Steven Carlson, P.E. CDH Energy Corp. Evansville, WI ASHRAE Chicago, 2006 Energy Benchmarking.
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Transcript of Steven Carlson, P.E. CDH Energy Corp. Evansville, WI ASHRAE Chicago, 2006 Energy Benchmarking.
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, $0.622/sq ft avg.
0
50
100
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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
Energy Cost ($/sq ft)
Nu
mb
er
of
Sch
oo
ls
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 ($/sqft) Energy Use (kBtu/sqft)
• Source / Site ?• Electricity / Gas ?
Related to...• Weather, Sales (meals served, beds), service level
Desire to include multiple factors• f (floor area, hours per week, occupants, etc)
Change in Rank Order• Financial• No normalizing factors – stay the same
Scale: Whole building vs system level Often devised based on type of available data
Self Reference
Comparison to past performance• More of a diagnostic than a “Benchmark”, but valid
energy Management tool• Validate project impact• Can look at small sub-system
Monthly Electricity Use
1997 1998 1999 2000 2001 2002 2003 20040
200
400
600
800
1000
Mw
h
BasePeriod
ConstructionPeriod
Monthly Electricity Use
1997 1998 1999 2000 2001 2002 2003 20040
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400
600
800
1000
Mw
h
TotalOn-PeakOff-Peak
Ruby Isle (6373) Electricity Demand
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
2004 2005
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500
600
kW
Ruby Isle (6373) Daily Electricity Load Line
0 20 40 60 80 100Daily Average Temperature (F)
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12
Dai
ly E
lect
ricity
Use
(M
Wh)
Energy Use Change After 12-14-04
Energy Use Change After 9-16-04
Self Reference Isolated from “Best Practices” No comparison to others Only relative sense of
performance over time
Recommissioned
Internal Reference
Internal data source (small organization)• Tabular ranking for small number of buildings• Example notes A/C characteristic• Example notes electricity price variation (load factor)• Energy pricing impacts cost metrics
Fond Du Lac School District Energy Costs (1998-99) Sorted by Energy Use Intensity ($/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
0 20 40 60 80 100
Percentile
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f
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
1 5 9 13 16 20 24 28
Observation
0
5
10
15
20
25
30
35
40
kWh
/sq
ft/
yr
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
1 5 9 13 16 20 24 28
Observation
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 90 100
Percentile
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 90 100
Percentile
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
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
0 20 40 60 80 100
Daily Average Outdoor Dry Bulb Temperature (F)
0
1000
2000
3000
4000
5000
6000
7000
8000
kWh/
day
Weekday
Sunday
Saturday
Weekday
Sunday
Saturday
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