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Wireless lighting control: a life cycle cost evaluation of multiple lighting control strategies.


Lighting controls present a key opportunity for designers and engineers to tune the lighting system to the needs of the occupants in a dynamic manner while potentially saving significant energy. As the need to reduce lighting energy consumption continues to increase, the ability to dynamically modify the energy use profile within a space is of great value, both to building owners and operators, and to the major utilities whose grid must respond.

This study evaluated the cost effectiveness and potential energy savings of a lighting control retrofit project in a typical 1970's or 1980's office building in two different geographical locations, Boston and Los Angeles. Multiple commercially-vailable lighting control systems, as detailed in the Methodology section, were compared to study the return on investing in lighting controls to capitalize on reducing lighting energy costs.


The primary intent of this study was to compare lighting control technologies that reduce energy use below code requirements, and to understand the cost-effectiveness of those technologies. Additionally, this study allowed the evaluation of the advantages of emerging, advanced and wireless lighting controls, including the cost impact of reduced wiring in retrofit and tenant finish office building controls. This study was also intended to provide a reference source for lighting designers and electrical engineers to communicate the potential savings of lighting control systems to their clients, while contributing analysis research to utility companies to encourage expansion of utility rebate programs for lighting controls.


The building selected for this study is based on a 1970's or 1980's office building with a typical 2'x4' acoustical grid ceiling. A single 25,000 ft2 floor of the building was selected for analysis. It was assumed that the ceiling height was 9'-0" and standard interior reflectances (80 percent/50 percent/20 percent) were assumed. Windows were located on all sides of the building at a 40 percent window-to-wall ratio extending from 2' -6" to 8'-0" above finished floor, and double-glazed with a low-e coating and a visible transmittance of 65 percent.

The floor used for this study, as shown in Fig. 1, used two different strategies for space planning to assess its impact on energy use. A traditional space plan was used on the west half of the floor, with perimeter private offices located near the windows and interior open offices. An inverted space plan was used on the east half of the floor which maximizes daylight availability by having perimeter open offices located near the windows and interior private offices.


For this study, six control system types were examined. It was assumed, for all control scenarios, that the luminaires themselves remain unchanged, except for ballast replacements where indicated.

Control Scenario 0, "Energy Baseline," was established as an energy baseline, and provided the bare minimum of devices necessary to meet the mandatory control provisions of ASHRAE 90.1-2007. The potential savings of advanced control strategies were evaluated relative to this energy baseline. For the office space considered, the mandatory requirements included automated control for all enclosed spaces and automatic shut-off for open office areas up to 2,500 square feet. Either vacancy sensors or time-switch control can be used to meet these requirements, and it was assumed that time-switch control was used since it is generally the lowest-cost method.


Control Scenario 1, "Localized Control," was established to examine the cost impact of upgrading to a noncentralized control system. This control strategy upgrade provided the bare minimum of devices necessary to meet the mandatory control provisions of California's Title 24 2008, which can be considered a codified example of an aggressive lighting control strategy. The mandatory requirements included occupancy-based automatic control for all enclosed areas. The mandatory requirements also included photocell control of perimeter open office spaces with access to daylight. In order to meet the mandatory requirements of multilevel operation, it was assumed that the ballasts are changed to provide inboard/outboard switching.

Control Scenario 2, "Relay Panel Switching," added an additional layer of flexibility and control via a central lighting relay panel. This scenario also enhanced the occupant-based control by adding vacancy sensors to all spaces. Photocells were also included in the open office spaces that have access to daylight. During periods of inactivity, the vacancy sensors automatically turned off the lighting, and required a manual-on operation. Photocells were provided to detect the availability of daylight, and respond with a signal to the relay panel, providing bi-level switching capability to maintain a consistent minimum interior workplane light level. Ballasts were changed out in areas with available daylight, to allow for inboard/outboard switching.

Control Scenario 3, "Dimming Panel," expanded on the hard-wired capabilities of the relay panel control scenario. It was assumed for this scenario that the existing ballasts were replaced with dimming ballasts in all areas except nondaylit open offices and support spaces. Photocells were added to allow daylight dimming and high-end trim dimming to reduce energy use. This scenario also included vacancy sensors in all locations with manual overrides. In the nondimmed areas, including the nondaylit open offices, the luminaires were fed through a relay panel, which receives signals from the occupancy sensors via the processor to trigger on/off action. The luminaires in the daylit spaces, including open offices, conference rooms and private offices, were fed through a dimming panel, which receives input from occupancy and daylight sensors via the processor to automatically control the lighting level.

Control Scenario 4, "Addressable Ballasts," further expanded on the capabilities of hard-wired systems by providing digital addresses for all ballasts and connecting them as a system through network cabling. The digital addresses allow individual ballast control and allow for re-zoning and flexibility over the life of the system. Photocells were provided in all spaces, with the exception of the support spaces, to allow for daylight dimming and high-end trim dimming. The entire lighting system was controlled via a software application provided on the server, which allows for feedback for maintenance personnel regarding lamp outages and energy usage. In this scenario, all ballasts were changed out with addressable dimming ballasts.

Control Scenario 5, "Wireless Partial Dimming," provided dimming in the private offices, conference rooms and open offices that have access to daylight, while using wireless switching control throughout the nondaylit and support spaces. The integration of wireless photocells allowed for dimming both to provide high-end trim and to respond to the presence of daylight. The batterypowered wireless photocells integrates simply into the scenario by communicating directly back to the area controller, which then responds with a signal to the dimming ballast adapters to dim the luminaires. Wired dual-technology occupancy sensors were also used in the open office areas, and use a wireless sensor adaptor to connect the sensors to the wireless system. Dimming ballasts were installed in luminaires in areas with daylight access.

Lastly, Control Scenario 6, "Wireless Full Dimming," provided the most robust control system, with full daylight dimming, high-end trim dimming, lumen maintenance dimming, and occupancy sensing in all major spaces. This scenario again used both wired and wireless occupancy sensors, and wireless photocells, all of which communicate via the wireless area controller to provide control over the luminaires. All ballasts in this scenario were changed out with dimming ballasts.



While the building shell was assumed to be a typical 1970's or 1980's office building, this study assumed that the lighting system was previously upgraded from T12 fluorescent lamps with magnetic ballasts to T8 fluorescent lamps with electronic ballasts. Therefore, the existing luminaire was assumed to be a 2'x4' 3-lamp T8 recessed parabolic troffer with standard instant start ballast, as shown in Fig. 2. The luminaires were assumed to generally be provided on an 8' by 8' grid throughout the space, with a single luminaire typical in the private offices. The resulting task-plane illuminance under this system was calculated to be 55 fc average, typical for a late 1990's lighting design.

For the controls retrofit, it was also assumed that the target illuminance level has been lowered to 35 fc, reflecting the current trend toward reduced ambient illuminance levels still within the recommended range for offices. Due to the ballast upgrades assumed, the resultant lighting power density of each scenario differed based on actual ballast losses, as shown in Table 1.

TABLE 1. Lighting power density per control scenario

Control            Connected
Scenario           Lighting

0-Energy                  1.10

1-Localized               1.11

2-Relay Panel             1.12

3-Dimming                 1.13

4-Addressable             1.15

5-Wireless                1.09
Partial Dimmin

6-Wireless                1.09
Full Dimming



An independent contractor estimated the capital and installation cost for each of these scenarios, including component, wiring, and installation costs. Using RSMeans [2009] regional cost data, all of the capital costs were adjusted for the appropriate region of the country.


Programming and commissioning costs were estimated for each control strategy by an independent commissioning agent. This effort included programming of the various occupancy and daylight sensors, and programming of all addressable and wireless components. The cost of commissioning included verification that the system performs as intended. The hourly rate of the commissioning agent was assumed to be the same for both locations, but the hourly rate of the electrician needed for many commissioning tasks was adjusted using RSMeans (2009) regional cost data.



Daylight simulations were performed for both locations for five key times per day (6am, 9am, 12 pm, 3 pm and 6 pm) under both clear and overcast sky conditions. Simulations were performed to study the daylight availability on the two solstices and on the 21st of each month in between, taking advantage of the symmetry of the solar year. To spread the data across a full calendar year, the monthly distribution of clear and overcast days for each location was determined based on data from the National Oceanic and Atmospheric Association [NOAA 2008], and the monthly values were weighted accordingly. Electric lighting workplane illuminance was calculated and used to determine the dimming levels for high-end trim dimming and daylight dimming under certain control scenarios. The resultant energy use when accounting for dimming, high-end trim and ballast performance was then determined. It was assumed that shades were drawn when interior illuminances exceeded 500 fc.


In order to determine the impact of occupant-based controls, an estimate of the hourly distribution of occupancy was obtained from the National Renewable Energy Laboratory [Deru 2007]. The weekday, Saturday and Sunday hourly occupancy profiles were weighted to create a single profile. These four occupancy profiles are shown in Fig. 3.



It was assumed that in a space without access to daylight or in a space with access on an overcast day, the occupants would turn on 100 percent of the lights if given control. In spaces with access to daylight on clear days, it was assumed that the occupants would turn on only 70 percent of the lights.


To account for the energy savings of occupancy sensors, previous research was found that provided an overall estimate of the anticipated energy savings based on space type and sensor delay time as shown in Table 2. For the "Sweep Auto-Off' Control Strategy, the energy savings was estimated as the mean savings from the Auto-Off results.


Since this simulation was performed hourly, the hourly impact of the occupancy sensors on reducing energy use was of interest. A series of curves were created that show energy savings due to occupancy as a function of the percentage occupied, as shown in Fig. 4.

TABLE 2. Estimated energy savings to occupancy sensors, vacancy
sensors and time-sweep due

Strategy   Control         Open       Open       Private
           Details         Office -   Office -   Office
                           No         With
                           Daylight   Daylight

None                       0%                   0%

Vacancy    Manual-On/      21.80%     21.80%     34% (Von
Sensing    Automatic-      (Von       (Von       Neida
           Off with        Neida et   Neida et   et al.
           10-Minute       al. 2001)  al. 2001)  2001)
           Manual-On/      21.80%     21.80%     28% (Von
           Automatic       (Von       (Von       Neida
           -Off with       Neida et   Neida et   et al.
           20-Minute       al. 2001)  al. 2001)  2001)

Occupancy  Automatic-On!   29%        29%        27% No
Sensing    Automatic-Off   15-Minute  15-Minute  delay
           with Various    Delay      Delay      (EERE
           Delays          (Galasiu   (Galasiu   Updated
                           2009)      2009)      2007)

Sweep      Manual-On/      21.80%     21.80%     31%
Auto-Off   Manual-Off
           Sweep After

Strategy   Conference  Support
           Room        Spaces

None       0%          0%

Vacancy    46% (Von    57.5%
Sensing    Neida et    (Von
           al. 2001)   Neida
           39% (Neida  57.5%
           Von etal.   (Von
           2001)       etal.

Occupancy  39% Delay   57.5%
Sensing    time        Delay
           anknown     time
           (Von Neida  unknown
           et a!.      (Von
           2001)       Neida
                       et al.

Sweep      42%         57.5%

When the space is at very low occupancy, it was assumed that the occupancy sensors would provide the most savings. For the private offices, conference rooms, and support spaces, it was assumed that the energy savings is nearly linear in its relationship to occupancy. For the open offices, it was assumed that the energy savings would reach zero before the space became fully occupied. The mean savings, for each of the curves across the full occupancy range, was controlled to match the resultant energy savings determined by the previous studies. The hourly anticipated savings, as a function of the occupancy level and type of occupant-based control, was then determined.


For each hour, the resultant energy use was determined as a percentage of the installed lighting power density over that time. That calculation was based on the effects of daylight dimming, high-end trim dimming, and occupant-based controls. Finally, the total annual energy use in each zone under each control scenario was determined, as well as the peak monthly power demand.


A life cycle cost analysis combined the capital and commissioning cost with the annual energy costs of each system. Assuming a real discount rate of 5 percent, these annualized energy costs were discounted to a present value over a 10-year analysis period. Adding the capital cost to the present value of the energy costs resulted in a total life cycle cost for each scenario.


The scenarios described are fictional and were never actually built. This allowed a thorough analysis of estimated capital, energy, installation, and commissioning costs. However, actual operating costs and equipment maintenance issues were not analyzed. Additionally, all capabilities of each control technology were applied to the entire scenario to provide for maximum flexibility.

The existing luminaire configuration was not adjusted from the current spacing of 8'x8', and the luminaires were not de-lamped for any scenarios. This results in tuned illuminance levels for all scenarios and zones that use photocells to dim or switch down to the target illuminance. However, scenarios and zones without photocells do not provide the same tuned illuminance.

Annual energy costs include the impacts of the utility rate structures from NSTAR for Boston [NSTAR 2010] and Southern California Edison for Los Angeles [Southern California Edison 2010]. Capital costs include currently available rebates for dimming ballasts, sensors and control systems from the respective utilities.



Figure 5 summarizes the anticipated annual energy density for both Los Angeles and Boston under all six control scenarios, along with the anticipated energy density of the energy baseline scenario. As is shown in the figure, the Wireless Full Dimming scenario is anticipated to have the lowest annual energy consumption, due to the integration of occupancy-based control, daylight harvesting and high-end trim dimming.

When considering both locations, the results show that the Wireless Full Dimming and Addressable Ballasts scenarios result in very similar annual energy densities due to the similar distribution of daylighting and occupancy controls. The Wireless Partial Dimming and Dimming Panel scenarios result in similar energy densities as both use daylight-responsive dimming in perimeter spaces, but do not take advantage of daylight sensors to provide tuned high-end trim dimming. The Relay Panel and Localized Control scenarios provide similar energy use, with the Relay Panel energy density slightly lower due to the inclusion of inboard/outboard switching capabilities on all luminaires in spaces with access to daylight. In general, though, all of the studied advanced control strategies were able to provide significant energy savings compared to the energy baseline.

Generally, the lighting energy density in Los Angeles is lower than in Boston, based in the fact that he climate in Los Angeles shows more clear days per month then in Boston. For control scenarios that include automated daylight-responsive dimming or switching, the lower energy density in Los Angeles is based on the increased daylight availability. For the ASHRAE energy baseline, which does not include daylight-responsive control, the difference in energy consumption per location is based on the assumed manual-on behavior in daylighted spaces which is related to sky conditions.



Figure 6 illustrates the resulting annual energy density for each location, split according to the type of space planning. It is clear that, for daylight-responsive control scenarios, the "Inverted Space Plan" side of the model resulted in a significantly lower energy density for both locations. For the energy baseline, which does not include the impact of daylight-responsive switching, the resulting variation in energy densities is mostly due to the distribution of interior spaces, as shown in Fig. 7, and their associated access to daylight and assumed manual-on behavior. For Control Scenarios 4 and 6, which include both daylight-responsive and high-end trim dimming, the impact of space planning is minimal since the majority of the dimming energy savings is due to high-end trim dimming.


The results for both Boston and Los Angeles show that the advanced lighting control strategies result in a lower life-cycle cost then the traditional control strategies.

Figure 8 illustrates the life cycle cost of the various scenarios in Los Angeles. The Wireless Full Dimming scenario has the lowest life-cycle cost when considering an analysis period of 10 years, with the Wireless Partial Dimming and Addressable Ballast scenarios following closely behind. The two conventional panel-based systems require the highest initial investment, and do not save sufficient energy to offset that high cost.

Figure 9 illustrates the life cycle cost of the various scenarios in Boston. The Wireless Full Dimming scenario was shown to have the lowest life-cycle cost when considering a 10-year analysis period, due to improved energy savings at a marginally higher capital cost. That scenario, though, is closely followed by both the Wireless Partial Dimming scenario and the Addressable Ballast scenario, which provide high energy savings leading to the lowest operating cost.

Fig. 7. Distribution of space types by area.

Traditional Plan - Private Office Area           4015
Traditional Plan - Open Office Area              7871
Traditional Plan - Conference Room Area           868
Traditional Plan - Support Spaces Area            884
Aggressive Energy Plan - Private Office Area     2363
Aggressive Energy Plan - Open Office Area        8136
Aggressive Energy Plan - Conference Room Area     564
Aggressive Energy Plan - Support Spaces Area      899

Note: Table made from pie chart.

Again, the traditional panel-based scenarios result in the highest life-cycle cost, mostly due to the high capital cost. Upgrading to a Title-24 compliant scenario, which can be used as a benchmark for codified efficient lighting controls, is less costly then providing a full panel-based system, requires the lowest commissioning investment, and reduces energy use to around 15 percent below the ASHRAE energy baseline.






Capital costs provided by an independent contractor were analyzed based on specific categories.

Control Equipment includes the cost of lighting control equipment supplied to the project via the Distributor and Contractor, including switches, control stations, occupancy and vacancy sensors, photocells, power packs, and commissioning tools.

Devices includes the Contractor's cost of labor, overhead and electrical commodities required to install switches, wall box dimmers, wall box occupancy sensors, control stations and photocells.

Branch Circuit Wiring includes the Contractor's cost of materials, labor and electrical commodities required to provide both power and control signals, where used, from the panel to the lighting load. It also includes the cost of routing control signal wiring between sensors and control components.

Demolition includes the Contractor's cost of labor and overhead required to remove existing control devices, including switches, and branch-circuit wiring.

Lighting System includes the Contractor's cost of materials, labor and electrical commodities required to install new ballasts in luminaire and make electrical connection from junction box to luminaire.

Control System includes the Contractor's cost for materials, labor, overhead and electrical commodities required to install relay panels, dimming panels, contact interfaces, time switches, busway controllers, servers, and processors.

Lighting System Demolition includes the Contractor's cost of labor, overhead, and electrical commodities required to remove existing ballasts and their associated wiring.

Commissioning includes the Commissioning Agent's cost for commissioning, programming and calibrating the lighting control system. Commissioning includes coordinating and developing the Owner's project requirements, reviewing the lighting controls design and specifications, developing commissioning specifications, reviewing Contractor submittals, preparing a Commissioning Plan, verifying that the installation meets the Owner's project requirements and verification of system performance.

Programming and Calibrating varies depending on the type of lighting control system. Programming for Addressable Ballast system requires a few more steps to locate individual ballast addresses and define control groups. Calibration of sensors is similar for all control systems. Programming and Calibration includes: identifying, addressing, and establishing communication between panels, ballasts, sensors, and devices (Addressable Ballasts only); programming ballast control groups (Addressable Ballasts and Wireless only); programming dimming scenes, time schedules, utility demand response strategies and daylight dimming response; calibrating occupancy sensor sensitivity and time delay; and setting photo-sensor set points, dead-band adjustment, and fade rates.


The breakdown of the total capital cost for both locations is shown in Fig. 10 for Los Angeles and Fig. 11 for Boston, for the six analyzed control scenarios. The Conventional Panel Dimming scenario resulted in the highest capital cost due to the extensive rewiring work needed. Though the control equipment cost is highest for the Wireless Partial and Full Dimming scenarios, the total capital cost is lower than the conventional upgrades due to a reduced need to restructure current wiring or run additional conductors to carry control signals. This reinforces the need to have an experienced contractor who is familiar with advanced control strategies, and can help to realize the potential for significantly reduced installed costs.

The capital costs for the two locations are not identical because of regional variations in the appropriate hourly rates for certified electricians, which are involved both in the installation and commissioning of the system.


The total capital costs for both locations also include the available equipment rebates from the two electrical utilities, as shown in Fig. 12. In Los Angeles, rebates are provided based on the quantity of controlling equipment, such as occupancy sensors and photocells, resulting in fairly uniform rebates across control scenarios. In Boston, rebates are provided based on the quantity of controlled lighting equipment, resulting in larger rebates when a more significant quantity of the lighting is controlled.






For the analysis in Boston, Rate Schedule B1 from NSTAR [NSTAR 2010] was used, which incorporates a tiered energy use billing system and a demand system, and provides a different rate for peak summer conditions.

For Los Angeles, Schedule GS-2 from Southern California Edison [Southern California Edison 2010] was used. This rate is a time-of-use schedule that is split for summer and winter, and also considers On-Peak, Mid-Peak and Off-Peak conditions.

The differences in the utility rate structures are apparent through the potential energy cost savings seen with daylight-responsive dimming. In Boston, where no time-of-use schedule is used, the cost-savings from reducing peak energy use during the daytime is less significant than in Los Angeles, where energy rates peak at that same time.


For both locations and under each of the eight control scenarios, the Annual Energy Cost Density was determined, considering both energy and demand charges.

In general, the demand charges in Boston are higher than in Los Angeles. In Los Angeles, a single demand charge rating is applied throughout the year, but in Boston, the demand charged is significantly increased during the summer months. Combined with lower daylight availability in Boston, the peak demand reached at mid-day results in a significantly higher demand-related annual cost.

The energy cost density is much higher in Los Angeles, due to the impact of the time-of-use rate structure. In particular, the scenarios that do not employ daylight-responsive controls result in a much higher energy cost density in Los Angeles then in Boston, since the peak pricing occurs near the peak of daylight availability.


The life cycle cost evaluation was able capture economic benefits well after an initial cost has been paid back.

Figure 13 illustrates the life-cycle cost of the six control scenarios in Los Angeles as a function of the analysis period length. As shown, the Wireless Full Dimming scenario has the lowest life-cycle cost at a 10-year period. This is due to additional energy savings beyond the Localized Control scenario through dimming in all spaces combined with a capital cost that is not significantly more.


The Wireless Partial Dimming scenario has a lower initial cost than the Localized Control scenario, and uses less energy, resulting in a lower life-cycle cost immediately after installation is complete. However, the energy savings seen in the Wireless Full Dimming scenario are greater than with Partial Dimming, with a break-even point between the two wireless scenarios between four and five years.

As is shown, the Addressable Ballast scenario has a break-even point in lifetime cost of ownership with the Localized Control scenario when the analysis period is around four years, which indicates that the initial investment into the advanced control system is essentially paid back within the first four years of ownership when comparing to an upgrade to a more standard system.

Both of the wired system upgrades, the Relay and Dimming Panel scenarios, result in the highest life-cycle cost due to the very high initial investment required to reconfigure the lighting control, which is not offset with energy savings.

Figure 14 shows the life-cycle cost of the various control scenarios in Boston. The Wireless Partial Dimming and Wireless Full Dimming scenarios have lower capital cost than the Localized Control scenario and maintain a lower cost-of-ownership across the analysis periods. Additional energy savings with the Wireless Full Dimming results in the lowest 10-year life cycle cost, with a break-even point between the two wireless scenarios at about seven years.

The Addressable Ballast scenario requires a higher initial investment compared to the Localized Control scenario, but due to significant energy savings, it has a break-even point around five years with the Localized Control system. The very high initial cost of the Relay Panel and Dimming Panel scenarios result in a very high life-cycle cost, and those systems do not pay back within the timeframes analyzed.



The results of the energy analysis illustrated that advanced lighting controls can save energy compared to conventional lighting controls. Advanced lighting controls, in this study, were estimated to provide up to 49 percent annual energy savings compared to the energy baseline, due to the layering of occupancy-sensing, daylight response and high-end trim dimming.

Networked controls, both wireless and wired, can be cost effective, especially when the utility rate is based on time-of-use. Relay Panel and Panel Dimming lighting controls are not cost effective for an aggressive energy retrofit strategy that incorporates daylight and occupancy responsive control, as the cost of rewiring branch circuits is too high.

Intelligent wireless and addressable lighting control systems can save more money and energy than conventional localized and centralized lighting control systems. Additional benefits of addressable lighting controls not accounted for in this cost analysis include easy reconfiguration, load-shedding and maintenance reporting.

This study also illustrated the impact of space planning on energy consumption. As shown, the Traditional space plan results in a higher energy density for daylight-responsive control systems, since the daylight penetration is restricted to the perimeter private offices. The Inverted space plan allows the daylight to be used throughout the open offices, thus impacting a larger footprint and more occupants.

Increased commissioning costs are often used as a reason to choose a conventional lighting control system over a networked addressable lighting control system. The results of this study show that the cost of rewiring in a retrofit application far outweighs the additional commissioning costs.

Utility rebates can help provide incentive for projects seeking energy efficiency but are restricted by capital costs. The results in Figs. 10 and 11 demonstrate that wireless lighting controls compete very closely for the lowest capital costs, even before utility rebates are applied. When utility rebates are linked to controlled load, such as dimming ballast incentives, the rebates can fully offset the additional cost of commissioning.


This research work was funded by Daintree, Inc, with input from Group 14 Engineering and Energy Products Associates, LLC.


[DOE] U.S. Department of Energy, Energy Efficiency and Renewable Energy, Federal Energy Management Program. 2007. Lighting control types. Available from: [2010 May]

Deru M. 2007. Energy savings modeling and inspection guidelines for commercial building federal tax deductions. 2nd ed. Golden (CO): National Renewable Energy Laboratory. Technical Report NREL/TP-550-40467. 54 p.

Galasiu AD, Newsham GR. 2009. Energy savings due to occupancy sensors and personal controls: a pilot field study. Ottawa (Canada): National Resource Council Canada. Technical Report No. NRCC-51264. 9 p.

[NOAA] National Oceanic and Atmospheric Administration. 2008. Cloudiness-Mean number of days. Available from: [2010 May]

NSTAR. 2010. Electric business rates. Available from: [2010 May]

RSMeans Company. 2009. Electrical cost data. 32nd ed. Kingston (MA): RSMeans.

Southern California Edison. 2010. Schedule GS-2 general service rate. Available from: [2010 May]

Von Neida B, Maniccia D, Tweed A. 2001. An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems. J Illum Eng Soc. 30(2):111-125.

Dane Sanders (1) PE, and Darcie Chinnis (2)

(1) Clanton & Associates Inc., Boulder, CO,; (2) University of Colo-rado, Boulder, CO

doi: 10.1582/LEUKOS.2011.08.01.004
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Author:Sanders, Dane; Chinnis, Darcie
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Geographic Code:1USA
Date:Jul 1, 2011
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