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Action-Oriented Benchmarking

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Title: Action-Oriented Benchmarking


1
Action-Oriented Benchmarking
Using CEUS Data to Identify and Prioritize
Efficiency Opportunities in California Commercial
Buildings
  • Paul A. Mathew, Ph.D.
  • Lawrence Berkeley National LaboratoryBerkeley
    California

2
Content
  • Introduction to action-oriented benchmarking
  • Using the CEUS database for AOB
  • AOB Vignettes schools and offices
  • Limits of AOB
  • Outlook

3
Many Applications for Energy Benchmarking
high
Property Valuation
CCx, RCxIdentify efficiency opportunities
Recognition programs
Rigor
Set building/portfolio targets
Set system targets
Identify outliers
low
Campus
Building
System
Component
Granularity
4
Action Oriented Benchmarking
Site BTU/sf-yr
Ventilation kWh/sf-yr
Peak cfm/sf
Peak W/cfm
Fan efficiency
Pressure drop
Fume hood density
Avg/Peak cfm ratio
Cooling BTU/sf-yr
5
Action-oriented Benchmarking Complements Other
Assessment Tools
Investment-GradeEnergy Audit
Action-oriented Energy Benchmarking
Whole Building Energy Benchmarking
Identifies and prioritizes specific
opportunities 2-10 day FTE Requires sub-metered
end-use data may require additional data
logging Highly applicable for RCx and CCx
  • Screen facilities for overall potential
  • 0.5-2 day FTE
  • Minimal data requirements
  • (utility bills, building features)

Estimates savings and cost for specific
opportunities 10-20 day FTE Requires detailed
data collection, cost estimation, financial
analysis Necessary for retrofits with capital
investments
6
CEUS Database
  • Commercial End Use Survey
  • Territories PGE, SCE, SDGE, 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

7
(No Transcript)
8
CEUS Calibrated Simulations
  • Energy intensities derived from calibrated
    simulations
  • Simulation models generated from survey data
  • Calibrated with utility data, data logging,
    interval metering

9
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

10
Using CEUS to Infer Actions
  • End-Use Benchmarking
  • End-Use Intensity
  • End-Use Breakout
  • Building Features
  • Presence/absence
  • Component efficiency
  • Correlate Energy Intensities Building Features
  • Identify and Prioritize Systems
  • Identify Potential Actions
  • Estimate Potential Savings

11
Whole Building Energy Intensity ? Overall
Efficiency Potential
12
Whole Building Energy Intensity ? Overall
Efficiency Potential
13
Whole Building Energy Intensity ? Overall
Efficiency Potential by Vintage
14
Whole Building Energy Intensity ? Overall
Efficiency Potential by Climate
15
End-Use Energy Intensity ? System Efficiency
Potential
16
End-Use Breakout ? System Efficiency Potential
and Prioritization
17
End-Use Breakout ? System Efficiency Potential
and Prioritization
18
Building Features Benchmarking ? Identify
Potential Actions by Presence/Absence
19
Building Features Benchmarking ? Identify
Potential Actions by Presence/Absence
20
Building Features Benchmarking ? Identify
Potential Actions by Presence/Absence
21
Building Features Benchmarking? Identify
Potential Actions by System Efficiency
22
Correlating Building Features and Energy
Intensity ? Estimate Potential Savings (sort of)
23
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

24
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

25
About How to use My IQHelp Center
Privacy Export Import Sign in/out
Energy IQAction-oriented energy benchmarking for
non-residential buildings
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
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...

Results Typical large office buildings use 191
kBTU/ft2-year. Enter your own building
information at the left to see how yours compares.
Or, enter all information in Project Profile
  • To Compare Your Building, Enter
  • Conditioned floor area (sf)
  • Total energy use
  • Electricity (MWh)
  • Fuel (MBTU)
  • Other (MBTU)

100,000
1000
100
This View California gt large office gt total
energy gt all end uses gt quintilesProject
Profile Large Office, California, 100ksf,
ElectricFuel
0
26
Questions?
  • Paul MathewMS 90-3111Lawrence Berkeley
    National Laboratory1 Cyclotron Road, Berkeley CA
    94720(510) 486-5116 pamathew_at_lbl.gov
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