evaluation of current rating method, ieer metric for...

Evaluation of current rating method, IEER metric for representativeness

October 15‐16, 2018DOE Variable Refrigerant Flow Multi‐Split Air Conditioners and Heat Pumps Working Group

1Photo credit: Samsung

Why are the CA IOUs here?

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Manufacturers Consumers Electric utilities

Federal appliance standards impact three stakeholders:

Unrepresentative equipment ratings hurt our ability to cost‐effectively serve our customers:• Give us inaccurate information on which to base major capital investment

decisions i.e. power plant construction driven by peak demand• Limit our ability to use most economically efficient path to meeting consumer

demand

Performed sensitivity analysis of IEER metric

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We will present two analyses

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Evaluated system rating, published

performance data for representativeness

Field data

Laboratory measurements

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Note: Laboratory investigation focused on cooling performance

We evaluated a VRF‐HR system in the field

• Location: Davis, California (DOE climate zone: 3)• Outdoor unit:

– 10‐ton,120 kBtu/hr condensing unit with heat recovery (simultaneous heating/cooling)

– Two inverter‐driven, direct flash injected scroll compressors with soft‐start capability

– Scrolls have an asymmetrical design with rotating compressor operation

– Refrigerant flow is controlled by electronic expansion valves throughout the system

– EER: 13.1, IEER: 28.7 (Non‐ducted configuration)

• Indoor units:– Thirteen three‐speed cassette fans (high, medium,

low) with capacity– Capacity ratio: 127.5%

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Three takeaways from field study

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Steady performance

Rated EER: 13.10

Three takeaways from field study:1. Rated EER represents

average performance2. Performance drops off at

high and low outside air temperature

3. Rated IEER is not representative of most measurements

Rated IEER: 28.7

Next: explored system performance at ATS lab

• Multidisciplinary team of over 120 engineers, scientists, and technicians

• Providing technology‐based, innovative, high‐value services to the company for over 40 years

• Clients include:

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Indoor room Outdoor room

Leveraged Internet of Things technology to measure actual performance

Internet of Things (IoT) is defined as the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect, collect and exchange data.

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“Dynamic” test approach defined as measuring equipment lab performance when the equipment operates with “as shipped and installed” controls

Compared four sets of VRF performance data

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Manufacturer ratings Dynamic full‐load Dynamic building‐load‐based1 2

Two load profiles investigated:

Field‐measured data

EnergyPlus simulation

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Manufacturer ATS‐Dyn‐Full ATS‐Field ATS‐SimData label:

EER appears to be more representative of performance than IEER

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~ 50% ratingNon‐ducted EER95 rating: 11.8

For every temperature examined, manufacturer‐published performance data suggested EER ~2x

higher than other approaches

Non‐ducted IEER rating: 24.3

Field Sim Dyn‐Full

Results of ATS and EPRI evaluations were consistent

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ATS dynamic full‐load test HT.10.SCE.250 Three pipe system – cooling

Indoor condition: 80F DBT/67F WBTCapacity ratio: 125%

In two VRF systems from different manufacturers, ATS and EPRI observed much lower EERs than

published by manufacturer

Tested VRF system does not operate at fixed speed

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System capacity cycled to maintain an indoor

temperature setpoint within the factory default dead‐band

of ~+/‐ 2F

BL – Building load; OA – Outside air; RA: Return air;

Tdb – Dry bulb temperature; Twb – Wet bulb temperature

Indoor temperature: 80F DB, 67F WBAverage EER: 6.57 Estimated EER based on manufacturer data: 14.39

Degradation due to cycling is much larger than assumed as shown in the gap between average and OEM estimated

EER

Manufacturer total capacity: 145 MBH

At low temperatures, tested VRF system cycles

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At low loads, the system behaves like a system with constant capacity by cycling on and off to maintain

setpoint

BL – Building load; OA – Outside air; RA: Return air;

Tdb – Dry bulb temperature; Twb – Wet bulb temperature

Indoor temperature: 80F DB, 67F WBAverage EER: 8.09Estimated EER based on manufacturer data: 23.6

Manufacturer total capacity: 150 MBH

Dynamic tests easier to set up, run vs. fixed‐speed tests

• We were unable to directly compare fixed‐compressor speed test method vs. dynamic test methods– Fixed‐compressor speed test method requires locking the system at four different speeds to measure part‐

load EERs for evaluation of IEER– Written instructions and proprietary equipment from the manufacturer are required to override the “as

shipped” controls in order to lock the system at different speeds– We were unable to execute the required NDA

• Based on experience testing residential mini‐splits using fixed‐speed and dynamic methods, we observed: – Dynamic tests were easier to set up than fixed‐speed test procedures which require legal support to gain

NDA and technician intervention / fine tuning of system– Dynamic full‐load test takes approximately the same amount of time as the fixed‐compressor speed test per

test condition– Dynamic test conditions can be scripted into laboratory operating software and run without continuous

supervision. In contrast, locking out system controls requires intervention by the manufacturer’s technician at each test point

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We expect test burden for dynamic full‐load test to be less than for current approach

Dynamic full‐load test takes ~10h to gather data for 7 conditions

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105F

95F

85F81.5F

75F

68F65F

Power (kW) 14.63 14.44 14.89 14.37 14.55 14.38 14.75

EER 7.16 8.05 8.92 9.39 9.71 10.04 9.84

• With dynamic full‐load test, system converges quickly resulting in similar test lengths as AHRI 1230

• Additional benefit: Dynamic test conditions can be scripted into laboratory operating software and run without continuous supervision

Lab and field data suggest EER95 as most representative rating option

• Lab and field data demonstrate:1. EER95 represents average

performance2. Performance drops off at high and

low outside air temperature3. Rated IEER is NOT representative

• Best option for fixed capacity rating of VRF equipment appears to be EER95

• Considerations:– Industry is generally moving

towards IEER using fixed capacity EER

– IEER from EER dynamic testing data will be more representative

– Is IEER the ”right” metric for RTUs with variable‐speed compressors?

– What are the implications for different metrics for different HVAC categories?

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We see two options to move forward

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Preferred option Back‐up option

Test procedure Adopt dynamic full‐load test

Current method based on fixed speed testing

Metric Updated IEER EER95

We recognize that our preferred option is a major change. Options to smooth transition include:• Delay finalization of test method• DOE’s technical team works stakeholders to assess the dynamic full‐load test method• Manufacturers evaluate their equipment with the dynamic full‐load test• Any other options?

Note: EPRI and ATS teams are available to respond to any questions, and would welcome the opportunity to demonstrate the dynamic full‐load test for DOE’s technical team


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Next we will present a sensitivity analysis of IEER metric

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Field data


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Note: The IEER metric can use EER from either fixed‐compressor‐speed or dynamic testing. Thus

the testing decision is the first priority.


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Next we will present a sensitivity analysis of IEER metric

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Field data


1


Note: The IEER metric can use EER from either fixed‐compressor‐speed or dynamic testing. Thus

the testing decision is the first priority.

Current IEER Calculation Method

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• 6.2 Part‐Load Ratings.. All units rated in accordance with the standard shall include an Integrated Part Load Energy Efficiency Ratio (IEER), even if they only have one stage of capacity control.

• 6.2.1 General. The IEER is intended to be a measure of merit for the part load performance of the unit. Each building may have different part load performance due to local occupancy schedules, building construction, building location and ventilation requirements. For specific building energy analysis an hour‐by‐hour analysis program should be used.

• 6.2.2 Integrated Energy Efficiency Ratio (IEER). For equipment covered by this standard, the IEER shall be calculated using test derived data and the following formula.

IEER = 0.020*A + 0.617*B + 0.238*C + 0.125*D• Bin A EER at 100% net capacity at design conditions Loads 97% to 100%

• Bin B EER at 75% net capacity and reduced ambient Loads 97% to 62.5%

• Bin C EER at 50% net capacity and reduced ambient Loads 62.5% to 37.5%

• Bin D EER at 25% net capacity and reduced ambient Loads 0% to 37.5%

History of the IEER Weighting Factors (as we understand it)

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Building Typologies Selected

Benchmark Cities Selected

HVAC with/without Economizers

Weighting by building type

Weighting by volume of units regionally

Office, Retail, School

15 typical US Cities

Per ASHRAE 90.1 at the time (90.1 2004?)Analysis for both air and water cooled conditions.Selected Air Cooled #s.Selected mechanical cooling hours.

40% Office, 30% Retail, 30% School

By City (source unknown)

Miami FL

Houston, TX

Phoenix, AZ

San Francisco CA

Baltimore MD

Salem, OR

Chicago, ILBoise, ID

Burlington, VT

Helena MTDuluth, MN

El Paso, TX

Albuquerque, NM

Memphis TN

Zone % Volume1a 1.18%2a 8.84%2b 3.88%3a 8.74%3b 8.32%3c 8.68%4a 13.67%4b 1.44%4c 2.15%5a 21.08%5b 5.29%6a 10.43%6b 2.54%7 2.33%8 1.42%

Developed for AHRI Standard 340/360

History of the IEER Weighting Factors (as we understand it)

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IEER = 0.020*A + 0.617*B + 0.238*C + 0.125*DAir Cooled System Factor Results: Only with Mechanical Cooling Hours

volume %

A B C D A B C D A B C D A B C D total1a Miami 1.181 no 0.008 0.914 0.072 0.007 0.009 0.798 0.182 0.011 0.007 0.806 0.179 0.008 0.008 0.847 0.137 0.008 1.0002a Houston 8.838 no 0.016 0.739 0.193 0.051 0.020 0.687 0.180 0.113 0.013 0.668 0.258 0.062 0.016 0.702 0.209 0.073 1.0002b Phoenix 3.876 yes 0.007 0.750 0.187 0.056 0.007 0.646 0.224 0.123 0.005 0.693 0.268 0.033 0.006 0.702 0.222 0.069 1.0003a Memphis 8.738 no 0.080 0.590 0.216 0.114 0.090 0.585 0.245 0.079 0.087 0.569 0.248 0.097 0.085 0.582 0.234 0.099 1.0003b El Paso 8.321 yes 0.021 0.724 0.157 0.097 0.021 0.758 0.158 0.064 0.028 0.796 0.126 0.051 0.023 0.756 0.148 0.073 1.0003c San Francisco 8.678 yes 0.005 0.196 0.272 0.527 0.008 0.279 0.352 0.362 0.005 0.232 0.318 0.445 0.006 0.232 0.310 0.453 1.0004a Baltimore 13.671 no 0.003 0.596 0.223 0.177 0.005 0.543 0.273 0.179 0.003 0.522 0.342 0.134 0.004 0.558 0.274 0.165 1.0004b Albuquerque 1.442 yes 0.008 0.703 0.171 0.118 0.010 0.563 0.353 0.074 0.006 0.574 0.351 0.070 0.008 0.622 0.280 0.091 1.0004c Salem 2.153 yes 0.013 0.495 0.279 0.213 0.018 0.557 0.297 0.127 0.011 0.547 0.283 0.159 0.014 0.529 0.286 0.171 1.0005a Chicago 21.081 yes 0.008 0.790 0.117 0.085 0.051 0.588 0.314 0.047 0.007 0.647 0.299 0.047 0.021 0.686 0.231 0.062 1.0005b Boise 5.294 yes 0.009 0.685 0.256 0.050 0.011 0.703 0.199 0.088 0.008 0.729 0.175 0.087 0.009 0.703 0.215 0.072 1.0006a Burlington 10.434 yes 0.018 0.747 0.151 0.083 0.023 0.624 0.179 0.174 0.025 0.640 0.296 0.039 0.022 0.678 0.203 0.097 1.0006b Helena 2.541 yes 0.007 0.587 0.337 0.069 0.007 0.444 0.400 0.149 0.006 0.498 0.368 0.128 0.007 0.517 0.365 0.111 1.0007 Duluth 2.334 yes 0.013 0.714 0.186 0.086 0.016 0.559 0.221 0.204 0.011 0.496 0.444 0.049 0.013 0.602 0.274 0.110 1.0008 Fairbanks 1.420 yes 0.007 0.531 0.286 0.177 0.010 0.293 0.600 0.097 0.007 0.637 0.269 0.087 0.008 0.491 0.375 0.126 1.000

0.0202 0.6166 0.2381 0.1250 1.0000

Weighted Average30% 30%

USA weighted average

Weighting FactorsZone City Econo

40%Office School Retail

volume %

A B C D A B C D A B C D A B C D1a Miami 1.181 no 92.55 80.25 64.56 49.72 92.55 81.98 70.18 54.41 92.55 81.67 70.66 54.99 92.550 81.194 68.075 52.7092a Houston 8.838 no 97.55 81.84 63.02 46.67 97.55 83.84 70.21 56.74 97.55 83.78 68.26 52.46 97.550 83.024 66.748 51.4282b Phoenix 3.876 yes 112.55 92.34 74.84 67.09 112.55 94.69 80.38 71.05 112.55 95.35 77.77 67.55 112.550 93.947 77.380 68.4173a Memphis 8.738 no 92.55 80.13 62.42 44.16 92.55 82.84 67.79 52.72 92.55 82.84 67.79 50.18 92.550 81.754 65.642 48.5333b El Paso 8.321 yes 97.55 83.77 72.55 66.47 97.55 83.57 72.55 67.55 97.55 84.02 72.55 67.35 97.550 83.783 72.550 67.0563c San Francisco 8.678 yes 92.55 80.59 72.55 65.28 92.55 80.59 72.55 54.49 92.55 80.30 72.55 66.31 92.550 80.505 72.550 62.3554a Baltimore 13.671 no 97.55 79.75 62.95 42.88 97.55 82.83 70.44 54.49 97.55 82.62 68.35 49.50 97.550 81.533 66.814 48.3484b Albuquerque 1.442 yes 102.55 83.94 72.55 66.62 102.55 86.48 74.98 67.55 102.55 86.44 75.24 67.55 102.550 85.453 74.085 67.1784c Salem 2.153 yes 97.55 81.69 72.55 65.96 97.55 81.63 72.55 67.55 97.55 81.48 72.55 67.28 97.550 81.611 72.550 66.8315a Chicago 21.081 yes 97.55 79.55 67.55 61.93 93.36 81.79 70.61 62.55 97.55 82.62 70.34 62.55 96.292 81.144 69.305 62.3025b Boise 5.294 yes 97.55 80.13 65.08 57.55 97.55 83.11 72.55 67.55 97.55 82.62 72.55 67.31 97.550 81.771 69.562 63.4796a Burlington 10.434 yes 87.55 73.53 62.55 57.29 87.55 75.62 67.55 61.21 87.55 75.97 65.29 57.55 87.550 74.889 64.873 58.5436b Helena 2.541 yes 97.55 78.55 65.26 57.55 97.55 81.98 69.79 61.69 97.55 82.02 69.91 61.27 97.550 80.618 68.013 59.9087 Duluth 2.334 yes 87.55 73.01 62.55 57.05 87.55 75.94 67.55 61.46 87.55 75.82 65.26 57.55 87.550 74.731 64.864 58.5218 Fairbanks 1.420 yes 82.55 70.38 62.55 56.92 82.55 73.72 64.79 57.55 82.55 70.55 62.55 57.55 82.550 71.434 63.223 57.298

95.519 81.229 68.736 58.372USA weighted average

Zone City Econo

40% 30% 30%Weighted AverageSchool Retail

Mean Ambient TemperaturesOffice

Air Cooled System Bin Average OAT Values

Current IEER coefficients

Case 1 of IEER Coefficient Development Process

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Building Typologies Selected

Benchmark Cities Selected

HVAC with/without Economizers

Weighting by building type

Weighting by volume of units regionally

Office, Retail, School


Per ASHRAE 90.1 at the time (90.1 2004?)

40% Office, 30% Retail, 30% School

By City (source unknown)

ASHRAE 90.1‐2013 Prototype models:Medium Office, Standalone Retail, Primary SchoolMost states use 90.1 2010 or above.


Per ASHRAE 90.1‐2013Loads for cooling include cooling for ventilation.

Note: Red font indicates modifications to original 2007 analysis

90.1 building code adoption by state

90.1‐2007 or betterSource: www.energycodes.gov

Findings: Case 1 IEER Factors, Binning by Frequency

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National Average

Weights based on frequency of hours/total mechanical hours for each bin.

Current IEER Calculation Method• Bin A 97% to 100% Design Bin (100%)• Bin B 97% to 62.5% Peak Bin (75%)• Bin C 62.5% to 37.5% Low Bin (50%)• Bin D 0% to 37.5% Min Bin (25%)

1. Updated analysis does not match current IEER weighting.2. This method considers each hour as equal in value and

ignores the total load /year.From 7 field sites of VRF installations NEEA has studied: “the systems almost never operate at full capacity, and only rarely operate at anything close to 75% capacity.”

Case 1 would result in 22% increase in IEER based on manufacturer data

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Load (%) Manufacturer ATS – Field & ATS – Dyn Full

100% 14.39 6.5875% 17.00 8.4950% 23.00 10.0025% 26.00 10.00

To rationalize how these weighting factors may impact actual rating for equipment, two examples were used in IEER calculations:

1. A manufacturer‐defined set of EER values corresponding to ABCD conditions.

2. Field testing conducted by PG&E.

Data source Current Weighting

Case 1 Weighting Percent Change

Manufacturer 19.50 23.77 22%

ATS ‐ Field 9.00 9.73 8%A B C D

IEER Current 0.0202 0.6166 0.2381 0.1250

Case 1 0.0029 0.1706 0.2195 0.6070

Field Sim Dyn‐Full1 2

Weightings compared:

Test data used:

Finding:

Recommended approach of binning by ton‐hours results in almost no change in IEER

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The same bins are used based on 0 to 100% and the sum of the load at each hour by bin are normalized by total annual ton‐hours.

A B C D1A Miami 1.181 0.008 0.847 0.137 0.0091B Riyadh2A Houston 8.838 0.016 0.702 0.209 0.0732B Phoenix 3.876 0.006 0.702 0.222 0.0693A Memphis 8.738 0.085 0.582 0.234 0.0983B El Paso 8.321 0.023 0.756 0.148 0.0733C San Francisco 8.678 0.006 0.232 0.310 0.4534A Baltimore 13.671 0.004 0.558 0.274 0.1654B Albuquerque 1.442 0.008 0.622 0.280 0.0904C Salem 2.153 0.014 0.529 0.286 0.1715A Chicago 21.081 0.021 0.687 0.231 0.0625B Boise 5.294 0.009 0.704 0.215 0.0735C Vancouver, BC6A Burlington 10.434 0.022 0.678 0.203 0.0976B Helena 2.541 0.007 0.517 0.365 0.1117 Duluth 2.334 0.013 0.602 0.274 0.1108 Fairbanks 1.42 0.008 0.491 0.375 0.126

ZoneBaseline Case:

(IEER Current, econ)City % VolumeA B C D

0.005 0.537 0.258 0.1970.004 0.288 0.188 0.2200.009 0.486 0.229 0.2420.004 0.457 0.224 0.2780.029 0.441 0.214 0.2920.008 0.465 0.241 0.2680.006 0.176 0.193 0.5170.004 0.395 0.268 0.2790.005 0.448 0.204 0.3220.009 0.331 0.254 0.3680.019 0.378 0.264 0.3250.006 0.455 0.229 0.2840.002 0.114 0.200 0.3840.010 0.329 0.255 0.3540.010 0.415 0.195 0.3350.008 0.370 0.205 0.3550.006 0.257 0.230 0.478

Case 2 with Ton-Hours

National Average

A B C D0.0113 0.3881 0.2399 0.3219

Data source Current Weighting

New Weighting

Percent Change

Manufacturer 19.50 20.65 6%

ATS 9.00 8.99 0%

1. Large shift to Bin D from Bin B. More cooling loads occur at lower‐loads relative to a buildings peak. Possibly from lower internal loads (lighting ex).

Finding:

Variables tested resulted in +/‐25% change from current IEER

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EER Test

Test Case

Manufacturer ATS

AHRI 1230 Ex 1

AHRI 1230 Ex 2

AHRI 1230 Ex 3

AHRI 1230 Ex 4

AHRI 1230 Ex 5

AHRI 340/360 Ex1

AHRI 340/360 Ex2

AHRI 340/360 Ex7

AHRI 340/360 Ex10

A 100% 14.39 6.58 10.92 10.92 10.92 10.92 10.92 11.40 11.96 12.46 11.27B 75% 17.00 8.49 11.13 11.81 12.05 12.32 14.39 11.58 11.95 15.73 13.05C 50% 23.00 10.00 10.35 12.08 12.60 12.57 16.32 11.15 11.19 18.52 12.58D 25% 26.00 10.00 7.39 12.60 10.04 10.13 22.34 9.31 9.17 17.86 15.09

Weighting Scenario A B C D Manufacture ATS

AHRI 1230 Ex

1

AHRI 1230 Ex

2

AHRI 1230 Ex

3

AHRI 1230 Ex

4

AHRI 1230 Ex

5

AHRI 340/360

Ex1

AHRI 340/360

Ex2

AHRI 340/360

Ex7

AHRI 340/360 Ex10

IEER Current Weighting 0.0202 0.6166 0.2381 0.1250 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Case 1, Frequency of Load by Bin 0.0029 0.1706 0.2195 0.6070 122% 108% 83% 103% 92% 91% 124% 90% 88% 106% 108%

Case 2, Annual Cooling Load by Bin 0.0113 0.3881 0.2399 0.3219 106% 100% 89% 98% 93% 93% 107% 92% 91% 99% 99%

Case 3, Staged Capacity Impact on Hourly Bin Range

0.1237 0.6114 0.0802 0.1848 97% 96% 99% 99% 97% 97% 99% 99% 100% 96% 100%

Case 4, Variable Capacity Impact on Hourly Bin Range

0.4638 0.3884 0.0381 0.1097 87% 87% 101% 96% 95% 95% 87% 100% 102% 88% 94%

Case 2, with 12.5% lower l imit 0.0119 0.4211 0.2715 0.2567 104% 99% 91% 97% 94% 94% 104% 93% 93% 99% 98%

Case 2, with lower and upper l imit 0.0376 0.3954 0.2715 0.2567 104% 99% 91% 97% 94% 94% 103% 93% 93% 98% 98%

Case 2, with oversizing (to 450 sf/ton) 0.0084 0.1823 0.2704 0.5000 115% 103% 82% 99% 90% 89% 116% 88% 87% 102% 102%

Recommendations based on IEER sensitivity analysis

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1. DOE to review our IEER analysis and share their perspective on best approach for updating IEER calculation

2. For VRF, consider modifying weighting factors to exclude ventilation loading

3. Consider modifying the bin‐sizes to align the average of each bin to be the test conditions used i.e. 25%, 50%, 75% respectively.

4. Consider evaluating Market Factors. Key considerations:

• Are building type breakdown % still representative of US?

• Are equipment sales distribution by volume % still representative of US and VRF?

• Currently based on (3) prototypes only: Medium Office, Standalone Retail, Primary School. Should other building types be included?

5. Consider requiring manufacturers to disclose part‐load coefficients (to allow regional incentivizing of regionally‐prioritized part‐load efficiency) (to improve ability to model energy performance and predict operations)

Note: Regarding analytical burden, work to date was completed over ~2 weeks

In summary: Why is it important to get this right?

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Manufacturers

•Creates an unfair advantage for VRF manufacturers compared to other equipment types e.g. variable air volume (VAV) systems; and for equipment with state‐of‐the‐art control systems compared to legacy systems

Consumers

•Gives consumers inaccurate information for making rational purchase decisions between VRF systems and other equipment types

Electric utilities

•Gives utilities inaccurate information on which to base major capital investment decisions i.e. power plant construction driven by peak demand

• Limits utilities’ ability to use most economically efficient path to meeting consumer demand e.g. leveraging performance benefits through building code compliance software credits, incentive programs

Unrepresentative test procedures and inaccurate metrics impact three stakeholders:

Manufacturers of competitive equipment are feeling the pinch

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Advertisement from ASHRAE Journal – July 2018“These ratings are not … designed to make apples‐to‐apples comparisons between different types of equipment”

“Before you build, get fast, accurate and free annual energy consumption and life‐cycle cost comparisons based on actual system performance, actual installed cost, and actual operating cost data”

Our data is for one field site and two independent lab studies. But our concerns are more broadly shared

Next steps

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Preferred option Back‐up option

Test procedure Adopt dynamic full‐load test

Current method based on fixed speed testing

Metric Updated IEER EER95

Next steps:• Working group to review mark‐up enabling dynamic full‐load test,

IEER sensitivity analysis and respond to proposed options

evaluation of current rating method, ieer metric for...

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