Action-Oriented Benchmarking

Paul A. Mathew, Ph.D.Lawrence Berkeley National Laboratory

Berkeley California

Using CEUS Data to Identify and Prioritize Efficiency Opportunities in California Commercial Buildings

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

Granularity

Rig

or

low

Campus Building System Component

Set building/portfolio targets

CCx, RCxIdentify efficiency opportunitiesRecognition programs

Identify “outliers”

Property Valuation

Set system targets

high

Action Oriented Benchmarking

Site BTU/sf-yr

Ventilation kWh/sf-yr

Peak cfm/sf

Peak W/cfm

Fume hood density

Avg/Peak cfm ratio

Fan efficiency

Pressure drop

Cooling BTU/sf-yr

Potential to improve fan efficiency

Potential to reduce energy use through ventilation system efficiency improvements

Potential to reduce energy use through operational practices e.g. by optimizing ventilation rates

Potential for energy efficiency in ventilation system

Overall potential for building-wide energy efficiency

Potential to reduce system pressure drop

Impact of fume hoods on ventilation energy use

Effectiveness of VAV fume hood sash management

Action-oriented Benchmarking Complements Other Assessment Tools

Whole Building

Energy BenchmarkingAction-oriented

Energy BenchmarkingInvestment-Grade

Energy Audit

Screen facilities for overall potential

0.5-2 day FTE

Minimal data requirements(utility bills, building features)

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

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

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)

Only Monthly Bills69%

Short Term & Interval Metering

3%

Interval Metering13%

Short Term Metering15%

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– Presence/absence

– Component efficiency

Correlate Energy Intensities & Building Features

Identify and Prioritize Systems

Identify Potential Actions

Estimate Potential Savings

Large Office > Total Source Energy Intensity

0%

5%

10%

15%

20%

25%

20 60 100

140

180

220

260

300

340

380

420

460

500

Source Energy Intensity (kBTU/sf-yr)

Fre

qu

en

cy

0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve

Fre

qu

en

cy

Your Building202.1

244 datapoints

Whole Building Energy Intensity Overall Efficiency Potential

Large Office > Total Source Energy Intensity

- 50 100 150 200 250 300 350 400

Source kBTU/sf-yr

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile

Median

244 datapoints Your Building202.1

25%5% 75% 95%

Whole Building Energy Intensity Overall Efficiency Potential

Large Offices > Total Source Energy Intensity

- 50 100 150 200 250 300 350 400

All

1901-1940

1941-1978

1979-1990

1991-2003

Vin

tag

e

Source Energy Intensity (kBTU/sf-yr)

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile Median

240 datapoints

Whole Building Energy Intensity Overall Efficiency Potential by Vintage

Schools > Source Energy Intensity

- 20 40 60 80 100 120 140 160 180 200

All

Central Coas t

Central Valley

Southern Coas t

Southern Inland

Clim

ate

Source Energy Intensity (kBTU/sf-yr)

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile Median

N=125

N=18

N=37

N=19

N=37

Whole Building Energy Intensity Overall Efficiency Potential by Climate

Large Office > End Use Source Energy Intensity

- 20 40 60 80 100

Ventilation

Cooling

Heating

Lighting

Office Eq

En

d U

se

Source Energy Intensity (kBTU/sf-yr)

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile

Median

Your Building32.3244 datapoints

25%5% 75% 95%66.7

26.3

32.3

24.3

End-Use Energy Intensity System Efficiency Potential

Large Office > End Use Breakout

- 50 100 150 200 250

Your Building

Typical

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

0% 20% 40% 60% 80% 100%

All

Central Coas t

Central Valley

Southern Coas t

Southern Inland

% of Total Source Energy Intensity

Heating Cooling Vent Lighting DHW OfficeEq Other

End-Use Breakout System Efficiency Potential and Prioritization

Building Features Benchmarking Identify Potential Actions by Presence/Absence

Schools> Single-zone AHU > Temp Control Type Aggregated by # Systems; N=125 sites, 2395 systems

Time clock11%

Manual25%

Alw ays on10%

Prog. Tstat30%

EMS24%

Large Office > Multi-zone AHU > Temp Control Type Aggregated by # Systems; N=178 sites, 1676 records

Time clock4%

Manual3%

Alw ays on2%

Prog. Tstat5%

EMS86%

Building Features Benchmarking Identify Potential Actions by Presence/Absence

Large Office > Ballast Type (% of total kW for 292 sites)

Std Elec84%

N/A4%

Magnetic4%

High Ef f Mag7%

Adv Elec1%

Eff icientVery Ef f icient

Standard

Large Office > Lighting Control (% of total kW for 292 sites)Other;N/A

2%Bi-level5%

Manual45%

EMS26%

Motion Sensor16%

Timeclock6%

Eff icientVery Ef f icient

Standard

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

Standard75%

High eff21%

Prem ium4%

Schools > Single-zone AHU > Supply Fan Motor Efficiency Aggregated by # Motors; N=123 sites, 2490 motors

Standard82%

High eff18%

Prem ium0%

Building Features Benchmarking Identify Potential Actions by System Efficiency

Large Office > Fan efficiency for multi-zone systems

- 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

VAV

CV

MZ

Sy

ste

m T

yp

e

Fan efficiency (hp/1000cfm)

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile Median

N=97

N=79

N=25

Correlating Building Features and Energy Intensity Estimate Potential Savings (sort of)

Large Office > Impact of Lighting Controls and Lighting Power on Lighting Energy Intensity

R2 = 0.3275

R2 = 0.659R2 = 0.6911

-

2

4

6

8

10

12

14

- 0.5 1.0 1.5 2.0 2.5

Lamp Power Density (W/sf)

Lig

hti

ng

En

erg

y U

se (

kWh

/sf-

yr)

Manual

EMS

Motion Sensor

183 datapoints

YourBuilding

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

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...

> Location> Vintage

This View: California > large office > total energy > all end uses > quintilesProject Profile: Large Office, California, 100ksf, Electric+Fuel

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

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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

Large Office > Total Source Energy

- 50 100 150 200 250 300 350 400

Source kBTU/sf-yr

05-25 %ile 25-50 %ile 50-75 %ile 75-95 %ile

Median

244 datapoints Your Building202.1

25%5% 75% 95%

To Compare Your Building, Enter:

Conditioned floor area (sf):Total energy useElectricity (MWh):Fuel (MBTU):Other (MBTU):

100,000

1000

100

0

Benchmark Actions

Questions?

Paul MathewMS 90-3111Lawrence Berkeley National Laboratory1 Cyclotron Road, Berkeley CA 94720

(510) 486-5116 pamathew@lbl.gov