Premium Ventilation Proof of Concept Field TestReid Hart, PEAssociate Director, Technical Research

NPCC Regional Technical Forum October 13th, 2009

2

Premium Ventilation Package SpecificationsAnalysisField Test Results

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Beyond tune-ups & EER:Premium Ventilation Package

Specifications:Start with a Premium Economizer

• Integrated with high or differential changeover

Upgrade the Economizer module with • DCV capable economizer control module• CO2 Sensor; Min air setting improved

Add variable speed fan motor control• Full (or near full) speed in heating or cooling• Low (40%) speed in idle mode

Field testing currently underway in Oregon

4

AcknowlegementsPrimary funding: Bonneville Power Administration

• Jack Callahan• Mira Vowles

With Support by: Eugene Water & Electric Board

• Will Price• Chris Wolgamott

Energy Trust of Oregon• Phil Degens• Nick O’Neil

NPCC Regional Technical Forum RTUG

5

Single Phase Fan Speed Control forNew Units or Retrofit

Retrofit VSD OptionsTwo manufacturers now available ~ $250

Both use temperature control strategyField testing discoveries:Functioned wellIncompatible with “start-capacitor” motors

6

Better Acceptance Testing Needed

Current acceptance testing is basically a sign-offCalifornia title 24 formsEWEB Premium Economizer checklist

Improved forms will require field found values to be enteredDesigned to walk the contractor through the checkout and setup process

Draft forms included in the reportNeed field testing during pilotNeed streamlining

Solid state controllers require a voltage generator during setup to properly adjust DCV setpointsMatching sensor range and output to the controller is non-trivial

7

Significant Ventilation

ImprovementsFan is in auto positionDuring January, little daytime operationCO2 concentration almost triple desired

After retrofit, highest CO2 concentration within target limits.Single return air sensor worked for multiple rooms:Computer labBilliard room

AC-1 Pre-Retrofit Poor Ventilation

300

600900

1,2001,500

1,8002,1002,400

2,700

12:00 PM1:00 PM

2:00 PM3:00 PM

4:00 PM5:00 PM

6:00 PM

Wed, Jan 14, 2009

PPM

Car

bon

Diox

ide

Prox

y fo

r Air

Qua

lity

Fan

on

Fan On CO2 outside CO2 RA

CO2 Space Far - Adj CO2 Space Near CO2 Target

Near & Far sensors in same space (Far adjusted)

Note: Fan is off most of the day because heat from occupants met heating load

AC-1 Multiple Room CO2 Measurement Divergance

300

500

700

900

1,100

1,300

9:00 AM

10:00 AM

11:00 AM

12:00 PM

Wed, May 27, 2009

PPM

Car

bon

Diox

ide

Prox

y fo

r Ind

oor A

ir Q

ualit

y

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Fan

Amps

Fan Amps CO2 Return Air CO2 Computer Rm adj

CO2 Billiard Rm CO2 outside CO2 Target

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Single CO2 Sensor Does It

• Easiest placement of the sensor is in the return air

Concerns:• Studies suggest putting

sensor in the breathing zone (California T24 code required)

• When multiple rooms are served by one unit, an imbalance in ventilation quality may occur with uneven occupancy

• With VSD, less air throw at the diffuser may reduce ventilation effectiveness.

Even with one room heavily occupied, return air sensing can meet requirements with a slight ventilation setpoint adjustment (900 ppm)

AC-1 Multiple Room CO2 Measurement Divergance

300

500

700

900

1,100

1:00 PM2:00 PM

3:00 PM4:00 PM

Mon, Jun 15, 2009

PPM

Car

bon

Diox

ide

Prox

y fo

r Ind

oor A

ir Q

ualit

y

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Fan

Amps

Fan Amps CO2 Return Air CO2 Computer Rm - Adj

CO2 Billiard Rm CO2 outside CO2 Target

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Premium Ventilation:Significant RTU Savings

Premium economizer savings, plusFan savings when not heating or coolingReduced ventilation when not occupied

Estimated HVAC savings: 25% - 45%Compare savings in Sacramento, CA:SEER 13 to 15: 0.22 kWh/sfPremium Ventilation: 2.0 kWh/sf

Limited field testing:One unit with adequate data shows double the savingsWaiting for post heating data to analyze others

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Premium Ventilation Package Regional HVAC Savings

Hart, et al. ACEEE, 2008

0

2,000

4,000

6,000

8,000

10,000

12,000

Phoenix AZ

Sa c'to CA

Eugene OR

Boise ID

Burl'ton VT

Chica go IL

M em phis TN

Houston TX

HVA

C k

Wh

per

1000

squa

re fe

et

Ba se Tota l HVAC

Tota l ECM Sa vings

Rem a ining Fa n & Aux

Rem a ining Cooling

Rem a ining Hea ting

Eva pora tive Pre-cool

Fa n VSD Idle

Dem a nd Ventila tion

Integra ted Econom izer

Ventila tion Wa rm up

Strip Hea t lockout

Optim um Sta rt

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Modeling Methods ReviewedFour IPMVP “B” methods based on equipment electric measurement (vs. whole building)

• Inverse model or change-point analysis• Hourly OAT average vs energy use• Daily OAT average vs energy use (NBI Proposed)• Multi-variable linear regression

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Inverse or Change Point ModelSupported by ASHRAE Research

• Works with full data set rather than averages• 3-point model made the most sense without post heating data

• 5-point model expected to be more accurate once post heating data available• Full year pre and post data recommended

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Hourly Average by OAT BinHourly provides better overlap of pre and post data than daily

• Does not separate occupied vs. unoccupied; • Will be separated in analysis after post-heating data is in• Sufficient post heating data to establish conservative estimate of post

heating model (unlike daily average)

• This unit shown has more than triple projected savings

• Additional savings from ◦ correcting

improper unoccupied heat setting &

◦ achieving unintentional night flush effect

HP-4 Averaged Hourly Use per Bin Temperature

y = -88.306x + 4588R2 = 0.7448

y = 270.11x - 13037R2 = 0.9083

y = 54.736x - 2788.4R2 = 0.7572

-

2,000

4,000

6,000

8,000

10,000

0 20 40 60 80 100 120

Outside Temp, F (rooftop); Balance Point 47F

Aver

age

Wat

t Hou

rs

Heat Pre

Cool Pre

Heat Post

Cool Post

Post Heat Trend

Linear (Heat Pre)

Linear (Cool Pre)

Linear (Cool Post)

Post Heat Trend (imputed): Lacking post heating data; Post heating taken as same slope as pre with intercept offset as observed near balance point from post data

y = -88.306x + 3993

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Daily Energy Signature

Heating separated from coolingNot enough post heating data

R2 look good, as they often will for highly averaged dataShows significant Savings

Current NBI Suggested Regional Approach – RTF/BPA buy in

HP-4 Daily Energy Signature

y = -1610.2x + 92541R2 = 0.5107

y = 2785.7x - 106048R2 = 0.4159

y = 1162x - 57944R2 = 0.8263

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 10 20 30 40 50 60 70 80 90

Average Daily Outside Temperature, degF

Wat

t-Hou

rs p

er D

ay

Heat Pre

Cool Pre

Heat Post

Cool Post

Linear (Heat Pre)

Linear (Cool Pre)

Linear (Cool Post)

Balance point selected at 44F to maximize pre R2. Data for post heating not yet available.

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Multi-Variable Regression

Select VariablesBoth hourly and multi-day OAT

Co-determination shows importanceECM & Occup“Heating” indicator captures change point

Coefficients move as expectedInteractive variables move R2 from 0.19 to 0.45MV regressions are difficult to visualize

1.1E-13710.6%91.57Interactive impact of Occupied*OATOcc*OAT

6.0E-810.2%-133.83Interactive impact of Heat*OATHeat*OAT

3.2E-193.3%-50.73Interactive impact of ECM*OATECM*OAT

3.1E-550.3%-1618.97Interactive impact of ECM*OccupiedECM*Occ

1.1E-030.2%-929.74Interactive impact of ECM*HeatECM*Heat

2.9E-850.8%6182.69510.59

A categorical variable 0 when outside air is above an assumed balance point and 1 when below. The balance point can be taken from a review of binned baseline results, although that is not strictly independent. The seasonal winter average temperature is a good proxy and is strictly independent

Heating

6.4E-070.1%28.4920.53The outside air temperature for the hour

OAT (Avg Hr)

1.6E-421.8%74.0175.35

The moving average of the outside temperature for the previous 5 days to give the impact of season on the building similar to the daily average approach

OAT Seasonal

1.5E-1077.9%-3569.57576.77

A categorical variable: 0 for unoccupied periods and 1 for occupied periods based on a general schedule

Occupied

1.9E-165.1%2434.48-807.16A categorical variable 0 for the pre condition and 1 for the post condition after retrofit

ECM

6.7E-39-4371.17-3678.79Intercept

interactiveseparate

Interactive P-value

Codeter-mination

with Watt-hours

CoefficientsExplanationIndependent Variable

1.1E-13710.6%91.57Interactive impact of Occupied*OATOcc*OAT

6.0E-810.2%-133.83Interactive impact of Heat*OATHeat*OAT

3.2E-193.3%-50.73Interactive impact of ECM*OATECM*OAT

3.1E-550.3%-1618.97Interactive impact of ECM*OccupiedECM*Occ

1.1E-030.2%-929.74Interactive impact of ECM*HeatECM*Heat

2.9E-850.8%6182.69510.59

A categorical variable 0 when outside air is above an assumed balance point and 1 when below. The balance point can be taken from a review of binned baseline results, although that is not strictly independent. The seasonal winter average temperature is a good proxy and is strictly independent

Heating

6.4E-070.1%28.4920.53The outside air temperature for the hour

OAT (Avg Hr)

1.6E-421.8%74.0175.35

The moving average of the outside temperature for the previous 5 days to give the impact of season on the building similar to the daily average approach

OAT Seasonal

1.5E-1077.9%-3569.57576.77

A categorical variable: 0 for unoccupied periods and 1 for occupied periods based on a general schedule

Occupied

1.9E-165.1%2434.48-807.16A categorical variable 0 for the pre condition and 1 for the post condition after retrofit

ECM

6.7E-39-4371.17-3678.79Intercept

interactiveseparate

Interactive P-value

Codeter-mination

with Watt-hours

CoefficientsExplanationIndependent Variable

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The Importance of Occupancy

Average hourly data (HOD or hour of day) for pre and post period show distinct occupied & unoccupied energy usesRequires modal (heat vs. cooling) analysis – more data points collectedWhat else is surprising?

HP-4 Pre vs. Post Hourly Energy Use

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Ave

rage

Mod

al W

att-

hour

Use

Heat Pre

Cool Pre

Heat Post

Cool Post

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Comparison of 4 Models

Period resultsAnnualized results2200 to 3600 kWh/ton

Original projections470 to 900 kWh/ton

This particular unit had other savings contributors:Corrected daily heating surge at beginning of unoccupied periodContinuous fan operation provided unwitting night flush effect in post period

ConclusionsBoth signature approaches & change point line up fairly well with monitoredAfter post-heating data is in, will run 2 units with occupied/unoccupied split

 Monitored Period

(kWh) TMY Annual Projection (kWh)

Method Pre Post Pre Post SavingsAdjusted Savings**

Actual Monitoring 3,201 711            

Day Signature* 3,201 709 17,072 2,434 14,638 86% 12,152 71%

Hour Signature 2,948 761 20,570 4,336 16,234 79% 13,477 66%

ChangePoint (3) 3,166 711 21,492 3,850 17,642 82% 14,645 68%

MV Regression     16,224 7,410 8,814 54% 8,814 54%

*Day signature did not have post heating use calculated        

**First 3 models are adjusted since weekends and holidays were not included in base data

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Programs for the Brave – Next StepsGo Digital ControlsLab-testing based RTU expected value savings Evaluated Field Pilot

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Its Time to Move from “Solid State” to Stand Alone Direct Digital Controllers

Its time to let the low cost, 35 year old solid-state economizer controllers go.Just too many wires up on the roof.

A combined programmable thermostat with BACnet DDC controller is now here at a reasonable price from multiple manufacturers.

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DCV Integrated Fan Control (DCV-IFC)

Yes, 62.1 allows fan cycling• General thinking: commercial

fans must be ON during the occupied period

• Section 6.2.6.2 of ASHRAE Standard 62.1-2007 allows short-term interruption of ventilation if ventilation levels are maintained on average

• Continuous ventilation requires the fan switch to be “ON” during the occupied modes.

• Studies found close to 40% of thermostats have fan switch in the “Auto” mode resulting in intermittent fan operation and no ventilation control

Advantages of DCV-IFC• Interface with any staged

rooftop unit with an economizer,• With the fan off when not

needed less damper leakage • Greater savings than VSD

power reduction at low speed• Lower cost as no VSDs and

associated wiring or motor upgrades are required.

• Higher reliability, as electronic (solid-state) controls are replaced with digital logic.

• Ventilation monitored and controlled

• Duty cycling would circulate air at least every 30 minutes

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The Retrofit is Uniform; Savings is Not

HVAC savings are not uniform like lighting

If analyze sensitivity of savings to baseline parameter variation:Multiple load impactsVentilation impactFan operation impact

Need to account for variation to find population savings for program

Impact of Baseline on kWh/sf SavingsHeat Pump Heating

0

0.5

1

1.5

2

2.5

LPD/Density EconoChangeover

Glazing VentilationMinimum

Econo Max Combined

Parameter

kWh

/ squ

are

foot

22

Program-wide Savings DistributionThe histogram of probability for different savings results based on the full range of baseline influences and combinations:

There is a VERY WIDE spread of savings results

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Field Testing for Measure Savings?

Load based lab testing may be the answerIn a few days in the lab:

• Dial in a range of weather conditions• Dial in a range of load conditions• Extrapolate results to different climates

Breaking new ground • Part of a current PIER RTU proposal• Current ASHRAE research request to

develop load based lab method• Next phase for premium ventilation

When the range of results is wide, to field verify program savings, either:• Field test every installation, or• Field test a very large sample in each climate

Probably difficult to justify the cost of either field approach

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Expected Value Savings Approach

• Lab test to develop range of climate & load results

• Parametric simulation and expected value analysis to find programmatic retrofit savings

• Single expected value savings for contractor delivery

• Simulation finds sensitive parameters• Field test to verify proof of concept

Impact of Baseline on kWh/sf SavingsHeat Pump Heating

0

0.5

1

1.5

2

2.5

LPD/Density EconoChangeover

Glazing VentilationMinimum

Econo Max Combined

Parameter

kWh

/ squ

are

foot

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Next StepsWrap up premium ventilation long term report

• Complete heating savings analysis

Field proof of concept testing of DCV-IFC• Upgrade to Digital Controls• Two flavors of premium ventilation

◦ Variable speed drive during idle◦ DCV based fan cycling

Phase 2: Expected Value Savings Development• Simulation to pin down parameter sensitivities• Limited field testing to verify functionality• Lab testing to cover range of loads and weather• Expected value based projection of programmatic savings

Phase 3: Program Pilots with Evaluation – Next Summer

Reid Hart, PEAssociate Director, Technical Research

503-961-6142rhart@peci.org