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RTF Lighting Standard Protocol Review of Hours of Operations, Controls Savings, and Variation

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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

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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

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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)

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College or University Descriptive statistics Yearly Hours Count46 mean2,719.26 sample variance73,756.73 sample standard deviation271.58 minimum2137 maximum3241 range1104 sum125,086.00 sum of squares343,460,518.00 deviation sum of squares (SSX)3,319,052.87

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Hospital Descriptive statistics Yearly Hours count27 mean4,088.89 sample variance1,070,182.26 sample standard deviation1,034.50 minimum2497 maximum5900 range3403 sum110,400.00 sum of squares479,238,072.00 deviation sum of squares (SSX)27,824,738.67

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Lodging Descriptive statistics Yearly Hours count38 mean3,605.42 sample variance3,862,898.14 sample standard deviation1,965.43 minimum755 maximum7884 range7129 sum137,006.00 sum of squares636,891,548.00 deviation sum of squares (SSX)142,927,231.26

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Manufacturing Descriptive statistics Yearly Hours count37 mean2,943.73 sample variance166,890.59 sample standard deviation408.52 minimum2250 maximum3916 range1666 sum108,918.00 sum of squares326,633,216.00 deviation sum of squares (SSX)6,008,061.30

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Office <20,000 sf Descriptive statistics Yearly Hours count26 mean2,746.58 sample variance407,228.49 sample standard deviation638.14 minimum1556 maximum3957 range2401 sum71,411.00 sum of squares206,316,517.00 deviation sum of squares (SSX)10,180,712.35

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Office >100,000 sf Descriptive statistics Yearly Hours count28 mean2,906.96 sample variance430,193.81 sample standard deviation655.89 minimum1647 maximum3860 range2213 sum81,395.00 sum of squares248,227,591.00 deviation sum of squares (SSX)11,615,232.96

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Other Health, Nursing, Medical Clinic Descriptive statistics Yearly Hours count14 mean3,621.93 sample variance13,247.61 sample standard deviation115.10 minimum3468 maximum3814 range346 sum50,707.00 sum of squares183,829,351.00 deviation sum of squares (SSX)172,218.93

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Restaurant Descriptive statistics Yearly Hours count37 mean4,036.19 sample variance701,765.32 sample standard deviation837.71 minimum2724 maximum5000 range2276 sum149,339.00 sum of squares628,024,009.00 deviation sum of squares (SSX)25,263,551.68

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Retail Boutique <5,000 sf Descriptive statistics Yearly Hours count15 mean3,138.67 sample variance309,082.67 sample standard deviation555.95 minimum2402 maximum4055 range1653 sum47,080.00 sum of squares152,095,584.00 deviation sum of squares (SSX)4,327,157.33

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Retail Supermarket Descriptive statistics Yearly Hours count23 mean3,844.17 sample variance845,342.79 sample standard deviation919.43 minimum1979 maximum4964 range2985 sum88,416.00 sum of squares358,484,022.00 deviation sum of squares (SSX)18,597,541.30

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Retail Big Box >50,000 sf One-Story Descriptive statistics Yearly Hours count24 mean3,408.33 sample variance677,648.67 sample standard deviation823.19 minimum2338 maximum4800 range2462 sum81,800.00 sum of squares294,387,586.00 deviation sum of squares (SSX)15,585,919.33

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Retail Anchor Store >50,000 sf Multistory Descriptive statistics Yearly Hours count18 mean3,214.44 sample variance310,169.20 sample standard deviation556.93 minimum2559 maximum4057 range1498 sum57,860.00 sum of squares191,260,632.00 deviation sum of squares (SSX)5,272,876.44

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School K-12 Descriptive statistics Yearly Hours count12 mean2,372.75 sample variance2,739.66 sample standard deviation52.34 minimum2291 maximum2452 range161 sum28,473.00 sum of squares67,589,447.00 deviation sum of squares (SSX)30,136.25

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Warehouse Descriptive statistics Yearly Hours count33 mean2,874.73 sample variance158,175.95 sample standard deviation397.71 minimum2074 maximum3522 range1448 sum94,866.00 sum of squares277,775,508.00 deviation sum of squares (SSX)5,061,630.55

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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?

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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 $$.

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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

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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

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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

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LBNL Meta-Analysis Conclusions Large Standard deviations indicated (+ - 20%) uncertainty and varying results Actual installations are not broken up by building type

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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

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Rensselaer Polytechnic Institute review of Occupancy Sensors Findings

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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

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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

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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

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