DOE Meeting Memorandum: Ex Parte Communications 

Date: May 4, 2015 

DOE Attendees: Ashley Armstrong, Lucy DeButts, EERE 

Outside attendees: Patrick Keal, Big Ass Solutions and Terry Paul and Janie Wise, Cassidy & 

Associates 

Meeting Contact: Janie Wise – jwise@cassidy.com / 202-585-2553 

Subject: Pending rulemaking on Ceiling Fans Energy Conservation Standards 

Summary: Big Ass Solutions is headquartered in Lexington, Kentucky, and designs and manufactures a 

variety of fan models for commercial, industrial and residential use. BAS met with DOE to discuss the 

ongoing rulemaking process for the residential ceiling fans energy conservation standards, docket number 

EERE-2012-BT-STD-0045. 

The discussion focused on occupancy sensors and how the energy consumption of standby mode 

operation would be accommodated in the forthcoming energy conservation standards. BAS and argued 

that ceiling fans without occupancy sensors, like overhead lights, are prone to being left on even when 

rooms are empty and they serve no utility. The energy wasted from lighting left on has been well 

documented. BAS noted that the newest update of ASHRAE 90.1 will give a 10% credit for automatic 

occupancy sensors installed on lighting, as these sensors will result in enhanced energy savings. BAS also 

noted that a recent Navigant study on lighting found that lights without occupancy sensors are on for an 

average of 40% of the time when there is no one in the room. That study is here: 

http://library.cee1.org/sites/default/files/library/11458/CEE_LightingMarketCharacterization.pdf and the 

key table from the study is pasted below. BAS also submits for consideration another study from the 

Rensselaer Polytechnic Institute’s Lighting Research Center and the U.S. EPA ENERGY STAR program 

analysis the energy and cost savings potential of occupancy sensors for commercial lighting systems. 

Notably it found that occupancy sensors help reduce energy consumption in commercial buildings in 

particular during peak demand times. That study is attached. 

Participants also discussed the ballpark of potential cost impacts of the future rulemaking on ceiling fan 

manufacturers. DOE noted that manufacturer impact analysis that includes formal cost projections will 

first appear in the notice of proposed rulemaking. Participants discussed the timing of the proposed rule 

and projected schedule for the final rule. Participants also discussed whether any Big Ass Solutions 

products would be covered under the forthcoming rulemaking for Commercial and Industrial Fans and 

Blowers. 

 

An analysis of the energy and cost savings potential of occupancy sensors for 


commercial lighting systems 

Authors: Bill VonNeida**, Dorene Maniccia*, Allan Tweed* 

*Lighting Research Center **U.S. Environmental Protection Agency 

School Of Architecture ENERGY STAR Buildings Program 

Rensselaer Polytechnic Institute 401 M. Street, SW 6202J 

Watervliet Lab, Room 2215 Washington, DC 20460 

Troy, NY 12180-3590 202-564-9725 

518-276-8716 

IES Paper #43 1 08/16/00 
 

An Analysis of the Energy and Cost Savings Potential 

of Occupancy Sensors for Commercial Lighting Systems 

Introduction 

Since their introduction more than 20 years ago, occupancy sensor controls for lighting systems have 

promised significant energy and dollar savings potential in a variety of commercial lighting applications. By 

automatically controlling lighting to turn lights off when spaces are unoccupied, occupancy sensors controls 

compliment connected load reductions accomplished by lamp and ballast retrofits, giving building owners and 

operators additional opportunities to improve energy savings without compromising lighting service to building 

occupants. With typical estimated energy savings potential in from 1⁄4 to more than 1⁄2 of lighting energy (Audin, 

1993, EPRI 1992), occupancy sensors have frequently been promoted as one of the most cost effective technologies 

available for retrofitting commercial lighting systems. Both national and many state new construction codes also 

recognize the contribution of occupancy sensors to meet the power density allowances for designing interior 

lighting systems. 

Despite widespread promotion of these benefits, occupancy sensors have relatively poor market 

development compared to other lighting technologies. In addition to confronting the typical market barriers facing 

all new lighting technologies (high first cost, no uniform performance standards or measurement methodology, 

difficulty in specifying and commissioning, interaction with other system components, long term persistence due to 

user interference, etc), occupancy sensors also suffer from the difficulty of definitively predicting and demonstrating 

savings. Unlike technologies that reduce connected load where savings can be readily measured, occupancy sensor 

performance is dependent on the user occupancy, lighting control patterns, sensor selection and commissioning. 

Consequently even within generic space uses categories, industry savings estimates range by a factor of two to three 

as shown in Table 1. 

These savings estimates have also been criticized as being overly optimistic, given that energy saved during 

utility off-peak hours is typically less valuable than energy saved during utility shoulder or peak periods. These 

IES Paper #43 2 08/16/00 
 

performance uncertainties make it difficult to predict the economic benefit, and create significant additional 

specification risks beyond those borne by demand limiting measures. 

Previous Work 

Little objective, independent and detailed research information is available on occupancy and lighting 

patterns. Single building case studies have also reported a range of savings in a variety of applications. Savings of 

10% to 19% have been reported for classrooms (Floyd, et al., 1995; Rundquist, 1996), and of 27% to 43% in private 

offices (Jennings et al., 1999; Maniccia et al., 1999; Seattle City Light, 1992). Richman, et al. (1996) reports 

potential energy savings of between 45% and 3% for private offices and between 86% and 73% for restrooms. 

The primary objective of the present study was to investigate lighting operation and workspace occupancy 

patterns across numerous commercial buildings to better quantify the performance estimates of occupancy sensors 

across typical space types. By examining how occupants occupy their spaces and manually control their lighting, 

and comparing these baselines to modeled occupancy sensor control scenarios, energy and dollars savings potentials 

are investigated. Note that the system economics of evaluating energy savings against lamp maintenance costs for 

these same control scenarios are evaluated in an associated paper, The Effects of Changes Occupancy Sensor 

Timeout Setting on Energy Savings, Lamp Cycling, and Maintenance Costs. 

Methodology 

Sixty organizations were chosen for study from active participants in the US. Environmental Protection 

Agency’s Green Lights Program. The study buildings were located in twenty-four states, and occupied by profit, 

not-for profit, service and manufacturing companies, healthcare organizations, primary and secondary education, 

and local, state, and federal government entities. The diversity of age, size, efficiency, ownership and occupancy 

types for these buildings was intended to represent a typical cross section of the country’s commercial building 

stock. 

IES Paper #43 3 08/16/00 
 

Rooms for study were identified by on-site facilities management staff as representative of space types 

within that building, both in their floorplan, occupancy, and lighting system . All spaces contained manual controls 

for the lighting systems, with a minimum connected lighting load of at least 150 watts. A two-week monitoring 

period between February and September 1997 was chosen to represent a typical lighting and occupancy schedule. 

Data for 180 rooms were originally collected; after eliminating records with inconsistent or incomplete data, the 

study database contained 158 rooms categorized by primary occupancy type into 42 restrooms, 37 private offices, 35 

classrooms, 33 conference rooms, and 11 break rooms. Rooms were surveyed for occupancy type, dimensions and 

lighting system specification. Occupancy and lighting operation data was collected using Wattstopper InteliTimer 

Pro® IT100 data loggers. The logger device recorded the time and state of the light and/or occupancy condition. 

Each time occupancy or the lighting condition changed, the logger documented the time of day and the change in 

condition. An algorithm was developed to convert the data recorded from the Watt Stopper InteliTimer Pro device 

into one-minute increments for the 14-day monitoring period. There were cases when the lights were turned on and 

off, but no occupant was detected in the space. This was considered a detection error, and was corrected by 

modifying the data set to switch the occupancy condition from unoccupied to occupied for those instances. This 

occurred for: 

•	 six of the break rooms with detection errors ranging between one and 181 events (0% to 1% of the 

total events) 

•	 17 of the classrooms with detection errors ranging between one and 2,677 events (0% to 13% of the 

total events) 

•	 16 of the conference rooms with detection errors ranging between one and 1,681 events (0% to 8% of 

the total events) 

•	 17 of the private offices with detection errors ranging between one and 5,686 events (0% to 28% of the 

total events) 

•	 seven of the restrooms with detection errors ranging between one and 275 events (0% to 1% of the 

total events). 

IES Paper #43	 4 08/16/00 
Descriptive statistics were calculated and cost analyses were performed for weekdays, weekends, and for the 

total 14-day monitoring period. Data also were analyzed by separating 24-hour periods into one 12-hour day shift 

(Day) and one 12-hour evening shift (Night). Day and night shifts were analyzed from 06:00 to 18:00 and 18:00 to 

06:00, respectively. Data presented for weekdays were averaged over the 10 weekdays, and for weekends were 

averaged over the four weekend days in the monitoring period. Data presented for the total period were averaged 

over the 14-day monitoring period. Baseline occupant switching and occupancy patterns were established using the 

collected data on occupancy and light usage. The baseline occupancy and light usage data were then used for 

modeling the effects of installing occupancy sensors with 5-, 10-, 15-, and 20-minute time delay periods. 

Statistical analyses also were conducted to investigate whether there were significant differences between 

the energy use for shift (day or night), time of week (weekday or weekend), and timeout setting, or any interactions 

between shift and timeout settings and time of week and timeout settings. These analyses were used to provide 

evidence of which differences were real and which occurred by chance. Within-subjects analyses of variance using 

repeated measures (ANOVR) were used for the analyes. One analysis compared day and night periods to the 

baseline and the four timeout settings. A second analysis compared the weekday and weekend data for a 24-hour 

period to the baseline and the four timeout settings. Follow-up tests were conducted using pairwise comparison ttests. 

For the energy calculations, the total load for each room was used to determine lighting energy use and 

waste. Lighting energy use was calculated by multiplying the total lighting load by the time that the lights were on 

and the room was occupied. Lighting energy waste was calculated by multiplying the total load by the time that the 

lights were on and the room was unoccupied. Total energy savings was determined by applying a flat $0.08/kWh 

rate to the modeled energy savings under each control scenario. 

IES Paper #43 5 08/16/00 
Findings – Baseline data 

Total energy savings potential 

Determining the basic energy savings potential across applications requires establishing a baseline of observed 

occupancy and lighting conditions. Lighting and occupancy use in any space will always fall into one of the 

following four conditions: 

1. Occupied with the lights on 

2. Occupied with the lights off 

3. Unoccupied with the lights on 

4. Unoccupied with the lights off 

Of the four conditions, the first three are of particular interest. Condition one is of interest for garnering 

information about how frequently occupants use these types of spaces with the lights on. Conditions two and three 

are of interest when considering lighting controls. If occupants frequently occupy a space with the lights off 

(condition two), then a manual lighting control device that allows occupants to turn lights off when needed should 

be provided. Condition three represents wasted lighting energy by having lights on when spaces are unoccupied. 

This condition is of primary importance when considering using automatic occupancy sensor control. Table 2 lists 

the average percentage of time each application was in each of the first three occupancy and lighting conditions. 

Table 2 illustrates that spaces were infrequently occupied, with the daily percentage of total occupied time 

with lights on and off never exceeding 24%. Also, occupants did not diligently turn lights off when they vacated 

spaces, with the lighting system in classrooms, conference rooms, and restrooms operating more often when the 

occupants were out of the room than in the room. This is intuitively understandable in common area applications 

(such as restrooms and conference rooms), where occupants do not feel that the lighting is “theirs” to control. The 

split, however, is still fairly even in private offices, indicating that even in personal spaces, occupants were not 

diligent about controlling their lighting use. The data shown for condition 2 indicates that occupants rarely occupied 

spaces with the lights off, indicating that for these spaces there may be a small potential benefit of installing manual 

IES Paper #43 6 08/16/00 
controls. Note that since the presence of daylight availability was not documented in this study, however, it is 

difficult to compare these results to other studies which have found higher savings potentials from installing manual 

controls (Maniccia et al., 1999). 

Time of day/week impacts on energy savings 

Determining the applicability of occupancy sensors as a control strategy suitable to obtain these savings 

requires an examination of when those savings present themselves. As an automatic control strategy, occupancy 

sensors work best in areas where occupancy is intermittent and unpredictable. Where the lighting is inadvertently 

left on overnight by cleaning, security, or occupants, a more cost effective control strategy may be an education 

campaign, or the installation of a simple timeclock. For these reasons, the savings estimates are examined over four 

defined periods - weekday days, weekday evenings, weekend days, and weekend evenings. 

As expected, Table 3 demonstrates that the majority of energy use (76-88%) occurs for all space types 

during the weekdays, with 55-85% of total energy use occurring during the day. Likewise, the majority of energy 

waste (between 70-87%) occurs during the weekdays, not on the weekends. For all space types except restrooms, 

the majority energy waste also occurs during the daytime (53-70%) rather than in the evenings. This indicates that 

occupants controlled their lighting poorly during the workday, but were more diligent about turning the lights off 

after hours and over weekends. This is particularly true for personal spaces such as private offices, where occupants 

feel a high degree of control over their lighting, and less true in common area spaces, such as classrooms and 

restrooms, where a high percentages of waste occurred over weekends and after hours. This indicates that timebased controls (timers, timeclocks) could eliminate a significant amount of energy waste in common areas by 

controlling runaway operation after hours and on weekends, however occupancy-based controls would be more 

effective given they save not only after hours but also at capturing savings during business hours. 

Coincidence of savings with peak demand 

Central to defining the economic benefits of occupancy sensors is understanding when the savings 

opportunities occur. Most commercial and industrial facilities pay a considerable portion (as high as 40%) of their 

IES Paper #43 7 08/16/00 
electric energy bill for the peak demand created by the electric loads. Lighting is the second largest contributor to 

summer peak demand in commercial facilities, and rivals heating as the largest contributor to a commercial 

building’s winter peak. As such, reductions in lighting demand can significantly impact the energy bill. 

Occupancy sensors have been criticized in their ability to reduce this peak demand, saving cheaper 

kilowatts in the utility’s off-peak billing hours rather than during the peak demand billing period. Although the 

value of this savings is highly dependent on how the utility billing rate is structured (time-of-use, annual peak, 

ratcheted, etc), it is useful to examine when the potential savings occur to understand how occupancy sensors may 

contribute to reducing a building’s peak demand, and reducing demand during peak utility periods. To evaluate this, 

Figures 1-5 illustrate the time of day profiles for when the spaces were lighted and occupied, and lighted and 

unoccupied for all weekdays.. 

Figures 1-5 indicate that the control’s largest contributions to potential savings are not coincident with a 

building’s peak occupancy. This is intuitive; when occupancy rises, occupancy-based savings opportunities 

diminishes. Although the majority of energy savings from sensors occur during weekdays, the sensors largest 

contribution to savings (with the exception of conference rooms and offices) is generally not coincident with a 

building’s peak load (10 a.m. to 4 p.m.) or with a utility’s peak billing periods (early afternoon hours). This would 

indicate that while sensors can reduce a buildings peak load, they may not be a reliable method of achieving of peak 

savings due to the diversity of savings profiles observed among these different space types. 

Findings – Occupancy Sensor Simulations 

Impact of time-out period on energy savings 

Most occupancy sensors are equipped with a variable time delay feature to adjust the time interval between 

the last detected motion and the switching off of the lamps. This allows the sensor to be customized to the 

application to reduce the chance of lamps switching off when a room is occupied but minor motions are not 

detected. Adjusting the time delay creates a tradeoff between saving energy and avoiding occupant complaints. 

Longer time delays reduce the incidence of occupant complaints. Shorter time delays increase energy savings 

IES Paper #43 8 08/16/00 
(particularly in rooms that are infrequently and briefly occupied), but also reduces lamp life from more frequent 

lamp cycling. Manufacturers report time delay setting ranging from several seconds to more than 30 minutes. 

To examine the impact of time delay on energy savings, control scenarios for 5-, 10-, 15-, and 20-minute 

time delays were modeled for each application. Statistical analyses were also conducted to investigate the impact of 

time of day, time of week, and timeout setting on energy use. Note that the impact of frequent switching on lamp 

and maintenance costs for these same control scenarios are evaluated in an associated paper, The Effects of Changes 

Occupancy Sensor Timeout Setting on Energy Savings, Lamp Cycling, and Maintenance Costs. 

Statistical Analysis Findings 

As discussed in the “Methodology” section, statistical analyses of the energy use data were conducted to 

investigate whether there were significant differences between the energy use for Shift (day or night), time of week 

(weekday or weekend), and timeout setting, or any interactions between shift and timeout settings and time of week 

and timeout settings. The energy use data for each Shift were compared to the baseline and the timeout settings for 

one analysis. A second analysis compared the weekday and weekend data for a 24-hour period to the baseline and 

the timeout settings. 

The statistical analyses addressed the following questions: 

• Is there a significant energy use difference between the shifts for the total monitoring period? 

• Is there a significant energy use difference between the baseline and each timeout setting? 

• Is there a significant energy use difference between weekdays and weekends?

 Shift verses timeout settings 

There were no significant interactions between the energy use for each shift and for the timeout settings for 

the break rooms or for the classrooms. For both applications, differences between the main effects (Shifts 1 and 2) 

were significant (p < 0.01 for the break rooms, and p < 0.001 for the classrooms). Differences between the timeout 

setting main effects were also significant for both of these applications (p < 0.01 for the break rooms, and p < 0.01 

IES Paper #43 9 08/16/00 
 

 

 

for the classrooms). Figures 6 through 9 illustrate the results of the main effects tests for the break rooms and 

classrooms, respectively. 

Significant interactions occurred between energy use for each shift and timeout settings for the conference 

rooms (p < 0.05), private offices (p < 0.001), and restrooms (p < 0.01). Follow-up tests illustrated that differences 

between the shifts at each timeout setting and the baseline condition were all significant (p < 0.001) for all three 

applications. Differences between the baseline condition and each timeout setting were all significant for Shift 1 for 

all three applications (p < 0.001). Differences between the baseline condition and each timeout setting also were 

significant for Shift 2 for all three applications (the conference rooms and private offices [p < 0.01] and restrooms 

[p <0.001]). Figures 10 through 12 illustrate the results of the follow-up tests with the 95% confidence intervals. 

Figures 6 through 12 illustrate that more energy is used during the day than at night, which would be 

expected of these types of applications. They also show that installing occupancy sensors decreases baseline energy 

use, and that energy use increases as the timeout setting increases because lights remain on for longer periods of 

time. 

 Time of week verses timeout settings 

There were significant interactions between the energy use for time of week and timeout settings for all five 

applications (p < 0.05 for the break rooms and p < 0.001 for the classrooms, conference rooms, private offices, and 

restrooms). Follow-up tests illustrated that differences between the energy use values for the weekdays and 

weekends at each timeout setting and the baseline were significant for all of the applications (p < 0.01 for the break 

rooms, and p < 0.001 for the classrooms, conference rooms, private offices, and restrooms). Differences between the 

energy use values for the baseline and each timeout setting for the weekdays also were significant for each 

application (p < 0.01 for the break rooms and classrooms, and p < 0.001 for the conference rooms, private offices, 

and restrooms). Differences between the energy use values for the baseline and each timeout setting for the 

weekends were not significant for the break rooms, classrooms, and conference rooms. However, these differences 

were significant for the private offices (p < 0.05) and restrooms (p < 0.001). These results are illustrated in Figures 

13 through 17. 

IES Paper #43 10 08/16/00 
 

The two main points that can be taken away from this analysis are: 

•	 the differences between the energy use for each timeout setting and the baseline for each application were 

all significant for each shift. This indicates that energy savings can be achieved during the day and at night 

using occupancy sensors. 

•	 the differences between the energy use for each timeout setting and the baseline for each application were 

all significant for the weekdays, but varied by application for the weekends. This indicates that energy 

savings can be achieved during the week for all applications, and during the weekend for private offices 

and restrooms. 

Effects of Time Delay on Energy and Cost Savings 

As demonstrated in Table 4, the savings estimates were considerable across all space types (ranging from 

17-60%), and consistent with the ranges of industry estimates provided in Table 1. Table 4 illustrates that both 

application and time delay selection significantly impacts the quantity of available savings. For this data set, 

restrooms showed the highest overall savings, followed by classrooms, conference rooms, and private offices. Break 

rooms showed the lowest overall savings. The range of savings between the shortest and longest time out setting 

varied with application as well because of the occupancy pattern differences among the applications. Classrooms 

had the smallest savings difference between the 5- and 20-minute time out settings (6%) and restrooms had the 

largest difference (13%). 

Figures 18-22 illustrate the load profiles for the baseline energy use and modeled energy use under 5 and 

20 minute simulated delay conditions. These figures graphically depict the differences found in the energy savings 

captured between the longest and shortest time delays. These figures also confirm the observations found from the 

baseline occupancy and lighting conditions depicted in Figures 1-5; although the majority of energy savings from 

sensors occur during weekdays, the sensors largest contribution to savings (with the exception of conference rooms 

and offices) is generally not coincident with a building’s peak load or with a utility’s peak billing periods. This 

suggests that while sensors can reduce a buildings peak load, sensors may not be a reliable method of achieving 

peak savings in buildings with a diversity of space types. 

IES Paper #43	 11 08/16/00 
 

Conclusions/Recommendations for Future Work 

People do not occupy spaces for a large percentage of time, and are not diligent about controlling the 

lighting in their spaces both during the workday, and after hours and weekends. This applies to both public spaces as 

well as personal spaces. The majority of this energy waste occurs during the weekdays, not during the weeknights 

or over the weekends. This pattern of energy waste is particularly suited to control by occupancy sensors, which not 

only prevent runaway operation after typical business hours, but also capture savings during the business day. 

Although the majority of observed savings opportunities occurred during the weekday, the peak savings 

contributions from occupancy sensors for several space types did not fall within the typical peak utility billing 

periods (early afternoon) or peak commercial building demand periods (10 a.m. to 4 p.m.). This suggests that while 

sensors may help to save expensive kilowatt-hours, they would have a variable effect at reducing a building’s peak 

demand, given their variable performance in when they provide high levels of savings among the various space 

types. This would be a useful topic of additional research, where assigning specific kilowatt-hour rates to each 

kilowatt-hour saved would yield more accurate indication of the economic benefits of installing sensors within these 

various space types. 

Finally, modeling control scenarios with 5- to 20-minute time delay periods indicated savings potentials 

that were within the ranges suggested by the industry estimates. The time delay settings used for these analyses 

showed that energy savings can range from between 6% and 13% depending on the application and on which time 

out setting is used. In addition, the highest savings were obtained in the restroom application (47% to 60%), and the 

lowest in the break rooms (17% to 29%). Thus, the time delay selection can greatly impact energy savings. 

Although these savings are significant they do not consider the increased maintenance lamp and labor replacement 

costs that could result due to more frequent lamp switching. This is evaluated in a related paper entitled The Effects 

of Changes Occupancy Sensor Timeout Setting on Energy Savings, Lamp Cycling, and Maintenance Costs. 

IES Paper #43 12 08/16/00 
References 

Audin, L., 1999. Occupancy Sensors: Promises and Pitfalls. Esource Tech Update, TU-93-8, Boulder CO.. 

California Energy Commission (CEC), 1992. Occupant Sensors. Advanced Lighting Guidelines, P400-93-014. 

California Energy Commission, Sacramento, CA. 

Electric Power Research Institute (EPRI), 1992. Occupancy Sensors: Positive On/Off Lighting Control, BR-100323. 

Electric Power Research Institute, Palo Alto, CA. 

Floyd, David B., Danny S. Parker, Janet E. R. McIlvaine, and John R. Sherwin. 1995. Energy efficiency technology 

demonstration project for Florida educational facilities: Occupancy sensors, FSEC-CR-867-95. Cocoa 

FL: Florida Solar Energy Center, Building Design Assistance Center. Accessed February 23, 2000 at 

http//:www.fsec.ucf.edu/~bdac/pubs/CR867/Cr-867.htm. 

Maniccia, Dorene, Burr Rutledge, Mark S. Rea, and Wayne Morrow. 1999. Occupant use of manual lighting 

controls in private offices. Journal of the Illuminating Engineering Society 28(2):42-56. 

R.A. Rundquist Associates. 1996. 	Lighting controls: Patterns for design, TR-107230. Palo Alto, CA: Electric Power 

Research Institute. 

Richman, E. E., A. L. Dittmer, and J. M. Keller. 1996. Field analysis of occupancy sensor operation: Parameters 

affecting lighting energy savings. Journal of the Illuminating Engineering Society 25(1):83-92. 

Seattle City Light. Energy Management Services Division. 1992. Case study on occupant sensors as an office 

lighting control strategy. Seattle WA: Seattle City Light 

Jennings, Judith D., Francis M. Rubinstein, Dennis DiBartolomeo, Steven L. Blanc. 1999. Comparison of control 

options in private offices in an advanced lighting controls testbed. Proceedings of the Illuminating 

Engineering Society, Paper #44. 275 – 298. 

The Watt Stopper. [1998] Applications & savings. Automatic Lighting, HVAC, and Office Power Controls. Santa 

Clara, CA: The Watt Stopper 

IES Paper #43	 13 08/16/00 
 

 

 

 

 

 

 

Table 1. Industry estimates of potential energy savings for occupancy sensors (in %) 

Space type CEC Esource EPRI Novitas Watt 

Stopper 

Private office 25-50 13-50 30 40-55 15-70 

Open office 20-25 20-28 15 30-35 5-25 

Classroom - 40-46 20-35 30-40 10-75 

Conference 45-65 22-65 35 45-65 20-65 

Restroom 30-75 30-90 40 45-65 30-75 

Warehouses 50-75 - 55 70-90 50-75 

Storage 45-65 45-80 - - 45-65 

Table 2. Average percentage of time each application was occupied with lights on and off, and unoccupied with 

lights on. 

Occupied with lights on Occupied with lights off Unoccupied with lights on 

Day Night Total Day Night Total Day Night Total 

Break room 36% 7% 21% 4% 2% 3% 17% 11% 14% 

Classroom 22% 3% 13% 4% 2% 3% 20% 17% 19% 

Conference 16% 2% 9% 4% 1% 2% 15% 7% 11% 

Private office 32% 2% 17% 1% 1% 1% 23% 9% 16% 

Restroom 32% 7% 19% 1% 1% 1% 44% 51% 48% 

IES Paper #43 14 08/16/00 
 

 

 

 

 

 

 

 

 

 

Table 3. Percentage of energy use and waste for weekdays, weekends, and for the total monitoring period for each 

application. 

Energy Use (%) 

Weekdays Weekends Total monitoring period 

Day Night Total Day Night Total Day Night Total 

Break room 69% 19% 88% 8% 5% 12% 77% 23% 100% 

Classroom 55% 26% 82% 11% 7% 18% 66% 34% 100% 

Conference 65% 19% 83% 11% 6% 17% 76% 24% 100% 

Private office 74% 12% 86% 11% 3% 14% 85% 15% 100% 

Restroom 43% 33% 76% 12% 12% 24% 55% 45% 100% 

Energy Waste (%) 

Weekdays Weekends Total monitoring period 

Day Night Total Day Night Total Day Night Total 

Break room 50% 29% 79% 12% 8% 21% 63% 37% 100% 

Classroom 40% 36% 76% 13% 11% 24% 53% 47% 100% 

Conference 55% 24% 80% 12% 9% 20% 67% 33% 100% 

Private office 67% 21% 87% 8% 5% 13% 75% 25% 100% 

Restroom 29% 41% 70% 14% 16% 30% 42% 58% 100% 

IES Paper #43 15 08/16/00 
Table 4. The effects of time delay on energy and cost savings for the total monitoring period. Total daily energy use (kWh) Energy saved compared to baseline Annual energy cost ($) Annual energy cost reduction ($) Total daily energy use (kWh) Energy saved compared to baseline Annual energy cost ($) Annual energy cost reduction ($) Total daily energy use (kWh)

Energy saved compared to

baseline 

Annual energy cost ($) 

Annual energy cost reduction 

($) 

Break 

Room Day Night Total 

Baseline 5.53 --­ 161.48 - 1.67 --­ 48.76 - 7.20 --­ 210.24 -

5-minute 4.33 22% 126.44 35.04 0.78 53% 22.78 25.99 5.11 29% 149.21 61.03 

10-minute 4.64 16% 135.49 25.99 0.89 47% 25.99 22.78 5.53 23% 161.48 48.76 

15-minute 4.83 13% 141.04 20.44 0.97 42% 28.32 20.44 5.80 19% 169.36 40.88 

20-minute 4.97 10% 145.12 16.35 1.03 38% 30.08 18.69 6.00 17% 175.20 35.04 

Classroom Day Night Total 

Baseline 11.37 --­ 332.00 - 5.84 --­ 170.53 - 17.21 --­ 502.53 -

5-minute 6.21 45% 181.33 150.67 0.94 84% 27.45 143.08 7.15 58% 208.78 293.75 

10-minute 6.60 42% 192.72 139.28 1.03 82% 30.08 140.45 7.63 56% 222.80 279.74 

15-minute 6.88 39% 200.90 131.11 1.11 81% 32.41 138.12 7.99 54% 233.31 269.22 

20-minute 7.12 37% 207.90 124.10 1.18 80% 34.46 136.07 8.30 52% 242.36 260.17 

Conference 

room Day Night Total 

Baseline 3.09 --­ 90.23 - 0.99 --­ 28.91 - 4.08 --­ 119.14 -

5-minute 1.76 43% 51.39 38.84 0.27 73% 7.88 21.02 2.03 50% 59.28 59.86 

10-minute 1.91 38% 55.77 34.46 0.31 69% 9.05 19.86 2.22 46% 64.82 54.31 

15-minute 2.02 35% 58.98 31.24 0.35 65% 10.22 18.69 2.37 42% 69.20 49.93 

20-minute 2.11 32% 61.61 28.62 0.38 62% 11.10 17.81 2.49 39% 72.71 46.43 

Private 

office Day Night Total 

Baseline 2.83 --­ 82.64 - 0.51 --­ 14.89 - 3.34 --­ 97.53 -

5-minute 1.92 32% 56.06 26.57 0.14 73% 4.09 10.80 2.06 38% 60.15 37.38 

10-minute 2.05 28% 59.86 22.78 0.16 69% 4.67 10.22 2.21 34% 64.53 33.00 

15-minute 2.14 24% 62.49 20.15 0.17 67% 4.96 9.93 2.31 31% 67.45 30.08 

20-minute 2.21 22% 64.53 18.10 0.18 65% 5.26 9.64 2.39 28% 69.79 27.74 

Restroom Day Night Total 

Baseline 3.10 --­ 90.52 - 2.50 --­ 73.00 - 5.60 --­ 163.52 -

5-minute 1.83 41% 53.44 37.08 0.42 83% 12.26 60.74 2.25 60% 65.70 97.82 

10-minute 2.04 34% 59.57 30.95 0.52 79% 15.18 57.82 2.56 54% 74.75 88.77 

15-minute 2.19 29% 63.95 26.57 0.60 76% 17.52 55.48 2.79 50% 81.47 82.05 

20-minute 2.29 26% 66.87 23.65 0.68 73% 19.86 53.14 2.97 47% 86.72 76.80 

IES Paper #43 16 08/16/00 
Figures 

Total time 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Weekday, Lit and Occupied 

Weekday, Lit and UnOccupied 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Figure 1. Break room average hourly lighting condition profile for the percentage of time when spaces were lighted 

during occupied and unoccupied periods for the weekdays. 

100%
 

90%
 

Total time 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Weekday, Lit and Occupied 

Weekday, Lit and UnOccupied 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Figure 2. Classroom average hourly lighting condition profile for the percentage of time when spaces were lighted 

during occupied and unoccupied periods for the weekdays. 

IES Paper #43 17 08/16/00 
0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

Total time 

Weekday, Lit and Occupied 

Weekday, Lit and UnOccupied 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 
 

Time of day 

Figure 3. Conference room average hourly lighting condition profile for the percentage of time when spaces were 

lighted during occupied and unoccupied periods for the weekdays. 

100%
 

90%
 

Total time 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

Weekday, Lit and Occupied 

Weekday, Lit and 

UnOccupied 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 
 

Time of day 

Figure 4. Private office average hourly lighting condition profile for the percentage of time when spaces were 

lighted during occupied and unoccupied periods for the weekdays. 

IES Paper #43 18
 08/16/00 
 

 

80 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

Total time 

Weekday, Lit and Occupied 

Weekday, Lit and UnOccupied 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Figure 5. Restroom average hourly lighting condition profile for the percentage of time when spaces were lighted 

during occupied and unoccupied periods for the weekdays. Energy use (kWh) 

70 

60 

50 

40 

30 

20 

10 

0 

62.65 

12.45 

Shift 1 Shift 2 

Time of day 

Figure 6. Break room main effects, Shift 1 and Shift 2 comparisons. 

IES Paper #43 19 08/16/00 
 

 

38.11 

37.01 

35.43 

33.00 

44.20 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Energy use (kWh) 

Baseline 5-min 10-min 15-min 20-min 

Timeout settings 

Figure 7. Break room main effects, baseline and timeout setting comparisons. 

120 

100 

80 

23.22 

96.37 

Shift 1 Shift 2 

Energy use (kWh) 60 

40 

20 

0 

Time of day 

Figure 8. Class room main effects, Shift 1 and Shift 2 comparisons. 

IES Paper #43 20 08/16/00 
98.72 

46.08 

49.16 51.52 53.49 

0 

20 

40 

60 

80 

100 

120 

140 

160 

Baseline 5-min 10-min 15-min 20-min 

Energy use (kWh) 

Timeout settings 

Figure 9. Classroom main effects, baseline and timeout setting comparison 

26.41 25.21 23.73 21.78 

36.90 

4.69 4.29 3.31 3.85 

10.69 

0 

10 

20 

30 

40 

50 

60 

70 

Energy use (kWh) 

Shift 1 

Shift 2 

Baseline 5-min 10-min 15-min 20-min Timeout settings 

Figure 10. Conference room post hoc comparisons for each shift for the baseline and each timeout setting with the 

95% confidence intervals. 

IES Paper #43 21 08/16/00 
27.47 26.48 25.21 23.48 

34.62 

1.71 1.85 1.97 1.53 

5.66 

0 

10 

20 

30 

40 

50 

60 

70 

Shift 1 

Shift 2 

Energy use (kWh) 

baseline 5 min 10 min 15 min 20 min Timeout settings 

Figure 11. Private office post hoc comparisons for each shift for the baseline and each timeout setting with the 95% 

confidence intervals. Energy use (kWh) 

100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

26.80 27.94 25.18 22.71 

33.82 

7.90 7.06 6.10 4.99 

25.56 

Baseline 5-min 10-min 15-min 20-min 

Shift 1 

Shift 2 

Timeout settings 

Figure 12. Restroom post hoc comparisons for each shift for the baseline and each timeout setting with the 95% 

confidence intervals 

IES Paper #43 22 08/16/00 
76.22 74.02 70.87 

66.00 

88.40 

7.25 7.82 6.53 5.59 

12.40 

0 

20 

40 

60 

80 

100 

120 

140 

Baseline 5-min 10-min 15-min 20-min 

Energy use (kWh)

Weekdays 

Weekends 

Timeout settings 

Figure 13. Break room post hoc comparisons for weekdays and weekends for the baseline and each timeout setting 

with the 95% confidence intervals. 

106.98 103.04 98.32 92.17 

197.45 

8.43 8.82 9.15 7.94 

43.49 

0 

50 

100 

150 

200 

250 

300 

350 

400 

Weekdays 

Weekends 

Energy use (kWh) 

Baseline 5-min 10-min 15-min 20-min 

Timeout settings 

Figure 14. Classroom post hoc comparisons for weekdays and weekends for the baseline and each timeout setting 

with the 95% confidence intervals. 

IES Paper #43 23 08/16/00 
31.10 29.50 

27.58 

25.09 

47.59 

3.50 3.65 3.79 3.31 

9.58 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

Baseline 5-min 10-min 15-min 20-min 

Weekdays 

Weekends 

Timeout settings 

Energy use (kWh) 

Figure 15. Conference room post hoc comparisons for weekdays and weekends for the baseline and each timeout 

setting with the 95% confidence intervals. Energy use (kWh) 

70 

60 

50 

40 

30 

20 

10 

0 

29.44 28.33 26.92 25.01 

40.29 

3.82 3.89 3.94 3.98 

6.40 

Baseline 5-min 10-min 15-min 20-min 

Weekdays 

Weekends 

Timeout settings 

Figure 16. Private office post hoc comparisons for weekdays and weekends for the baseline and each timeout setting 

with the 95% confidence intervals. 

IES Paper #43 24 08/16/00 
35.85 33.87 31.29 

27.71 

59.38 

5.16 5.74 4.50 3.73 

19.02 

0 

20 

40 

60 

80 

100 

120 

Weekdays 

Weekends 

Energy use (kWh) 

Baseline 5-min 10-min 15-min 20-min Timeout settings 

Figure 17. Restroom post hoc comparisons for weekdays and weekends for the baseline and each timeout setting 

with the 95% confidence intervals. 

1.4 

1.2 

1 

Average energy use (kWh) 0.8 

0.6 

0.4 

0.2 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Baseline 

5-minute time out 

20-minute time out 

Figures 18. Break room weekday load profiles for the baseline and for the shortest and longest time delay settings. 

IES Paper #43 25 08/16/00 
Average energy use (kWh) 1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Baseline 

5-minute time out 

20-minute time out 

Figures 19. Class room weekday load profiles for the baseline and for the shortest and longest time delay settings. 

1.4 

1.2 

1 

Average energy use (kWh) 0.8 

0.6 

0.4 

0.2 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Baseline 

5-minute time out 

20-minute time out 

Figures 20. Conference room weekday load profiles for the baseline and for the shortest and longest time delay 

settings. 

IES Paper #43 26 08/16/00 
Average energy use (kWh) 1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Baseline 

5-minute time out 

20-minute time out 

Figures 21. Private office weekday load profiles for the baseline and for the shortest and longest time delay settings. 

1.4 

1.2 

1 

Average energy use (kWh) 0.8 

0.6 

0.4 

0.2 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

Time of day 

Baseline 

5-minute time out 

20-minute time out 

Figures 22. Restroom weekday load profiles for the baseline and for the shortest and longest time delay settings. 

IES Paper #43 27 08/16/00