by samantha havassy and cori jackson t
TRANSCRIPT
RESEARCH
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The retail sector, which represents 13 percent of California’s commercial
lighting electricity use, has historically not embraced the use of light-
ing controls. Reasons for this are many including perceived high cost,
system complexity, and the potential for negative impacts on custom-
ers and sales. California building lighting energy-eff iciency standards for the retail
sector have a higher lighting power density allowance and fewer requirements for
mandatory controls in sales areas than other commercial and industrial building
space types. The use of adaptive lighting systems, which automatically adjust their
output and operation based on occupancy, daylight availability and other appli-
cation-specific criteria, are underutilized in the retail sector, leaving a significant
quantity of energy reduction potential unrealized. Retailers who design and apply
an eff ective adaptive lighting control strategy could see a significant reduction in
annual electricity costs and a rapid return on the investment.
Researchers at CLTC are investigating strategies to optimize lighting designs
and lighting control systems in order to maximize energy savings, minimize cost
and reduce potential for negative impacts on business. Work is ongoing; how-
ever, preliminary findings demonstrate several benefits of adaptive lighting and
its ability to meet the needs of the retail sector in California.
To achieve savings, retailers must elect to complete lighting system upgrades;
however, most are hesitant to complete energy-eff iciency projects. A retail market
survey conducted in connection with this research identified several important
factors that influence a retailer’s decision to complete an energy-eff icient lighting
upgrade (Figure 1). Almost half of retailers surveyed stated they would upgrade
their lighting systems if it led to increased sales. Less than 20 percent of those sur-
veyed said they would upgrade their
system if it would only decrease their
electricity cost and not increase sales.
When asked “why” they held reserva-
tions about conducting eff iciency proj-
ects, lack of understanding was cited
as the number one reason. In addition,
53 percent responded that high first
cost was a primary concern regarding
lighting upgrades.
USING WHAT YOU NEED, NOT WHAT’S ALLOWED
To better understand the potential
of updated lighting design and con-
trols optimization in the retail sector,
researchers began by comparing the
illuminance levels resulting from appli-
cation of maximum energy-code light-
ing power allowances to those resulting
from designs conforming to industry
recommended illuminance levels. Il-
luminance levels for both scenarios
were estimated through building simu-
lations. Reductions between that al-
lowed by California’s building eff iciency
standards and that recommended in
modern designs represent clear elec-
tricity savings for retail businesses.
Researchers examined two building
types: a warehouse store and a depart-
ment store (Tables 1 and 2). Three sets
of lighting designs were completed for
each building type. Each set utilized a
diff erent source type and included one
design that utilized the maximum LPD
allowed by 2013 Title 24’s area catego-
ry method for retail merchandise sales
Adaptive Lighting for Retail EnvironmentsBy Samantha Havassy and Cori Jackson
Figure 1: Reservations regarding lighting upgrades - retail business owners.
Source: Consumer Preference Survey on Directional
LED Replacement Lam
ps for Retail Application
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www.ies.org August 2015 LD+A 77
and wholesale showroom areas, and
a second designed to achieve the illu-
minance recommended by the IES. For
both space types, and all sources mod-
eled, the code-based designs used
more energy and provided more light
than was necessary to reach industry
recommended light levels.
For the warehouse store, design-
ing to IES recommended light levels
provides 13-43 percent savings over
code. Savings in the department store
model ranged from 53-60 percent.
OPTIMIZING CONTROLSMany factors contribute to the suc-
cess of control strategies. To maxi-
mize the value of an investment in
adaptive lighting, systems must be
optimized in terms of their operation.
Optimizing control settings such as
sensor time-out periods and the size
of sensor coverage zones is critical
to achieve maximum energy savings.
Researchers completed two studies
to understand how changes in light-
ing control settings aff ect overall
lighting energy use.
Occupancy Time-out and Zoning.
Most occupancy sensors allow us-
ers to select a time-out period, usu-
ally between 0 and 30 minutes, which
controls how quickly luminaires are
extinguished aft er the sensor no lon-
ger detects occupants in the space.
The length of the time-out period has
a direct influence on the energy use of
a lighting system. Systems controlled
Table 2: Department store - Electricity savings potential of an updated retail lighting design as compared to a design using the maximum LPD allowed by Title 24.
Source:CLTC
Table 1: Warehouse store - Electricity savings potential of a modern, retail light-ing design as compared to a design using the maximum LPD allowed by Title 24.
Source:CLTC
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RESEARCH
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exceed 30 minutes. Savings between a
30-minute time-out period and a one-
minute time-out period, using a zoned
control approach, were found to be ap-
proximately 17 percent.
Modeling Zone Size and Occupancy
Controls. Coverage zone size has an ef-
fect on occupancy sensor performance
with respect to accurate occupant de-
tection. Accuracy translates directly to
energy use. False triggers can increase
lighting energy use, while failure to de-
tect occupants can result in increased
savings but at the expense of light qual-
ity, safety and potentially sales.
To understand the relationship
between sensor coverage area, cov-
erage zone size and energy use, re-
searchers simulated the eff ects of
various sensor zone sizes on lighting
energy use using models developed
from audits of multiple department
stores in Northern California.
Lighting control systems normally
utilize one of two approaches for occu-
pancy sensor coverage. The first is to
use the minimum number of sensors
needed for coverage of the desired
area. With this strategy, one or more
sensors are typically mounted on the
ceiling to create one or more zones of
control. In the second approach, one
sensor is installed in each luminaire to
create multiple, small zones of control,
regardless of any overlap in coverage
area between sensors. These strate-
gies are independent of the number of
luminaires in the space.
Researchers created a model light-
ing plan and operating schedule for a
typical retail department store using
by occupancy sensors with a long
time-out period use more energy
than those controlled by sensors with
short time-out periods. The challenge
in a retail environment, however, is
to reduce frequent switching, which
can negatively impact customers and
sales. This is achieved by lengthening
the time-out period at the expense of
energy savings. To better understand
the tradeoff s associated with the use
of occupancy sensors in retail spaces,
researchers recorded the occupancy
patterns in a typical retail space and
applied varied occupancy time-out
profiles to the data, resulting in a
spectrum of energy use correlated to
occupancy sensor time-out period.
Researchers installed more than 50
light and occupancy data loggers in a
midsized retail store, approximately
12,000 sq ft (Figure 2), to gather infor-
mation on occupancy as compared to
lighting system use. Loggers were set
to record occupancy and light-level
status in one-minute increments for a
period of 30 days. The store sales floor
was broken into 12 zones. Within each
zone, researchers selected one data
logger to represent the zone’s “occu-
pancy sensor.” Researchers compared
the data from all other sensors in the
zone to the “occupancy sensor” in
order to determine the diff erence in
actual occupancy and lighting energy
as compared to that sensed by the “oc-
cupancy sensor.” This data was used to
calculate the percent of ON hours.
This calculation was repeated, ap-
plying increasing time-out periods to
the “occupancy sensor” in order to
map the relationship between time-
out period and lighting energy use.
As the time-out period increased, so
did the percent of ON hours for the
lighting system. For both the absolute
occupancy (that sensed by the zonal
“occupancy sensor”) and the local oc-
cupancy, increases in the time-out
period increased lighting energy use
by up to 30 percent. In California, occu-
pancy sensor time-out settings cannot
Figure 2: Absolute occupancy rates during business (green) and non-business (red) hours with a five-minute time-out.
Source:CLTC
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prototypical data collected through
on-site audit of three department
stores (Figure 3). Lighting consisted
of general area lighting for sales floors
and walkways, and track lighting for
wall displays and accents. The total
area of the model was approximately
48,000 sq ft . Luminaires simulated
were four-lamp 28-W 2x4 recessed
troff ers for the area lighting, one-
lamp 54-W T5HO recessed wall wash-
ers for decorative lighting and sets of
six 13-W halogen track lights for the
accent, display and feature lighting.
Luminaires were grouped together
into zones under four unique scenari-
os to simulate the eff ects of detected
occupancy and zone size on lighting
energy use. The first zoning scenario
utilized individual luminaire zones,
where each luminaire acted autono-
mously based on occupancy signals
from a sensor installed in the fixture.
The Small Zone grouping included
walkway troff ers and track controlled
in groups of three luminaires, and the
area troff ers controlled in groups of
six. The Medium Zone grouping includ-
ed walkway troff ers, area lighting and
track subdivided into approximately
10 zones. The Large Zone grouping
combined all of the walkway lighting
into one functional zone and subdi-
vided the remainder of the lighting
into four zones or quadrants, creating
a total of five control zones. All lumi-
naires within a zone react in tandem
based on the occupancy stimuli pro-
vided to the zone’s occupancy sensor.
Occupancy profiles were then ap-
plied to the space (Table 3). Through-
out the day, light levels varied to
accommodate diff erent occupancy
events within the store. During stan-
dard business hours, troff ers, walkway
lighting and track lighting dimmed to
20 percent when no store occupants
were detected. Lighting was also re-
duced before and aft er standard busi-
ness hours when employees were re-
stocking or doing other maintenance
tasks. During this time, the wall-wash
lighting remained off and the track
lights turned on only when triggered
by occupants. During all other times,
10 p.m. to 6 a.m., the wall washers and
track remained off and the troff er out-
put was reduced to 50 percent during
occupied periods and 20 percent dur-
ing unoccupied periods.
For each time period, and for each
zoning scenario, a high and low occu-
pancy map was applied (Figure 4). The
highest occupancy areas follow the
main paths within the store (red and
orange). When a zone had two or more
occupancy levels within its borders,
the higher occupancy rate was utilized
for the entire zone.
The use of large occupancy control
zones equated to increased lighting
energy use. Average annual savings
ranged from 13 percent to 23 percent
Table 3: Bi-level light level outputs for various store hours.
Source:CLTC
Figure 3: Zones for department store model.
Source:CLTC
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RESEARCH
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depending on the control zone size as
compared to the same store without
occupancy controls (Table 4). Control
zone size did not significantly impact
savings when zone size increased be-
yond approximately 500 sq ft .
RETAIL DEMONSTRATION Researchers utilized results from
these analyses as part of a demon-
stration project conducted to assess
the in-situ performance of adaptive
lighting systems in retail environ-
ments. The demonstration space was
composed of approximately 4,300 sq
ft of retail space in a multi-tenant light
commercial building.
The lighting demonstration pack-
age included LED area lighting and
a networked lighting control sys-
tem. The system was designed and
specified such that its performance
Figure 4: Occupancy profiles for high and low volume occupant scenarios.
Source:CLTC
Table 4: Control zone size and energy savings.
achieved IES recommended light
levels for retail applications. Existing
luminaire mounting locations were
maintained in most areas of the store.
Designs met LPD and controls re-
quirements contained in California’s
2013 Building Energy Eff iciency Stan-
dards (Title 24, Part 6). As of the time
of this demonstration, the allowed LPD
for the main retail merchandise sales
area was 1.2 watts per sq ft with two
“use it or lose it” allowances for accent,
display, feature and decorative lighting
for an additional total of 0.5 watts per
sq ft . A sales support area classified as
a general commercial and industrial
work area (in the demonstration, this
is a product repair area) had an al-
lowed LPD of 0.9 watts per sq ft .
Multi-level control strategies were
achieved by using two separate dim-
ming zones, one for the retail space and
another for the shop area. Additionally,
manual dimming did not override other
controls measures such as daylighting
and high-end tuning; however, man-
ual dimming did override scheduled
dimming or OFF periods for up to two
hours. Automatic shutoff control was
achieved through the use of zonal oc-
cupancy sensors and a scheduling
feature included with the digital con-
trol system. Daylighting controls were
also included, but were not required
by code for this alteration. Occupancy
sensors were used to control bi-level
dimming during vacant hours.
System Performance. Energy use
of the new system was monitored in
phases in order to attribute energy
savings to each of the control layers ap-
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Defining ‘Adaptive’An adaptive lighting system automatically adjusts its light output and
operation to provide targeted light levels based on environmental condi-
tions, user schedules or other application-specific criteria. The system can
include many diff erent types of products including dimmable lamps and
luminaires, occupancy sensors, photocontrols, time clocks, communica-
tion panels and wireless communication nodes. An adaptive system can
also oft en be manually tuned, over time, in terms of light level, and in some
cases, color, to provide optimal lighting conditions as designated by sys-
tem operators, building owners or occupants.
plied to the space. During phase one,
tuning, scheduling and occupancy con-
trol were enabled. Data was collected
for two weeks under these control
conditions. In the second phase, task
specific tuning was added. The new lu-
minaires, excluding application of con-
trol measures, saved 52 percent annu-
ally as compared to a Title 24 compliant
lighting system (Table 5). The addition
of bi-level, occupancy-based control
per the schedule resulted in an addi-
tional 10 percent energy savings, or 62
percent total savings annually. Applica-
tion of a 20 percent high-end trim to
tune the system to deliver light levels
consistent with industry recommenda-
tions resulted in a final system savings
of approximately 70 percent.
Applications. Many commercially
available advanced lighting control
systems contain functions and fea-
tures that show potential to bring
significant energy reductions to retail
environments. The projects described
address models and demonstrations
that can be applied to department
stores, warehouse stores, midsized re-
tail and small businesses located with-
in multi-tenant light commercial prop-
erties. Sub-sectors within retail with
specific lighting needs have yet to be
addressed, including restaurant and
grocery applications. While control
strategies will diff er among retail sites,
at a minimum, strategies should utilize
scheduling, high-end trim and occu-
pancy-based dimming. Control zones
and control device settings follow-
ing the guidelines developed through
this project can be expected to show
Table 5: Annual energy savings - retail demonstration site.
1 Based on 3,640 annual hours of use (actual business operating hours).2 Weighted average of 1.7 W/sf in main retail space and 0.9 W/sf in bike repair area.
similar savings results. Demonstration
showed that these recommendations
can result in significant energy savings
as compared to 2013 Title 24 building
energy-eff iciency standards.
For more information on the require-
ments of Title 24, Part 6 2013, visit cltc.
ucdavis.edu/title24 and download the
Retail Lighting Guide.
1
2
Samantha Havassy was an assistant development engineer with CLTC from April 2012 to April 2015.
Cori Jackson is the pro-gram director at CLTC.
THE AUTHORS
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