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

ASHRAE Chicago, 2006

Energy Benchmarking

Presentation Overview

What• Benchmarking as an Energy Management Tool

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

How• Comparison options

– Metrics– Data Sources

My Background (Biases?)

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

Benchmarking - History

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

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

organizational improvement."

Michael J. Spendolini, The Benchmarking Book, 1992 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

Energy Management Tool 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.

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Define Performance

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

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

• Source / Site ?• Electricity / Gas ?

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

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

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

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

Self Reference

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

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Ruby Isle (6373) Electricity Demand

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2004 2005

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Ruby Isle (6373) Daily Electricity Load Line

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Self Reference Isolated from “Best Practices” No comparison to others Only relative sense of

performance over time

Recommissioned

Internal Reference

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 40.113 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 49.612 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 38.814 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

Corporate Store Gas Use Distribution

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Internal Reference

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

External Reference

Comparison to large scale data• Industry associations• Census data

Limited by existing data sets Data by others / analysis black box Normalizing Characteristics

• Weather, Floor Area, Use, etc Type of Comparison

• Ranks / Distributions• Regressions• Standard / Best Practices

Site Electricity Use

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Northland ELLC

External ReferenceDirect Data Comparison

Comparison of residence hall to CBECS micro data

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

Limited Sample Wide Range in EUI Representative? Site Fuel Use

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External ReferenceDirect Data Comparison

Wisconsin Schools 1998 Energy Cost

847 Schools > 10,000 sq ft

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Wisconsin Schools 1998 Energy Cost

847 Schools > 10,000 sq ft

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Typical $0.60/sfGood

$0.48/sf

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

External Reference

Let others develop method Energy Star

• Multi-parameter• Representative sample of sector• Rank specific to building parameters• Source energy

External Reference

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

How to Use the Information?

Moving Toward Best Practice

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) How is it achieved?

• Look at system details• Design characteristics (changeable?)• Operational parameters (changeable?)• Management actions (changeable?)

Implementation & Feedback

Best PracticePerformance Target: Model

Daily Total Electricity Use

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Daily Average Outdoor Dry Bulb Temperature (F)

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Simulation

Using Benchmarking

Benchmarking isn’t the destination,

Just the mile marker

Benchmark only hints at potential for improvement The benchmark is a tool Still need to figure out where to go

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

Summary Effective Benchmarking

Define performance• Metrics

Define peer group• Data set

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

Benchmark only gives the score Use information

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