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Why Care About Building Energy Why Care About Building Energy Performance?Performance?

Aside from building energy increasing?

Ignoring performance ratings is choosing to fly fairly blind — staying at the “dumb” end of the “dumb and dumber” scale

Performance ratings are an evaluation, quickly, and not an investigation

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New Construction has been a problem for 50 New Construction has been a problem for 50 years, increasing carbon footprintyears, increasing carbon footprint

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50

100

150

200

250

300 2003 CBECS

data with malls, kBtu/sq-ft-yr weighted means, higher source energy EUIs in newer buildings

CBECS data show same pattern with each survey year, life-cycle influences are shown

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Basic Energy Benchmarking Basic Energy Benchmarking (Performance) Info(Performance) Info

Go to TC 7.6 website (shown on title slide previously)

Select Program Activities at bottom

Chicago 2006, Seminar 17, first presentation

Atlantic City 2002, Seminar 41, first two presentations

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Current ASHRAE High-Performance Current ASHRAE High-Performance Protocol ProjectProtocol Project

“ASHRAE needs to provide guidance regarding the measurement and reporting of the performance of new and existing [commercial] buildings . . . .”

“ . . . to further the development of building energy performance standards.”

“Measuring and Reporting the On-site Performance of Buildings . . .”

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ASHRAE STANDARD 105ASHRAE STANDARD 105 1984 to now 1984 to now

BSR / ANSI / ASHRAE Standard 105-1984 (RA99) covers measurement and expression of building energy performance at a basic level, with suggested optional extensions

Standard 105-[2007?] is a major revision and has been submitted for publication. It extends the coverage of energy performance measurement and expression, and comparison of building energy performance against others

The nature and level of performance comparison requires some performance “standard” and requires or intrinsically offers some evaluation

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Standards of ComparisonStandards of Comparison

1. Minimum prescriptions or best practice levels (Stds 90.1, 90.2, 189P, LEED)

2. Self-reference, e.g., past and future

3. Ad-hoc building populations

4. Representative populations, e.g., CBECS, RECS for USA and CEUS for CA

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2007 Applications Handbook2007 Applications HandbookEnergy Comparisons using CBECSEnergy Comparisons using CBECS

Chapter 35, energy management, 3 tables on commercial buildings

Based on 2003 CBECS micro-data without malls

About 50 building types

Site energy use indexes for mean and percentiles 10, 25, 50, 75, and 90

Electricity and cost indexes at same detail

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Commercial Buildings Energy Commercial Buildings Energy Consumption Survey, CBECSConsumption Survey, CBECS

Latest survey micro data available = 2003, next is 2007 (released in 2010?)

Publicly available government reports and data on EIA website

Nationally representative sample, with fairly complicated cluster sampling frame

Different versions have been available, ~5,000 records

Not including imputation flags, there are ~350 data parameters

Data seem to get better each time

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CBECS and CEUS,CBECS and CEUS, some important differences some important differences

Item CBECS 2003 CEUS 2003

Survey approach Phone Site, skilled

Unit of interest One building, even if a campus

Site, including campuses

Characteristics detail Limited Very detailed

Floor area limits due to masking

< 1,000,000 sq ft for valid data

No limit, but not over 2M here

Fuel data limitations Propane data coarse Only gas and electric real

Simulated or regressed end uses

None Simulated

Fuel cost data Annual by fuel None

Fuel data intervals Annual only Monthly

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Basic EUI StatisticsBasic EUI Statistics kBtu/sq-ft per yr kBtu/sq-ft per yr

Quantity, all weighted CBECS 2003

N = 4678

CEUS 2003

N = 2360

Mean 216 208

10th percentile 30.8 27.6

25th percentile 66.1 57.8

Median 134 115

75th percentile 244 204

90th percentile 449 521

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Floor Area Distributions, Sq FtFloor Area Distributions, Sq Ft

Quantity, all weighted CBECS 2003

N = 4738

CEUS 2003

N = 2360

Mean 14,352 8,813

10th percentile 1500 832

25th percentile 2400 1200

Median 5000 2444

75th percentile 12000 5280

90th percentile 28000 14,960

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Week Schedule, hr/week openWeek Schedule, hr/week open

Quantity, all weighted CBECS 2003

N = 4360

CEUS 2003

N = 2360

Mean 63.7 61.6

10th percentile 16 40

25th percentile 40 45

Median 50 50

75th percentile 75 70

90th percentile 168 98

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Worker DensityWorker Density workers per 1,000 Sq-ft workers per 1,000 Sq-ft

Quantity, all weighted CBECS 2003

N = 4360

CEUS 2003

N = 2352

Mean 1.37 2.69

10th percentile 0.05 0.53

25th percentile 0.38 1.00

Median 0.86 1.88

75th percentile 1.74 3.51

90th percentile 3.13 5.00

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Density of PCsDensity of PCs PCs per 1,000 Sq-ft PCs per 1,000 Sq-ft

Quantity, all weighted CBECS 2003

N = 4360

CEUS 2003

N = 2127

Mean 1.32 1.89

10th percentile 0.15 0.22

25th percentile 0.31 0.46

Median 0.69 1.00

75th percentile 1.68 2.50

90th percentile 3.25 4.63

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Rough-cut, Incomplete Regression Rough-cut, Incomplete Regression Models, weightedModels, weighted

Parameter coefficient

>> intercepts not signif.

CBECS 2003

N = 4300

CEUS 2003

N = 2352

EUI change per hr/wk 1.9 4.1

EUI change w/ worker density

50 25.7

Lab, change in EUI from average

386 NS

Offices – 52 – 49

Clinics – 42 – 33

Restaurant 336 82.8

Fast Food 830 272

Average EUI 245 179

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Not done fishing yetNot done fishing yet