Action-Oriented Benchmarking
Using CEUS Data to Identify and Prioritize Efficiency
Opportunities in California Commercial Buildings
Paul A. Mathew, Ph.D.
Lawrence Berkeley National Laboratory
Berkeley California
Content
 Introduction to action-oriented benchmarking
 Using the CEUS database for AOB
 AOB Vignettes – schools and offices
 Limits of AOB
 Outlook
Many Applications
for Energy Benchmarking
high
Property Valuation
Rigor
Recognition programs
Set building/portfolio
targets
CCx, RCx
Identify efficiency opportunities
Set system targets
Identify “outliers”
low
Campus
Building
System
Granularity
Component
Action Oriented Benchmarking
Overall potential for building-wide energy efficiency
Site BTU/sf-yr
Ventilation
kWh/sf-yr
Potential for energy efficiency in ventilation system
Peak cfm/sf
Potential to reduce energy use through operational practices
e.g. by optimizing ventilation rates
Peak W/cfm
Potential to reduce energy use through
ventilation system efficiency improvements
Fan
efficiency
Potential to improve fan efficiency
Pressure
drop
Potential to reduce system pressure drop
Fume hood
density
Avg/Peak
cfm ratio
Cooling
BTU/sf-yr
Impact of fume hoods on ventilation energy use
Effectiveness of VAV fume hood sash management
Action-oriented Benchmarking
Complements Other Assessment Tools
Investment-Grade
Energy Audit
Whole Building
Energy Benchmarking
Action-oriented
Energy Benchmarking
Screen facilities for overall
potential
Identifies and prioritizes
specific opportunities
Estimates savings and cost
for specific opportunities
0.5-2 day FTE
2-10 day FTE
10-20 day FTE
Minimal data requirements
(utility bills, building
features)
Requires sub-metered enduse data ; may require
additional data logging
Requires detailed data
collection, cost estimation,
financial analysis
Highly applicable for RCx
and CCx
Necessary for retrofits with
capital investments
CEUS Database
 Commercial End Use Survey
– Territories: PG&E, SCE, SDG&E, SMUD
 Survey of 2800 premises
– Stratified random sampling based on utility region, climate
zone, building type, size (consumption)
– On-site survey of building characteristics, features
– Monthly utility bill data
– Short term data logging and/or interval metering at some
sites
CEUS Calibrated Simulations
 Energy intensities derived from calibrated simulations
– Simulation models generated from survey data
– Calibrated with utility data, data logging, interval metering
Calibration of CEUS sites (N=2704)
Short Term & Interval
Metering
3%
Interval Metering
13%
Short Term Metering
15%
Only Monthly Bills
69%
CEUS End Uses
 HVAC
– Space Heating
– Space Cooling
– Ventilation
 Lighting
– Interior Lighting
– Exterior Lighting
 Other
–
–
–
–
–
–
–
–
Water Heating
Office Equipment
Cooking
Miscellaneous Equipment
Refrigeration
Air Compressors
Motors (non-HVAC)
Process Equipment
Using CEUS to Infer Actions
End-Use Benchmarking
– End-Use Intensity
– End-Use Breakout
Building Features
Identify and Prioritize
Systems
Identify Potential Actions
– Presence/absence
– Component efficiency
Correlate Energy Intensities &
Building Features
Estimate Potential Savings
Whole Building Energy Intensity
 Overall Efficiency Potential
Large Office > Total Source Energy Intensity
25%
100%
Source Energy Intensity (kBTU/sf-yr)
500
460
420
380
0%
340
0%
300
20%
260
5%
220
40%
180
10%
140
60%
100
15%
60
80%
Cumulative Frequency
Your Building
202.1
20%
20
Frequency
244 datapoints
Whole Building Energy Intensity
 Overall Efficiency Potential
Large Office > Total Source Energy Intensity
244 datapoints
Your Building
202.1
5%
-
50
100
25% Median
150
75%
200
95%
250
300
50-75 %ile
75-95 %ile
Source kBTU/sf-yr
05-25 %ile
25-50 %ile
350
400
Whole Building Energy Intensity
 Overall Efficiency Potential by Vintage
Large Offices > Total Source Energy Intensity
240 datapoints
1991-2003
Vintage
1979-1990
1941-1978
1901-1940
All
-
50
100
150
200
250
300
350
Source Energy Intensity (kBTU/sf-yr)
05-25 %ile
25-50 %ile
50-75 %ile
75-95 %ile
Median
400
Whole Building Energy Intensity
 Overall Efficiency Potential by Climate
Climate
Schools > Source Energy Intensity
Southern Inland
N=37
Southern Coast
N=19
Central Valley
N=37
Central Coast
N=18
All
N=125
-
20
40
60
80
100
120
140
160
180
200
Source Energy Intensity (kBTU/sf-yr)
05-25 %ile
25-50 %ile
50-75 %ile
75-95 %ile
Median
End-Use Energy Intensity
 System Efficiency Potential
Large Office > End Use Source Energy Intensity
Your Building
32.3
244 datapoints
Office Eq
5%
25%
66.7
75%
Median
95%
Lighting
End Use
26.3
Heating
32.3
Cooling
24.3
Ventilation
-
40
20
80
60
Source Energy Intensity (kBTU/sf-yr)
05-25 %ile
25-50 %ile
50-75 %ile
75-95 %ile
100
End-Use Breakout
 System Efficiency Potential and Prioritization
Large Office > End Use Breakout
Typical
Your Building
-
50
100
150
200
250
Source Energy Intensity (kBTU/sf-yr)
Heating
Cooling
Vent
Lighting
DHW
OfficeEq
Other
End-Use Breakout
 System Efficiency Potential and Prioritization
Schools > End Use Breakout
Southern Inland
Southern Coast
Central Valley
Central Coast
All
0%
20%
40%
60%
80%
100%
% of Total Source Energy Intensity
Heating
Cooling
Vent
Lighting
DHW
OfficeEq
Other
Building Features Benchmarking
 Identify Potential Actions by Presence/Absence
Large Office > Multi-zone AHU > Temp Control Type
Aggregated by # Systems; N=178 sites, 1676 records
Time clock
4%
Manual
3%
Schools> Single-zone AHU > Temp Control Type
Aggregated by # Systems; N=125 sites, 2395 systems
Alw ays on
2%
Alw ays on
10%
EMS
24%
Prog. Tstat
5%
Manual
25%
EMS
86%
Time clock
11%
Prog. Tstat
30%
Building Features Benchmarking
 Identify Potential Actions by Presence/Absence
Large Office > Ballast Type (% of total kW for 292 sites)
Adv Elec
1%
Large Office > Lighting Control (% of total kW for 292 sites)
N/A
4%
Very Efficient
Efficient
Standard
Magnetic
4%
High Eff Mag
7%
Bi-level
5%
Timeclock
6%
Very Efficient
Efficient
Standard
Manual
45%
Motion Sensor
16%
Std Elec
84%
Other;N/A
2%
EMS
26%
Building Features Benchmarking
 Identify Potential Actions by Presence/Absence
Large Office > Multi-zone AHU > Supply Fan Motor Efficiency
Aggregated by # Motors; N=176 sites, 1911 motors
Premium
4%
Schools > Single-zone AHU > Supply Fan Motor Efficiency
Aggregated by # Motors; N=123 sites, 2490 motors
High eff
18%
Premium
0%
High eff
21%
Standard
75%
Standard
82%
Building Features Benchmarking
 Identify Potential Actions by System Efficiency
System Type
Large Office > Fan efficiency for multi-zone systems
MZ
N=25
CV
N=79
VAV
N=97
-
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Fan efficiency (hp/1000cfm)
05-25 %ile
25-50 %ile
50-75 %ile
75-95 %ile
Median
Correlating Building Features and Energy Intensity
 Estimate Potential Savings (sort of)
Large Office > Impact of Lighting Controls and Lighting Power on
Lighting Energy Intensity
14
Lighting Energy Use (kWh/sf-yr)
183 datapoints
12
Your
Building
10
R2 = 0.3275
8
Manual
R2 = 0.659
2
R = 0.6911
6
EMS
Motion Sensor
4
2
-
0.5
1.0
1.5
Lamp Power Density (W/sf)
2.0
2.5
Limits of AOB
 NOT “audit in a box”
– Only identifies potential actions from predefined list
– Only crude savings estimates (range)
 Effectiveness is driven by database density
– Many gaps in CEUS survey data
 Ability to identify actions proportional to user ability to input data
AOB helps identify potential actions and prioritize areas for
more detailed analysis and audits
Outlook
 Continued analysis of CEUS
– Opportunities and limits for AOB
 Development of action inference methodology
– Mapping list of actions to benchmarking metrics
 Prototype tool currently under development
– Extensive user surveys to determine features
– Expected April 2008
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Action-oriented energy benchmarking for non-residential buildings
Benchmark
Actions
V Building type
 All
 Small Office
 Large Office
 Restaurant
 Retail
 Food Store
 Warehouse
 School
 College
 Healthcare
 Lodging
 Public Assembly
 Laboratory
 Cleanroom
 Datacenter
 Mixed Use
 More choices...
Energy, or...
Characteristics &
Operations, or
Combinations
Indicators
Views
 Total energy
 Lighting
 Quantity
 Summary
 Electricity
 Envelope
 Cost
 End Uses
 Peak power
 Air Handling
 Emissions
 Distribution
 Fuel
 Chillers
 Thermal
 Boilers
 Hot Water
 Plug/Process
Results: Typical large office buildings use 191 kBTU/ft2-year. Enter your own building
information at the left to see how yours compares.
Large Office > Total Source Energy
244 datapoints
Your Building
202.1
> Location
> Vintage
Or, enter all information in Project Profile
5%
25%
Median
75%
95%
To Compare Your Building, Enter:
Conditioned floor area (sf): 100,000
Total energy use
1000
Electricity (MWh):
Fuel (MBTU):
100
Other (MBTU):
0
-
50
100
150
200
250
300
Source kBTU/sf-yr
05-25 %ile
25-50 %ile
50-75 %ile
75-95 %ile
This View: California > large office > total energy > all end uses > quintiles
Project Profile: Large Office, California, 100ksf, Electric+Fuel
350
400
Questions?
Paul Mathew
MS 90-3111
Lawrence Berkeley National Laboratory
1 Cyclotron Road, Berkeley CA 94720
(510) 486-5116
[email protected]