Steven Carlson, P.E.CDH Energy Corp.Evansville, WIwww.cdhenergy.com

ASHRAE Chicago, 2006

Energy Benchmarking

Presentation Overview

n What• Benchmarking as an Energy Management Tool

n Why• Identify savings potential• Prioritize where to look for improvements

n How• Comparison options

– Metrics– Data Sources

My Background (Biases?)

n Building Performance• Technology Demonstration• Metric Development• Commissioning• Monitoring & Verification• Energy Management• Feasibility Studies• Energy Simulations

Benchmarking - History

n Business: Total Quality Management"Benchmarking - a continuous, systematic process for evaluating the

products, services, and work processes of organizations that arerecognized as representing best practices for the purpose of

organizational improvement."

Michael J. Spendolini, The Benchmarking Book, 1992

n Identify actions to improve performance• Identify issues (metrics)• Collect Internal data (baseline)• Collect External data (comparison framework)• Analysis• Implement change• Monitor Impact

Building Energy Benchmarking

n Energy Management Tooln How am I doing?

• Relative to previous performance• Relative to portfolio• Relative to national average• Relative to a standard (“Best Practices”)

Wisconsin School Energy Cost Survey 1998918 Schools, 69.8 million sq ft, $0.622/sq ft avg.

0

50

100

150

200

250

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Energy Cost ($/sq ft)

Num

ber of

Sch

ools

Define Performance

n A Meaningful Metric• Rich dataset for comparison

– Compare to what?– Data source?– Comparison method?

• Normalize for unmanaged characteristics

– Building Area– Building Use– Level of service

- Outdoor air volumes- Comfort- Hours of use- Etc

Metrics

n Often normalized to arean Energy Cost ($/sqft)n Energy Use (kBtu/sqft)

• Source / Site ?• Electricity / Gas ?

n Related to...• Weather, Sales (meals served, beds), service level

n Desire to include multiple factors• f (floor area, hours per week, occupants, etc)

n Change in Rank Order• Financial• No normalizing factors – stay the same

n Scale: Whole building vs system leveln Often devised based on type of available data

Self Reference

n Comparison to past performance• More of a diagnostic than a “Benchmark”, but valid

energy Management tool• Validate project impact• Can look at small sub-system

Monthly Electricity Use

19971998 1999 2000 2001 2002 2003 20040

200

400

600

800

1000

Mwh

BasePeriod

ConstructionPeriod

Monthly Electricity Use

19971998 1999 2000 2001 2002 2003 20040

200

400

600

800

1000

Mwh

TotalOn-PeakOff-Peak

Ruby Isle (6373) Electricity Demand

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar2004 2005

0

100

200

300

400

500

600

kW

Ruby Isle (6373) Daily Electricity Load Line

0 20 40 60 80 100Daily Average Temperature (F)

0

2

4

6

8

10

12

Dai

ly E

lect

ricity

Use

(M

Wh)

Energy Use Change After 12-14-04

Energy Use Change After 9-16-04

Self Referencen Isolated from “Best Practices”n No comparison to othersn Only relative sense of

performance over time

Recommissioned

Internal Reference

n Internal data source (small organization)• Tabular ranking for small number of buildings• Example notes A/C characteristic• Example notes electricity price variation (load factor)• Energy pricing impacts cost metrics

Fond Du Lac School District Energy Costs (1998-99) Sorted by Energy Use Intensity ($/sq ft)Energy Costs Electricity Use and Cost Gas Use and Cost

Building A/C ft2 $/ft2 $/yr kWh $/kWh $/ft2 kWh/ft2 therms $/therm $/sq ft mBtu/ft2

6 Goodrich Sr. High no 220,684 0.641 141,458$ 1,477,200 0.0548 0.367 6.69 156,004 0.388 0.27 70.73 Fahey no 14,600 0.640 9,344$ 71,280 0.0635 0.310 4.88 11,454 0.421 0.33 78.58 Parkside yes 40,000 0.575 23,000$ 249,760 0.0655 0.409 6.24 16,036 0.414 0.17 40.1

13 Theisen yes 132,000 0.570 75,240$ 929,600 0.0653 0.460 7.04 37,186 0.393 0.11 28.22 Evans yes 48,600 0.533 25,904$ 268,320 0.0658 0.363 5.52 20,501 0.404 0.17 42.27 Lakeshore yes 63,400 0.533 33,792$ 472,800 0.0559 0.417 7.46 21,463 0.402 0.14 33.91 Chegwin yes 63,000 0.448 28,224$ 286,480 0.0615 0.280 4.55 25,885 0.410 0.17 41.19 Pier no 57,600 0.434 24,998$ 229,440 0.0588 0.234 3.98 28,552 0.403 0.20 49.6

12 Sabish Jr. High no 104,300 0.421 43,910$ 448,000 0.0565 0.243 4.30 46,699 0.399 0.18 44.815 Woodworth no 110,020 0.381 41,918$ 466,880 0.0552 0.234 4.24 40,589 0.398 0.15 36.910 Roberts no 62,054 0.369 22,898$ 223,600 0.0607 0.219 3.60 22,719 0.410 0.15 36.65 Franklin no 40,926 0.363 14,856$ 131,560 0.0631 0.203 3.21 15,862 0.414 0.16 38.8

14 Elizabeth Waters no 72,438 0.338 24,484$ 173,120 0.0633 0.151 2.39 33,571 0.402 0.19 46.311 Rosenow no 61,530 0.279 17,167$ 162,400 0.0624 0.165 2.64 16,788 0.420 0.11 27.3

All buildings 1,091,152 0.483 527,194$ 5,590,440 0.0594 0.304 5.12 493,309 0.399 0.18 45.2Uncooled 744,152 0.458 341,034$ 3,383,480 0.0548 0.259 4.55

Cooled 347,000 0.536 186,160$ 2,206,960 0.0658 0.400 6.36

Internal Reference

n Internal data source (large portfolio)• Rank similar properties• Implied similar characteristics• Can quantify benefit of reducing large users to norm• See only internal best practices

Corporate Store Gas Use Distribution

0 20 40 60 80 100Percentile

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Gas

Use

The

rm/s

f

External Reference

n Comparison to large scale data• Industry associations• Census data

n Limited by existing data setsn Data by others / analysis black boxn Normalizing Characteristics

• Weather, Floor Area, Use, etc

n Type of Comparison• Ranks / Distributions• Regressions• Standard / Best Practices

Site Electricity Use

1 5 9 13 16 20 24 28Observation

0

5

10

15

20

25

30

35

40

kWh/

sq ft

/yr

Northland ELLC

External ReferenceDirect Data Comparison

n Comparison of residence hall to CBECS micro data

n Data representative of ...• Broad classifications• Broad age range

n Limited Samplen Wide Range in EUIn Representative? Site Fuel Use

1 5 9 13 16 20 24 28Observation

0

50

100

150

200

250

300

350

400

mB

TU

/sq

ft/yr

Northland ELLC

External ReferenceDirect Data Comparison

Wisconsin Schools 1998 Energy Cost

847 Schools > 10,000 sq ft

0 10 20 30 40 50 60 70 80 90 100Percentile

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

$/sq

ft

Wisconsin Schools 1998 Energy Cost

847 Schools > 10,000 sq ft

0 10 20 30 40 50 60 70 80 90 100Percentile

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

$/sq

ft Typical$0.60/sfGood

$0.48/sf

n Industry specific data set (WI Schools)n WI-centric, doesn’t look at other statesn CA looking to benchmark all commercial buildings

External Reference

n Let others develop methodn Energy Star

• Multi-parameter• Representative

sample of sector• Rank specific

to buildingparameters

• Source energy

External Reference

n Point/Score system: Ranking/Grade (0-100)

How to Use the Information?

Moving Toward Best Practice

n How is it defined?• Target Score / Rating (relative performance)• System performance (rules of thumb)

– HVAC: sf/ton, cfm/sf, hp/cfm, OA cfm/person, kw/ton– Lighting: W/sf, W/lx

• Energy Model (absolute standard)

n How is it achieved?• Look at system details• Design characteristics (changeable?)• Operational parameters (changeable?)• Management actions (changeable?)

n Implementation & Feedback

Best PracticePerformance Target: Model

Daily Total Electricity Use

0 20 40 60 80 100Daily Average Outdoor Dry Bulb Temperature (F)

0

1000

2000

3000

4000

5000

6000

7000

8000

kWh/

day

WeekdaySundaySaturday

WeekdaySundaySaturdaySimulation

Using Benchmarking

n Benchmarking isn’t the destination,

Just the mile marker

n Benchmark only hints at potential for improvementn The benchmark is a tooln Still need to figure out where to go

• Apply expertise• Investigate systems• Devise changes• Assess performance

Summary Effective Benchmarking

n Define performance• Metrics

n Define peer group• Data set

n Define comparison method• Direct• Distribution / Rank / Score• Standard (Best Practice)

n Benchmark only gives the scoren Use information

• Investigate why• Motivate action• Confirm project impact• Manage energy use