RTF Lighting Standard Protocol
Review of Hours of Operations,
Controls Savings, and Variation
Hours
• Context: SR relies on interview used for hours with
default as backup. How do our proposed defaults look?
• Review measured hours versus default
• Caveats
– All logged data by some method from studies meant to
ascertain hours
– Unknown protocols for annualization
– Unknown site sampling procedures & duration
– Know little about building/occupant charateristics
– Litlle know about sample selection
• It would take substantial time mine for those
unknowns
Hours of Operations Datasets Used
• CPUC & Itron. (2010). Small Commercial Contract Group Direct
Impact Evaluation Report CALMAC Study ID: CPU0019.01. Retrieved
from http://www.calmac.org/abstract.asp?id=2739
• CPUC Database for Energy Efficient Resources (DEER). (2010).
Summary of 2008 DEER Measure Energy Analysis Revisions Version
2008.2.05 – 09-11 Planning/Reporting Version, Comparison of 2005
and 2008 EFLH for Lighting. Retrieved from
http://www.deeresources.com/deer0911planning/downloads/DEE
R2008UPDATE-EnergyAnalysisMethodsChangeSummaryV9.pdf
• New York State. (2012). New York State Technical Resource Manual.
Retrieved from
http://www.aging.ny.gov/livableny/ResourceManual/Index.cfm
• Bonneville Power Administration. (2011). BPA C&I Lighting
Calculator. Retrieved from
http://www.bpa.gov/energy/n/projects/lighting
Default from Calculator
Total = 27 default options
Reviewed = 14
Reviews were completed
on datasets that had over 4
quality data points.
Descriptive statistics were
completed on all analyzed
default inputs. Data was
cleaned by eliminating
statistical outliers. See
workbook for full statistical
analysis. (tab: Stats Review
of Hours)
College or University
Descriptive statistics
Yearly Hours
Count
46
mean
2,719.26
sample variance
sample standard deviation
73,756.73
271.58
minimum
2137
maximum
3241
range
sum
sum of squares
deviation sum of squares (SSX)
1104
125,086.00
343,460,518.00
3,319,052.87
Hospital
Descriptive statistics
Yearly Hours
count
27
mean
4,088.89
sample variance
sample standard deviation
1,070,182.26
1,034.50
minimum
2497
maximum
5900
range
sum
sum of squares
deviation sum of squares (SSX)
3403
110,400.00
479,238,072.00
27,824,738.67
Lodging
Descriptive statistics
Yearly Hours
count
38
mean
3,605.42
sample variance
sample standard deviation
3,862,898.14
1,965.43
minimum
755
maximum
7884
range
sum
7129
137,006.00
sum of squares
636,891,548.00
deviation sum of squares (SSX)
142,927,231.26
Manufacturing
Descriptive statistics
Yearly Hours
count
37
mean
2,943.73
sample variance
sample standard deviation
166,890.59
408.52
minimum
2250
maximum
3916
range
sum
sum of squares
deviation sum of squares (SSX)
1666
108,918.00
326,633,216.00
6,008,061.30
Office <20,000 sf
Descriptive statistics
Yearly Hours
count
26
mean
2,746.58
sample variance
sample standard deviation
407,228.49
638.14
minimum
1556
maximum
3957
range
sum
sum of squares
deviation sum of squares (SSX)
2401
71,411.00
206,316,517.00
10,180,712.35
Office >100,000 sf
Descriptive statistics
Yearly Hours
count
28
mean
2,906.96
sample variance
sample standard deviation
430,193.81
655.89
minimum
1647
maximum
3860
range
sum
sum of squares
deviation sum of squares (SSX)
2213
81,395.00
248,227,591.00
11,615,232.96
Other Health, Nursing, Medical Clinic
Descriptive statistics
Yearly Hours
count
14
mean
3,621.93
sample variance
sample standard deviation
13,247.61
115.10
minimum
3468
maximum
3814
range
sum
sum of squares
deviation sum of squares (SSX)
346
50,707.00
183,829,351.00
172,218.93
Restaurant
Descriptive statistics
Yearly Hours
count
37
mean
4,036.19
sample variance
sample standard deviation
701,765.32
837.71
minimum
2724
maximum
5000
range
sum
sum of squares
deviation sum of squares (SSX)
2276
149,339.00
628,024,009.00
25,263,551.68
Retail Boutique <5,000 sf
Descriptive statistics
Yearly Hours
count
15
mean
3,138.67
sample variance
sample standard deviation
309,082.67
555.95
minimum
2402
maximum
4055
range
sum
sum of squares
deviation sum of squares (SSX)
1653
47,080.00
152,095,584.00
4,327,157.33
Retail Supermarket
Descriptive statistics
Yearly Hours
count
23
mean
3,844.17
sample variance
sample standard deviation
845,342.79
919.43
minimum
1979
maximum
4964
range
sum
sum of squares
deviation sum of squares (SSX)
2985
88,416.00
358,484,022.00
18,597,541.30
Retail Big Box >50,000 sf One-Story
Descriptive statistics
Yearly Hours
count
24
mean
3,408.33
sample variance
sample standard deviation
677,648.67
823.19
minimum
2338
maximum
4800
range
sum
sum of squares
deviation sum of squares (SSX)
2462
81,800.00
294,387,586.00
15,585,919.33
Retail Anchor Store >50,000 sf Multistory
Descriptive statistics
Yearly Hours
count
18
mean
3,214.44
sample variance
sample standard deviation
310,169.20
556.93
minimum
2559
maximum
4057
range
sum
sum of squares
deviation sum of squares (SSX)
1498
57,860.00
191,260,632.00
5,272,876.44
School K-12
Descriptive statistics
Yearly Hours
count
12
mean
2,372.75
sample variance
2,739.66
sample standard deviation
52.34
minimum
2291
maximum
2452
range
sum
sum of squares
deviation sum of squares (SSX)
161
28,473.00
67,589,447.00
30,136.25
Warehouse
Descriptive statistics
Yearly Hours
count
33
mean
2,874.73
sample variance
sample standard deviation
158,175.95
397.71
minimum
2074
maximum
3522
range
sum
sum of squares
deviation sum of squares (SSX)
1448
94,866.00
277,775,508.00
5,061,630.55
Some Conclusions
• Significant range for hours within some building
use types highlights the importance of the
interview part of SRM
• Use of default carries more uncertainty in some
use types than others
• Default hours for some building use types should
be revised
• Test of simplest reliable needs to include how
often default is used
• What is correlation between logged hours and
reported business hours?
Hours of Operations Conclusion
• Hours of Operations varies across building types and
within building types. It depends on the building,
occupants, type of work, and location within the
building (e.g.: office vs. break room vs. computer room
vs…..)
• Because of large variation within building types it will
be difficult to use defaults and estimate accurate
operating hours
• Even within the same utility, different programs report
different operating hours for the same building type.
• A regional primary study would most likely return the
same uncertainty and not be worth the $$.
Control savings as a %
Issues:
– Most analysis, case studies focus on full lighting
retrofits not just control upgrades
– There are large variations in control only reported
data
– Type of lights, control settings, and operating hours all
effect results
– Location of controls effects performance
3 studies are presented to show the variability
LBNL Meta-Analysis from Lighting
Controls
• Focus on commercial buildings by building type
• 240 savings estimates from 88 papers and case
studies
• Control types evaluated = Occupancy, Day
lighting, Personal tuning, Institutional Tuning, and
multiple types
• Report website:
http://efficiency.lbl.gov/drupal.files/ees/Lighting
%20Controls%20in%20Commercial%20Buildings_
LBNL-5095-E.pdf
LBNL Meta-Analysis Conclusions
•Table 7 page 15
•These are the data points used in the calculator
•These points are the average of all studies analyzed
•These numbers include studies that are calculations and actual installation
LBNL Meta-Analysis Conclusions
Large Standard deviations indicated (+ - 20%) uncertainty and varying results
Actual installations are not broken up by building type
Rensselaer Polytechnic Institute review
of Occupancy Sensors
• Evaluation of Documented and
undocumented studies by building location
type
• Evaluation identifies ranges and average
savings
• Most data evaluated reported prior to 2001
Rensselaer Polytechnic Institute review
of Occupancy Sensors Findings
M & V of Day lighting Photocontrols
• 5 Buildings evaluated (office, school,
manufacturing, medical, warehouse)
• Looked at the direction of Office Spaces
• All locations based in Idaho
• Report website:
http://www.idlboise.com/pdf/papers/201001
11_Final_Photo%20Controls.pdf
M & V of Day lighting Photocontrols
Findings
•Results from monitoring period during regular operation hours
•Direction of Offices has large significant impact on savings
•Variations in site savings provides uncertainty
•Numbers and savings % are different from the LBNL study
Controls Savings Conclusions
• Documented variations are large
• Occupancy variations, directional variations,
and space type variations, add to uncertainty
• Small samples of installations do not
accurately represent the population of
installations
• Most calculations overestimate actual savings