy use associated with a variety of plug load devices as well as conservation tips. 

Information was conveyed via four, 1-page, email letters–one sent each week. All letters 

included background information about the study and provided occupants with contact 

information if they had questions, concerns or problems. The intention of this phase was to 

passively suggest energy conservation measures through occupant awareness, leaving the 

decision to take action up to the occupants. However, there was no way of tracking how many 

occupants read the letters and were actively participating. All letters from this experimental 

phase are included in Appendix A. 

Letter #1: The first letter notified occupants about the study and informed them that they 

would be receiving additional letters with facts about energy and conservation tips and 
11

plug load energy awareness. The letter included the definition of plug loads and the 

average percent contribution of plug loads to a building’s total electrical use. It listed the 

five pieces of workstation equipment with the highest energy consumption in the EPA 

Region 8 Headquarters Building (desktop computers, desktop printers, laptop computers, 

under-cabinet task lighting, and computer monitors) and provided an estimate of 629 

pounds of CO2 equivalent per person annually associated with plug load electrical 

consumption in the EPA Region 8 Headquarters Building. 

Letter #2: The second letter focused on education regarding computer energy settings. It 

provided facts about computer energy use and suggested an energy conservation idea 

whereby occupants activate power management settings on computers and monitors. It 

provided guidance on how to enable power management settings. 

Letter #3: Printers and their associated energy use were the focus of the third letter. Facts 

were provided regarding printer energy use and an energy conservation idea was 

suggested. The letter stated that removing all personal printers and using the networked 

printer instead could reduce energy consumption and loads during inactive periods, 

reduce additional waste streams, and improve office air quality. 

Letter #4: The focus of the fourth letter was energy and occupancy and it conveyed the 

message that plug loads can consume significant energy even when spaces are 

unoccupied, and some equipment draws electricity even when it is turned off. Several 

energy conservation ideas were presented, including: 

• Shutting down equipment, unplugging devices, and turning off power strips when 

leaving the office.

• Using compact fluorescent (CFL) or LED lighting in task lamps.

• Replacing office equipment with ENERGY STAR models. 

5.3.3 Competition

The final phase, during May 2011, was a competition. All occupants were provided directions, 

via email, on how to access an online digital dashboard to determine how much plug load-related 

electricity their pod was consuming, and the associated greenhouse gas (GHG) emissions, 

relative to other pods in the experiment. 

The data were presented for each pod and normalized for pod occupancy. It was hoped that by 

enabling occupants to compare their pod electricity use with other pods they would be able to 

understand how their energy use ranked comparatively, and they would be motivated to reduce 

their energy use in an attempt to outperform their peers in terms of energy efficiency. Data, such 

as that in Figure 5, below, was accessible to all occupants in the study. 
12

Figure 5: Plug Load Accountability by Pod

The competition prompts also provided key questions for occupants to consider in regards to 

their plug loads: 

• Is your plug load carbon dioxide equivalent (CO2e) accountability higher than others, 

and why? 

• Why is the plug load CO2e accountability so high on the weekends? 

• What can you do to reduce your plug load CO2e accountability?

Pod E3 was an outlier for all stages of this research. Upon further inspection it was observed 

that a network multifunction copy/printer was plugged into the pod circuit. This significantly 

altered the energy results. However, it is shown that the copy/printer did go into standby mode 

over the weekends. Pod E0 was disconnected or vacant throughout the study 
13

6. Results

The following results summarize the finding of the baseline and experimental phases that were 

conducted from February through July, 2011. 

6.1 Inventory and ASHRAE Schedules

An inventory of plug load equipment for all participants included in this analysis was taken at the 

beginning and end of the study. A walk-through of the pods was conducted in January and July, 

preceding and following the experimental phase. Equipment wattages were estimated, because 

interaction with the occupants was restricted, and diversity factors were applied based on 

ASHRAE publications. Diversity factors take into account the fact that all equipment that is 

plugged into the outlets are not always in use at the same time, and provide a more accurate 

representation of actual operating loads. Average cubicle sizes were measured to be 80 feet 

squared (ft2

) with an average total connected equipment load of 145 Watts (W) without diversity 

and 71 W with diversity. Average cubicle equipment power densities were calculated to be 0.9 

Watts/ft2 with diversity factors. The ASHRAE equipment schedule was used to approximate the 

estimated annual energy consumption and average usage profiles. Table 2 shows the equipment 

inventory, assumed wattages, and diversity factors. Table 3 shows the ASHRAE equipment 

schedules for weekdays, Saturdays, and Sundays.

Only slight differences were observed between the equipment inventories taken prior to the 

research and after the research. Table 2 shows the comparison. A reduction in the number of 

desktop computers and increase in number of laptops reflects the EPA policy to gradually 

replace desktop computers with laptops and occupant requests for laptops. Numbers of desktop 

printers, task lights, desktop speakers, and radios also decreased slightly. However, it is also 

shown that there are increases in the number of other workstation plug load equipment. 

Equipment that was shown to increase included desktop fans, heavy duty calculators, phone 

chargers, and label makers. These shifts in the equipment inventory may be due to several 

factors. One possibility is that occupants were influenced by the research and removed some of 

their desktop equipment such as printers, task lights, speakers, and radios. Another possibility is 

that shifts in occupation such as changes in desk location, personnel shifts, new hires, retirees, or 

other changes may have affected these inventories. The shifts in occupation are unquantifiable 

and have been stated as assumed to remain constant in this study. Overall, there were minor 

shifts observed when comparing the inventories from before and after the research but not 

significant enough to show a distinct influence on behavioral change. 
14

Table 2: Inventory of Plug Load Equipment, Assumed Wattage, and Diversity Factors for Baseline Period

 

Pre-Experiment 

Count Totals

Post-Experiment 

Count Totals

Assumed 

Wattage 

Diversity 

Factor References

Desktop 

Computer 35 21 85 75%

May 2011, 

ASHRAE Journal, 

Plug Load Design 

Factors, Wilkins, 

C.K., Hosni, M.H.

Laptop 

Computer 92 95 40 75%

May 2011, 

ASHRAE Journal, 

Plug Load Design 

Factors, Wilkins, 

C.K., Hosni, M.H.

LCD 

Monitor 130 127 34 60%

May 2011, 

ASHRAE Journal, 

Plug Load Design 

Factors, Wilkins, 

C.K., Hosni, M.H.

Undercabinet 

Task Light

266 266 31 5%

ASHRAE 

Fundamentals Ch. 

30

Desktop 

Printer 8 4 80 33% ASHRAE Research 

Project 1482-RP

Fax/ 

Scanner 1 1 20 33% ASHRAE Research 

Project 1482-RP

Task Light 94 92 13 33% 

Desktop 

Fan 5 10 15 33% 

Desktop 

Speakers 62 53 20 33% 

Radio 4 2 5 33% 

Radio with 

Lit Clock 14 11 10 100% 

Heavy Duty 

Calculator 11 20 10 33% 

Phone 

Charger 7 10 10 50% 

External 

Hard Drive 2 2 15 100% 

Electric 

Stapler 1 1 5 10% 

Wireless 

Headset 

Charger

5 5 10 50% 

Label 

Maker 1 2 10 33% 
15

Table 3: ASHRAE Equipment Schedule from Standard 90.1

ASHRAE Equipment Schedule 

Hour of 

Day 

% Usage 

Weekday 

% Usage 

Saturday

% Usage 

Sunday 

0 5 5 5 

1 5 5 5 

2 5 5 5 

3 5 5 5 

4 5 5 5 

5 10 5 5 

6 10 10 5 

7 30 10 5 

8 90 30 5 

9 90 30 5 

10 90 30 5 

11 90 30 5 

12 80 15 5 

13 90 15 5 

14 90 15 5 

15 90 15 5 

16 90 15 5 

17 50 5 5 

18 30 5 5 

19 30 5 5 

20 20 5 5 

21 20 5 5 

22 10 5 5 

23 5 5 5 

Total 1040 280 120

Average 

% Usage

43% 12% 5%

6.2 Data Conditioning and Development of the Baseline

The baseline period was developed from submetering the test group in a “business as usual” 

situation. Data were collected without any interaction with the occupants or any attempt to 

control circuits. Figure 6 shows an example of the hourly energy consumption and occupancy 

trends over a period of a several weeks. 
16

Figure 6: Sample Data Showing Energy and Occupancy Trends

Figure 6 was selected to show an example of both the energy and occupancy trends. In addition, 

some key issues with data collection are illustrated by this graph and are important in discussing

how these issues are handled in this analysis. 

First, it is noted that between February 3rd and February 8th, submetering equipment 

communications were not working properly, so no data were collected during those periods. 

This issue occurred on a couple of occasions. For the purposes of this analysis, these periods 

were omitted so that the data were not skewed, and replaced with average performance from 

similar days of the week and hours of the day during the same experimental method test period.

Second, it is noted that February 21st was a federal holiday, Presidents’ Day, and therefore 

employees were not in the office as they would be on a typical Monday. Therefore, holidays 

were also omitted from the final data set and replaced with average performance from similar 

days of the week and hours of the day during the same experimental method test period. 

Third, occupancy data could only be collected for the control system phase due to limitations in 

the submetering system. The reason for this limitation was due to the way that the control 

capability was configured and linked to the occupancy. For the purposes of this research, it was 

required to turn off the control mode, which is not the normal intention for this submetering and 

control system. The only way to disable the control mode was to disable the occupancy 

modules, which significantly limited the occupancy data desired for this research. As a result, 

average occupancy profiles were assembled for a typical week and applied to all experimental 

methods. 

The final data set for each experimental method represented four full weeks (Monday-Sunday) of 

hourly data for typical operating conditions, omitting periods of lost data and schedule 

discrepancies such as holidays. This final data set was used for all analysis and the average 

Communication downtime: Loss of dataHolidays create 

inconsistencies 
17

occupancy profile was applied to each experimental method. Figure 7 shows the average energy 

and occupancy profiles for the baseline period for a typical week. 

Figure 7: Baseline Period Average Energy and Occupancy Profiles

As expected, the energy and occupancy follow a similar trend. It is noted that some of the 

occupancy sensors were continually reading in active mode resulting in no times of 0% 

occupancy. It was observed that some of the occupants reacted negatively to the sensors and 

disabled, repositioned, or placed obstructions over them to give faulty readings continuously. 

During the baseline period, relatively high energy consumption was observed during unoccupied 

periods. 

The results of the baseline showed a total consumption over the four week period for the 126 

person test group of 2,090 kWh, which is 16.6 kWh/person. Normalized by day, the baseline 

showed a consumption of 0.6 kWh/person/day. When this number is extrapolated to all 775 

occupants in the building it shows an annual consumption of 167,576 kWh/yr. This 

extrapolation shows that workstation plug loads only account for 4.2% of the total building 

electricity. This could be an indication that other plug load equipment accounts for a larger 

portion of the plug load consumption, or that other energy use systems in the building 

significantly outweigh the plug loads. 

6.2.1 Submetered and ASHRAE Comparison

The baseline period was compared to the typical plug load assessment using estimated power 

ratings for equipment and ASHRAE-defined diversity factors and equipment schedules for 

modeling purposes. Figure 8 shows the comparison of the baseline period to the projected 
18

ASHRAE energy and occupancy profiles, derived from the inventory of equipment and typical 

modeling assumptions. 

Figure 8: Comparison of Baseline and ASHRAE Average Energy Profiles

It is shown that the energy profile from the baseline period tracks very closely, within 10% of the 

projected energy profile using the equipment inventory data with ASHRAE diversity factors and 

equipment schedules. However, it is noted that the profile for a typical Friday significantly 

differs. This indicates the possibility of alternative work schedules or telecommuting, which 

would reduce the energy consumption of a typical Friday. Site staff indicated that at least 50% 

of the occupants participate in an alternative working schedule, which confirms the reduced 

occupancy levels on Fridays. Also, the baseline energy consumption during unoccupied periods, 

nights and weekends, is significantly higher than the projected energy use. This shows a good 

opportunity for energy savings during unoccupied times. 

Figure 9: Comparison of Average and ASHRAE Average Occupancy Profiles
19

Figure 9 shows that the time intervals of typical occupancy match well between the average and 

the projected ASHRAE schedule; however, the magnitude of percent occupancy varies 

significantly. This shows that the ASHRAE occupancy schedule is not a good representation of 

the actual time that occupants spend at their workstations for this building. Also, this indicates 

the high likelihood of alternative work schedules and telecommuting policies, which are intended 

to reduce the occupancy in buildings. Another possibility is that the occupants working in the 

test group could be subjected to high travel rates causing them to work remotely for a high 

percentage of the time. ASHRAE occupancy schedules suggest a projected 95% occupancy rate

in buildings, but this study and others indicate that occupancy levels at the workstations are 

significantly lower than the projected total building occupancy rate.26

6.3 Average Energy Profiles

 

This section compares the average energy profiles for each experimental method to the baseline 

and ASHRAE energy profiles. The following graphs clearly illustrate whether or not energy 

reductions were achieved and when those reductions occurred (occupied versus unoccupied). 

Figure 10: Average Energy Profile Comparison for Control System

The control system showed significant energy savings during the occupied periods. This is as 

expected considering the low percent occupancy illustrated in the occupancy profile. With a 

lower percent occupancy, the control system was able to turn off several circuits during the 

occupied period. Weekend unoccupied periods also showed a significant energy savings over 

the baseline. However, the control system had mixed results during week nights, which may be 

 

26Morrow, W, Rutledge, B. Maniccia, D., Rea, M. “High Performance Lighting Controls in Private Offices,” World 

Workplace, Chicago, IL, Oct. 1998. 

http://www.starfieldcorp.com/pdf/publications/Field%20Study%20of%20Lighting%20Controls.pdf. 
20

caused by after-hours staff (i.e., security staff walking between pods) triggering the occupancy 

sensors and causing circuits to cycle on and off. 

Figure 11: Average Energy Profile Comparison for Letters 

The experimental method of sending plug load energy awareness letters to the occupants showed 

almost negligible energy savings and tracked very closely to the baseline period. Only very 

slight energy savings were observed during the weekend unoccupied periods. 

Figure 12: Average Energy Profile Comparison for Competition
21

The experimental method of encouraging competition between occupants showed almost 

negligible energy savings during the occupied periods and tracked very closely to the baseline 

period. However, moderate energy savings were observed during the weekend unoccupied 

periods. 

Figure 13: Average Energy Profile Comparison for All Experimental Methods

Figure 13 compares the average energy profiles of all experimental methods to the baseline and 

ASHRAE energy profiles. The control system performed the best out of the three methods and 

showed significant energy savings during the occupied periods and during the weekend 

unoccupied periods. Between the two behavioral change methods, the competition performed 

the best, especially during the weekend unoccupied periods. The results indicate that the 

behavior of occupants was influenced and most effective at reducing weekend energy 

consumption. The letters experimental method showed little to no effect on energy consumption. 

Figure 14 shows the raw data for the four week periods and 126 person test group for the 

baseline and three experimental methods. Approximate savings can be seen, but also a large 

variability in data. This may be an indication that longer test periods were needed to obtain a 

larger more reliable data set.
22

Figure 14: Raw Data for Four Week Periods and 126 Person Test Groups

6.4 Statistical Results

In this section, the data are analyzed for statistical significance. Summary statistics and the 

results from hypothesis testing are reported for each experimental method. Confidence levels at 

95% are calculated and confidence intervals are given for the mean energy savings of the hourly 

difference from baseline. Terms used in this analysis are defined below.

Sample Size (N): The number of observations within a statistical sample. In this study, it is the 

number of hourly data points collected from the submetering system. 

Mean Difference (M): The sum of the differences between one data set and another, divided by 

the number of values. In this study, it refers to the average hourly savings from an experimental 

method compared to the baseline. 

Standard Error: The estimate of uncertainty around the mean. In this study, it represents the 

uncertainty of the mean difference, or “savings” for the hourly data. 

Standard Deviation (SD): The measure of variability, diversity, or dispersion from the average 

in a data set. In this study, it shows the large spread of energy savings within each data set. 

Confidence Level (95%): The boundary of reliability around a parameter. In this study, it 

represents the value that forms a reliability boundary around the mean difference.

Confidence Interval (C.I.): The estimate of a range used to indicate the reliability of a 

parameter. In this study, it is the range about the mean difference that has a 95% confidence 

level.
23

Null hypothesis: The default position that there is relationship or effect. In this study, it 

represents the assumption that an experimental method has no effect on energy savings. 

T-statistic (t-stat): The ratio of the mean difference to the standard error, used in hypothesis 

testing. In this study, the higher the t-stat value, the more evidence there is to reject the null 

hypothesis, indicating that energy savings are statistically significant. 

P-value: The probability of achieving a test statistic similar to the observation, assuming the null 

hypothesis. In this study, the lower the p-value, the stronger the evidence that energy savings are 

statistically significant. 

Sum of Savings: The sum of energy savings, in kWh, throughout the test period for the entire 

test group. In this case it represents the savings from 672 hours of data and 126 cubicles in the 

test group, for each experimental method. 

6.4.1 Totals

Table 4 summarizes the statistical results for the total test group and total test period. Data 

represent four weeks of hourly data collected for 126 cubicles for each experimental phase. All 

data are presented as a difference compared to the baseline period that is discussed above. 

Table 4: Summarized Statistical Results for the Total Experimental Period

Experimental 

Phase

Sample 

Size 

Mean 

Difference 

(Watt-hrs)

Std. 

Error

Std. 

Deviation 

Confidence 

Level 

(95%) t-stat

P(T<=t) 

two-tail

Sum of 

Savings for 

126 

Cubicles 

(kWh)

Control System 672 645.07 53.16 1378.18 +/- 104.39 12.13 9.03E-31 433.49

Letters 672 -7.56 37.33 967.73 +/- 73.30 -0.20 8.40E-01 -5.08

Competition 672 183.98 37.14 962.75 +/- 72.92 4.95 9.22E-07 123.63

A paired t-test was performed to determine if each experimental method was effective:

Control System

The mean energy reduction for the control system in Watt-hours (M=645.07, SD =1,378.18, N= 

672) was significantly greater than zero, t(671)=12.13, two-tail p = 9.03E-31, providing strong 

evidence that this experimental method is effective at reducing plug load energy consumption at 

the workstation. A 95% C.I. about mean energy reduction is (540.68 Wh, 749.46 Wh). This 

range indicates the observed hourly energy savings for the 126 participants, with a 95% 

confidence level. 

Letters for Behavioral Change

The mean energy reduction for the letters phase in Watt-hours (M=-7.56, SD =967.73, N= 672) 

was not greater than zero, t(671)=-0.20, two-tail p = 8.40E-01, providing evidence that this 

experimental method has little to no effect on reducing plug load energy consumption at the 
24

workstation. It is also noted that the standard error for this data set is greater than the mean 

savings, again showing evidence that this experimental method had little to no effect on reducing 

the energy consumption at the workstation. A 95% C.I. about mean energy reduction is (-80.86 

Wh, 65.74 Wh). This range indicates the observed hourly energy savings for the 126 

participants, with a 95% confidence level.

Competition for Behavioral Change 

The mean energy reduction for the competition phase in Watt-hours (M=183.98, SD =962.75, 

N= 672) was greater than zero, t(671)=4.95, two-tail p = 9.22E-07, providing strong evidence 

that this experimental method is effective at reducing plug load energy consumption at the 

workstation. A 95% C.I. about mean energy reduction is (111.06 Wh, 256.90 Wh). This range 

indicates the observed hourly energy savings for the 126 participants, with a 95% confidence 

level.

6.4.2 Occupied Period

Table 5 summarizes the statistical results for the total test group only during the occupied period. 

Data represent four weeks of hourly data, only between the times of 7 a.m. and 7 p.m., collected 

for 126 cubicles for each experimental phase. All data are presented as a difference compared to 

the baseline period that is discussed above. 

Table 5: Summarized Statistical Results for the Occupied Period

Experimental 

Phase

Sample 

Size 

Mean 

Difference 

(Watt-hrs)

Std. 

Error

Std. 

Deviation 

Confidence 

Level (95%) t-stat

P(T<=t) 

two-tail

Sum of 

Savings for 

126 

Cubicles 

(kWh)

Control 

System 260 821.28 96.53 1556.57 190.09 8.51

1.44E15 213.53

Letters 260 -51.76 60.45 974.72 119.04 -0.86

3.93E01 -13.46

Competition 260 135.32 57.27 923.40 112.77 2.36

1.89E02 35.18

A paired t-test was performed to determine if each experimental method was effective:

Control System

The mean energy reduction for the control system in Watt-hours (M=821.28, SD =1,556.57, N= 

260) was significantly greater than zero, t(259)=8.51, two-tail p = 1.44E-15, providing strong 

evidence that this experimental method is effective at reducing plug load energy consumption at 

the workstation during the occupied period. A 95% C.I. about mean energy reduction is (631.18 

Wh, 1,011.37 Wh). This range indicates the observed hourly energy savings for the 126 

participants, with a 95% confidence level.
25

Letters for Behavioral Change

The mean energy reduction for the letters phase in Watt-hours (M=-51.76, SD =974.72, N= 260) 

was not greater than zero, t(259)=-0.86, two-tail p = 3.93E-01, providing evidence that this 

experimental method has little to no effect on reducing plug load energy consumption at the 

workstation during the occupied period. It is also noted that the standard error for this data set is 

greater than the mean savings, again showing evidence that this experimental method had little to 

no effect on reducing the energy consumption at the workstation during the occupied period. A 

95% C.I. about mean energy reduction is (-170.80 Wh, 67.27 Wh). This range indicates the 

observed hourly energy savings for the 126 participants, with a 95% confidence level. 

Competition for Behavioral Change 

The mean energy reduction for the competition phase in Watt-hours (M=135.32, SD =923.40, 

N= 260) was greater than zero, t(259)=2.36, two-tail p = 1.89E-02, providing moderate evidence 

that this experimental method is effective at reducing plug load energy consumption at the 

workstation during the occupied phase. A 95% C.I. about mean energy reduction is (22.55 Wh, 

248.09 Wh). This range indicates the observed hourly energy savings for the 126 participants, 

with a 95% confidence level. 

6.4.3 Unoccupied Period

Table 6 summarizes the statistical results for the total test group only during the unoccupied 

period. Data represent four weeks of hourly data, only between the times of 7 p.m. and 7 a.m. 

and 24 hours on Saturday and Sunday, collected for 126 cubicles for each experimental phase. 

All data are presented as a difference compared to the baseline period that is discussed above. 

Table 6: Summarized Statistical Results for the Unoccupied Period

Experimental 

Phase

Sample 

Size 

Mean 

Difference 

(Watt-hrs)

Std. 

Error

Std. 

Deviation 

Confidence 

Level 

(95%) t-stat

P(T<=t) 

two-tail

Sum of 

Savings 

for 126 

Cubicles 

(kWh)

Control 

System 412 533.87

61.1

8 1241.80 120.26 8.73

6.64E17 219.95

Letters 412 20.34

47.4

6 963.43 93.30 0.43

6.69E01 8.38

Competition 412 214.69

48.6

1 986.64 95.55 4.42

1.28E05 88.45

A paired t-test was performed to determine if each experimental method was effective:
26

Control System

The mean energy reduction for the control system in Watt-hours (M=533.87, SD =1,241.80, N= 

412) was significantly greater than zero, t(411)=8.73, two-tail p=6.64E-17, providing strong 

evidence that this experimental method is effective at reducing plug load energy consumption at 

the workstation during the unoccupied period. A 95% C.I. about mean energy reduction is 

(413.61 Wh, 654.13 Wh). This range indicates the observed hourly energy savings for the 126 

participants, with a 95% confidence level.

Letters for Behavioral Change

The mean energy reduction for the letters phase in Watt-hours (M=20.34, SD=963.43, N= 412) 

was slightly greater than zero, t(411)=0.43, two-tail p=6.69E-01, providing evidence that this 

experimental method has little to no effect on reducing plug load energy consumption at the 

workstation during the unoccupied period. It is also noted that the standard error for this data set 

is greater than the mean savings, again showing evidence that this experimental method had little 

to no effect on reducing the energy consumption at the workstation during the unoccupied 

period. A 95% C.I. about mean energy reduction is (-72.97 Wh, 113.64 Wh). This range 

indicates the observed hourly energy savings for the 126 participants, with a 95% confidence 

level.

Competition for Behavioral Change 

The mean energy reduction for the competition phase in Watt-hours (M=214.69, SD=986.64, N= 

412) was significantly greater than zero, t(411)=4.42, two-tail p=1.28E-05, providing strong 

evidence that this experimental method is effective at reducing plug load energy consumption at 

the workstation during the unoccupied phase. A 95% C.I. about mean energy reduction is 

(119.13 Wh, 310.24 Wh). This range indicates the observed hourly energy savings for the 126 

participants, with a 95% confidence level.

6.5 Energy and Cost Savings

Energy savings for each test period are extrapolated to estimate the annual energy savings in 

kWh. These savings are shown below for the total test period and broken out by occupied and 

unoccupied periods for each experimental method. The estimated annual savings were used to 

estimate the annual cost savings and simple payback period derived from the utility data 

discussed earlier in this report. 

6.5.1 Energy Savings

The following graphs summarize the results for the total test group and total test period. Data 

represent the estimated annual energy savings extrapolated from four weeks of hourly data, and 

normalized into a per-person basis for each experimental phase. 
27

Figure 15: Estimated Total Annual Energy Savings per Person 

Figure 16: Estimated Total Annual Energy Savings per Person during Occupied and Unoccupied Periods

Figure 15 and Figure 16 show that the control system performs best, with an estimated 45 kWh 

per person annual energy savings. The competition phase had the second best results with 

approximately 13 kWh per person annual energy savings. The letters achieved little to no annual 

energy savings. Also, the figures show that the majority of savings occurred during unoccupied 

periods for all experimental methods. 

Figure 17 shows the percent energy savings when compared to the baseline. Figure 18 shows the 

energy savings extrapolated to all occupants in the building, approximately 775, compared to the 

whole building electricity consumption. The total energy savings potential from the control 

system was calculated to be 34,757 kWh/yr, which represents a 0.9% reduction in the whole 
28

building electricity consumption (4,034,397 kWh/yr) and a 21% reduction from the baseline. 

The total energy savings from the letters method was negligible. The total energy savings 

potential from the competition method was calculated to be 9,913 kWh/yr, representing a 0.3% 

reduction in the whole building electricity consumption and a 6% reduction from the baseline. 

Figure 17: Percent Energy Savings from Baseline

Figure 18: Whole Building Percent Energy Reduction Estimated for 775 Occupants 

Figure 19 shows the mean hourly load reduction per person. It is important to note that these 

graphs present the load reduction as if it is constant throughout the hour. This is not a realistic 

assumption because the load can vary significantly during a one-hour timeframe. Therefore, 
29

these load reduction graphs cannot be taken as a constant demand reduction resulting from the 

different experimental methods. 

Figure 19: Total Mean Hourly Load Reduction

Figure 19 shows that the control system achieved a significantly higher hourly load reduction 

than the letters or competition. This indicates that the control system achieved greater watt 

reductions than occupant actions taken to reduce their workstation plug loads during the 

behavioral change experimental periods. 

6.5.2 Cost Savings

Cost savings were estimated from the extrapolated estimated annual energy savings for all 775 

occupants in the building. The average blended utility rate from the past four years is 

approximately $0.10/kWh. CO2e emissions factor for Colorado was assumed to be 1920.4 

pounds per megawatt-hour (lb/MWh).

27 Table 7 summarizes the cost savings analysis for each 

experimental method. 

Table 7: Cost Savings Analysis for Each Experimental Method

Experimental 

Method 

Total Annual 

Energy Savings 

(Extrapolated 

for 775 People) 

(kWh/yr) 

Percent 

Energy 

Reduction 

from 

Baseline

Percent of Whole 

Building Electricity 

Reduction 

(Extrapolated for 775 

People) 

Total 

Annual 

Cost 

Savings 

($/yr)

Total 

CO2e 

Savings 

(tons) 

Control 

System 34,757 21% 0.9% $3,476 30 

Letters -407 0% 0.0% $-41 0

Competition 9,912 6% 0.3% $991 9

 

27 eGRID, EPA: http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html. Accessed July 26, 2010. 
30

The best case energy savings would most likely result from operating the control system and the 

competition phase simultaneously with significant promotion of occupant awareness. 

Alternatively, implementing a competition as a behavioral change mechanism without a control 

system may be the most cost effective. However, without the submetering system the savings 

could not be verified and normalized comparisons of occupant energy consumption would not be 

possible. 

6.6 Occupant Feedback

Occupant feedback was recorded throughout this research project. Some feedback occurred 

during the letters phase which is shown in section 6.6.1. 

During the competition phase, the online dashboard was monitored for how many people visited 

the website. The total number of visits was 374, which equates to an approximate average of 

three website visits per person. The majority of these visits occurred within a day immediately

following the notifications.

In addition, occupants were asked to fill out an exit survey at the end of the research, and provide 

feedback and comments about the actions taken to reduce energy and their perception of the 

different experimental methods. Results of the exit survey are summarized in section 6.6.2. 

6.6.1 Experimental Phase

Throughout the test period, occupants occasionally responded to the behavioral change methods, 

which provided some unique insight. During the letters phase, the facilities manager received 

two responses from occupants. Both comments were in response to Letter #2. Both of the 

occupant responses had somewhat negative connotations toward putting the computers into 

standby mode, because of the time to reboot. This shows a lack of awareness about the different 

modes of operation for the computer. In standby mode, the computer uses far less energy and 

maintains all active applications; there is no reboot time. Depending on security, login is 

typically required but the computer returns to active mode in a matter of seconds. In addition, 

these responses state that occupants do not have the administrative privileges to change the 

computer power settings. Therefore, to change the settings on the computer would require an IT 

policy change and probably support from upper management. However, there were no requests 

from occupants to support the change. The two occupant responses are shown below. 

• “We (or maybe just myself) don't have the authority under the user rights to make those 

adjustments that you request. Unfortunately, there are times when I don't want it to go to 

standby (and take minutes to boot back up) because I am busy with something else. I'd 

love to make the change to reduce power but I am unable to comply.” 

• “Since it appears that we are unable to change the times on the preset "power schemes", 

we essentially have to spend our work time restarting our computers and rebooting the 

programs we have been working on after 30 minutes of meetings, etc. Has there been a 

cost estimate done on the work time lost restarting/rebooting computers?”
31

6.6.2 Exit Survey

Occupants were asked to fill out exit surveys at the end of the research period, and provide 

feedback and comments about the actions taken to reduce energy and their perception of the 

different experimental methods. Completed exit surveys were received from only 16 occupants

(12.7% of the total occupants). The exit survey questionnaire can be found in Appendix C. The 

results of the exit survey are summarized below. 

• None of the respondents felt that this project was disruptive to their work at any point. 

• When asked to describe what actions occupants took to reduce energy use at their 

workstation, the most frequent response (six responses) was that no action was taken. The 

next most frequent response (five responses) was that the respondent ensured that his or 

her computer and other energy-consuming devices were shut-down in the evenings. Not 

one respondent mentioned switching plug loads from a critical (always on) circuit to a 

circuit that is switched off based on whether the entire pod is unoccupied for a pre-set 

amount of time. 

• The responses to the question of whether occupants reduced their energy consumption 

during the plug load experiment were: 

Table 8: Response Rate to the Question “Did You Reduce Your Energy Consumption During the Experiment?”

Response Number of Responses Percent of Responses

Unsure 7 44%

Yes 5 31%

No 4 25%

Respondents seemed confused regarding whether the data were normalized for weekends, 

holidays, vacations, and employees leaving or entering the pod. Not many respondents 

answered the second part of the question, which asked what method (information on 

energy use of devices or competition with colleagues) motivated them the most to save 

energy. One person said competition, one person said information regarding energy use 

devices, and two people stated that having access to energy usage data was motivating. 

• Participants were asked if they reduced their plug load energy usage during this 

experiment, would they continue to do so in the future. The majority of responses (10) 

were yes, only one respondent indicated no, and five indicated ‘N/A’. 

• When asked if participants visited the energy monitoring website and what kind of 

information they found valuable, 75% of respondents (12) said they did visit the website, 

but only 56% (9) found the information useful or interesting. 

• Occupants were asked to list the equipment they had plugged into the A or B outlets 

(non-critical and switched off based on occupancy) and in C or D (critical, always on 

outlets). The discrepancy in responses shows a need for occupant education. 
32

Table 9: Response Rate to the Question “Which Equipment Was Plugged Into Non-critical and Critical Outlets?”28

Non-critical (A or B) Outlets Critical (C or D) Outlets

Equipment Respondents Equipment Respondents

Computer 4 Computer 10

Light 6 Light 4

Other (phone, radio, coffee warmer) 2 Other (phone, radio, coffee warmer) 2

Nothing 1 Nothing 1

Did not respond 3 Did not respond 1

• Occupants were asked if they had any comments or suggestions for improving this study, 

which could result in greater energy savings. Most respondents (12 or 75%) said no or 

did not respond. These were the comments provided: 

o “Present the data on a cube by cube basis, rather than by pod.” 

o “Compare actual energy usage for different computer models and upgrade those 

that are older or less efficient. Computers are the only thing most people have 

plugged in in their offices.” 

o “Provide additional information on the energy use of devices. This may help users 

make conscious choices to reduce energy use. For instance, do computer 

accessories–like speakers or overhead workstation lights–use energy even when 

off? Or would it help to unplug computer monitors at night? Take better 

advantage of competitive aspects. Consider sending out messages indicating, by 

code, which pods or users reduced their energy use the most.” 

o “Consider the energy use of the large, networked printers.” 

6.7 Prolonged Energy Savings: Post-Experiment Baseline

A second baseline period ran for four weeks following the experimental phase. The goal of this 

second baseline period was to evaluate the extent to which behavioral changes continue with no 

interaction or communication with the occupants. Therefore, no messaging or control was 

conducted during this baseline period; however, occupants could use the online dashboard 

system by visiting the Web page. Energy savings compared to the pre-experiment baseline are 

summarized below. These results showed that prolonged savings were achieved during the postexperiment baseline period. Savings were observed during occupied periods and weekend 

periods. These results indicate that behavioral change did have an effect on the overall operating 

practices of the test group. These savings, extrapolated to the entire building (775 occupants) for 

the entire year are estimated to achieve 15,669 kWh/yr, representing a 0.4% reduction in whole 

building electricity consumption and a 9% reduction from baseline. However, data collection for 

a longer period of time is required to confirm that these prolonged savings are permanent. 

 

28 Some respondents indicated that they had multiple items plugged into either the A/B outlets of the C/D outlets. 
33

Figure 20: Raw Data for Four Week Pre- and Post-Experimental Baseline Periods

Figure 21: Average Energy Profiles for Pre- and Post-Experimental Baselines
34

7. Conclusion

This study found that the most effective method for reducing plug loads was through the control 

system, which turned off plug load devices after 15 minutes of no occupancy in a pod. The 

competition among pods was also effective at reducing plug load energy consumption, although 

less so than the control system. The letters sent to occupants educating them about plug load 

energy use and opportunities for conservation had negligible savings. Assuming that the 

submetering system was required to verify savings for each experimental method, none of the 

methods individually achieved a reasonable payback period. The best case scenario for energy 

savings would consist of a control system and occupant competition with significant promotion 

of occupant awareness. Alternatively, implementing a competition as a behavioral change 

mechanism without a control system may be the most cost effective. However, without the 

submetering system the savings could not be verified and normalized comparisons of occupant 

energy consumption would not be possible. 

There were several uncontrolled factors in this research that introduced uncertainties into the 

analyzed data. Some of the assumptions made to account for the uncontrolled factors include: 

• Employees occupying workstations remained constant during test periods 

• Working hours and travel schedules align with the average profile. Occupancy data were 

only available for the control system experimental phase. Therefore, an average profile 

was created and applied to all test periods. 

• Interaction with heating/cooling load was not considered 

• Thermal comfort is uniform for all locations 

• Seasonal changes have no effects on plug loads 

• Time constraints required experimental test periods of four weeks, which was assumed 

sufficient for this study. 

Lessons learned stemmed from all aspects of this project. 

• Site support and occupant participation and interest are critical to the outcome of 

behavioral change research. 

• Approval from the EPA Union required additional time and planning for all interactions 

with the occupants and required the research to comply with protocols that would ensure

that occupant anonymity could be maintained. Anonymity is typically required for field 

research and should be included in dashboard interfaces for displaying data. 

• Installation of the control and submetering system took longer and was more costly than 

expected. However, costs are expected to decrease significantly with scale and 

experience working in federal facilities. 

• The wired installation of the control system and communications were very cumbersome

and complex. Wireless communications and controls with “plug and play” installation 
35

are expected to have less complexity, quoted at lower costs, and are currently 

commercially available. 

• Cybersecurity created a hurdle for the dashboard and data storage of the submetering 

system. 

• Several generalizing assumptions are required to account for all of the uncontrolled 

factors in behavioral change research. 

• Occupant participation and willingness to take actions to reduce energy was met with 

some resistance 

• The control system had significantly higher energy and cost savings compared to 

behavioral change methods. However, it is expected that incentivizing behavioral change 

could significantly improve occupant participation and energy reductions. Previous 

studies have indicated that the level of occupant involvement usually correlates with the 

incentive.29

• Implementing behavioral change mechanisms without a control system would 

significantly improve the cost effectiveness. However, energy and cost savings could not 

be verified and normalized occupant energy comparisons could not be generated without 

a submetering system.

 Incentives can be in many forms including money, prizes, food, public 

recognition, etc. 

• Developing the appropriate plug load management process can have a significant 

influence on the success of energy reduction goals. This may include behavioral change 

mechanisms, controls systems, or other policies. Establishing a program champion, 

developing a business case, benchmarking, identifying occupant needs, equipment 

selection/replacement, controlling equipment schedules, institutionalizing reduction 

measures, and promoting occupant awareness can all be critical steps in the process.

30

• Higher energy savings from plug loads may be accomplished by expanding the research 

beyond just workstations. This study focuses on the workstation and did not include 

equipment typically found in shared spaces such as large multifunction printers, 

refrigerators, microwaves, water-coolers, and other equipment. 

 

• Prolonged energy savings were found to be significant during the occupied period and 

weekend periods. However, longer term monitoring is recommended to verify the 

estimated prolonged savings. 

Next steps for additional research may include: 

• Combining multiple methods to determine if even greater savings could be achieved

• Comparing the energy savings between non-incentivized and incentivized competition 

 

29 Birch, D., and Veroff, J. (1966). Motivation: A Study of Action. Belmont, CA: Brooks/Cole. 30 Lobato, C., Sheppy, M., Bracknet, L., and Torcellinin, P. MELs Management Selection Process and Lessons 

Learned. NREL/TP-5500-51708, July 2011. 
36

• Evaluating the results of institutionalizing reduction measures through information 

technology (IT) policy31

• Achieving occupancy-based control at the cubicle level rather than at the pod level

• Install and monitor a wireless monitoring and control system and compare to the current 

system.

 

31 Assessing and Reducing Plug and Process Loads in Office Buildings. NREL/BR-5500-51199, June 2011. 
37

8. Appendix

Appendix A: Letters to Occupants
38
39
40
41

Appendix B: Competition Notifications to Occupants

 
42
43

Appendix C: Exit Survey
44

Appendix D: Summary of Applicable Legislation

EPAct 200532

[§104] Federal agencies shall incorporate energy efficiency criteria consistent with ENERGY 

STAR and FEMP-designated products for all procurements involving energy-consuming 

products and services.

[§203] Renewable energy is not less than:

• 2.5% of total consumption during FY 2006, 

• 3% of total consumption during FY 2007 - 2009, 

• 5% of total consumption during FY 2010 - 2012, and 

• 7.5% of total consumption during FY 2013 and thereafter. 

Note: accounting of renewable energy can be doubled if on federal or Indian land and used at a 

federal facility.

EO 1342333

[§2(a)] Reduction of energy intensity by 3% annually through the end of fiscal year 2015, or 

30% by the end of fiscal year 2015, relative to a 2003 baseline. 

[§2(b)] Ensure 50% of the required renewable energy consumed by the agency in a fiscal year 

comes from renewable sources on agency property. 

[§2(h)] Enable the ENERGY STAR feature on agency computers and monitors. 

EISA 200734

[§431] Reduce building energy intensity 3% annually through 2015, or 30% total reduction by 

2015, relative to a 2003 baseline. 

[§432] Energy and water evaluations must be completed every 4 years for covered facilities. 

Facility energy managers are also responsible for commissioning equipment and establishing 

operations and maintenance (O&M) plans for measuring, verifying, and reporting energy and 

water savings.

[§434] Ensure major replacement, renovation, or expansion projects employ the most energyefficient designs, systems, equipment, and controls that are life-cycle cost effective.

[§524] Encourages agencies to minimize standby energy use in purchases of energy-using 

equipment. 

[§525] Requires procurement to focus on ENERGY STAR and Federal Energy Management 

Program (FEMP)-designated products. 

 

32 “Energy Policy Act of 2005,” Federal Energy Management Program: 

http://www1.eere.energy.gov/femp/regulations/epact2005.html. Accessed July 26, 2011. 33 “Executive Order 13423,” Federal Energy Management Program: 

http://www1.eere.energy.gov/femp/regulations/eo13423.html. Accessed July 26, 2011. 34 “Energy Independence & Security Act,” Federal Energy Management Program: 

http://www1.eere.energy.gov/femp/regulations/eisa.html. Accessed July 26, 2011.
45

[§527] Each federal agency must issue an annual report that describes the status of initiatives to 

improve energy efficiency, reduce energy costs, and reduce GHG emissions.

EO 1351435

[§2-20] Greenhouse Gas Management: baseline, accounting, and reduction target reporting 

“President Obama announced a Federal Government-wide target of a 28 percent reduction by 

2020 in direct GHG emissions, such as those from fuels and building energy use, and a target 13 

percent reduction by 2020 in indirect GHG emissions, such as those from employee commuting 

and landfill waste.” 

[§2(g)(iv)] Minimize consumption of energy, water, and materials through cost-effective, 

innovative strategies, such as highly reflective and vegetated roofs. 

[§2(g)(v)] Manage existing buildings to reduce consumption of energy, water, and materials. 

Identify alternatives to renovation to reduce existing assets’ deferred maintenance costs. 

[§2(i)(ii)] Enable power management, duplex printing, and other energy-efficient or 

environmentally preferable features on all eligible Department of Energy (DOE) electronic 

products. 

[§2(i)(iv)] Ensure procurement of ENERGY STAR and FEMP-designated electronic equipment.

Other Mandates36

[EPAct 1992 §152] Install in federal buildings owned by the United States all energy and water 

conservation measures with payback periods of less than 10 years. 

[EPA Mandatory Greenhouse Gas Reporting Rule] Facilities emitting more than 25,000 metric 

tons of CO2e per year must report their emissions annually.

 

35 “Executive Order 13514,” Federal Energy Management Program: 

http://www1.eere.energy.gov/femp/regulations/eo13514.html. Accessed July 26, 2011. 36 “Energy Management Requirements by Law and Regulation,” Federal Energy Management Program: 

http://www1.eere.energy.gov/femp/regulations/requirements_by_reg.html. Accessed July 26, 2011.