predictive pre-cooling control for low lift radiant cooling using building thermal mass

PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS

Nick Gayeski, PhD candidate in Building TechnologyAugust 2010, Dissertation Defense

Topics

1. Thesis2. Motivation3. Low lift cooling systems (LLCS)

3.1 Low lift chiller performance3.2 Zone temperature model identification3.3 Predictive pre-cooling control optimization

4. Experimental assessment of LLCS5. Future research6. Summary of original contributions

1. Thesis

Predictive pre-cooling control for low lift radiant cooling using building thermal mass can lead to significant sensible cooling energy savings. What is a low-lift cooling system (LLCS)? How is it implemented using building thermal

mass? How is predictive pre-cooling control achieved? How significant are the energy savings in a real

installation?

2. Motivation: energy and climateAddressing energy, climate and

development challenges Buildings are responsible for 40% of energy and

70% of electricity consumption in the US1

Low cost carbon emission reduction potential2 Most rapidly developing cities in cooling-

dominated climates3

Increasing demand for thermal comfort4

1. USDOE 2006. Building Energy Databook2. IPCC 2007. Fourth Assessment Report3. Sivak 2009. Energy Policy 374. McNeil and Letschert 2007. ECEEE 2007 Summer Study

2. Motivation: better buildingsLeveraging integrated design, advanced HVAC, and building monitoring and automation Using integrated design to enhance active

mechanical system efficiency through thermal storage

Applying HVAC technologies in a coordinated manner for synergistic energy savings

Growth in building monitoring and automation creates opportunities for intelligent control

Topics




3. Low lift cooling systems (LLCS)Low lift cooling system: a cooling strategy that leverages the following technologies to reduce cooling energy: Variable speed compressors Hydronic distribution with variable flow Radiant cooling Thermal energy storage (TES) Pre-cooling control Dedicated outdoor air systems (DOAS)

3. Low lift cooling systems (LLCS)Low lift cooling systems save cooling energy by Operating chillers more efficiently at low lift more

of the time through predictive pre-cooling control Night time operation Spreading out the cooling load to operate at part

capacity Radiant cooling

Reducing energy for transporting cooling to a space

Providing ventilation and dehumidification efficiently

Prior research on component strategies Variable speed compressors, pumps and fans

(Hiller, Glicksman, Purdue, Armstrong, UIUC, NIST) Radiant cooling

(Olesen, Adlam, Simmonds, Scheatzle, Feustel, Stetiu) Thermal energy storage (TES)

(Braun, Henze, Norford, Rabl, Koschenz, Lehmann, Roth, Kintner-Meyer, Emery, Armstrong)

Pre-cooling control (Braun, Henze, Armstrong)

Dedicated outdoor air systems (DOAS)(Mumma, Dieckmann, Larranga)

Prior research on LLCS shows significant cooling energy savings potentialSimulated energy savings: 12 building types in 16 cities relative to a DOE benchmark HVAC system

Total annual cooling energy savings 37 to 84% in standard buildings, average 60-70% -9 to 70% in high performance buildings, average

40-60%

(Katipamula et al 2010, PNNL-19114)

LLCS cooling energy savings in AtlantaSimulated total annual cooling energy savings: in a medium size office building in Atlanta over a full year with respect to a variable air volume (VAV) system

served by a variable-speed chiller with an economizer and ideal storage

similar to a split-system air conditioner (SSAC) used as an experimental base line, with some differences

28 % annual cooling energy savings

(Katipamula et al 2010, PNNL-19114)

0

20

40

T -

Te

mpe

ratu

re (°

C)

60

1 1.2 1.4S - Entropy (kJ/kg-K)

1.6 1.8

100

200

300

400500600700 psia

Radiant cooling and variable speed pump

Predictive pre-cooling of thermal storage and variable speed fans

Low-lift refers to a lower temperature difference between evaporation and condensation

Variable speed compressor

Low lift vapor compression cycle requires less work

Predict 24-hour optimal chiller control schedule

Variable capacity chiller

Load forecasts

Building data

Identify building temperature response models

Charge active TES

Direct zone coolingCompressor

Condenser

Evaporator

TXV

Sight Glass

Outdoor Air

Outdoor Air

E1

E2

E3

T5

T6

T4

P1

P2

H3, T7

Chilled Water T2T3

F3

Bypass

BypassCompressor

Condenser

Evaporator

TXV

Sight Glass

Outdoor Air

Outdoor Air

E1

E2

E3

T5

T6

T4

P1

P2

H3, T7

Chilled Water T2T3

F3

Bypass

Bypass

Pre-cool concrete-core thermal energy storage

Pre-cool passive TES

LLCS operates a chiller at low lift more of the time

Occupied zone

LLCS research overviewDevelop the pre-cooling control and experimentally test an LLCSOptimize control of a chiller over a 24-hour look-ahead schedule to minimize daily chiller energy consumption by operating at low lift conditions while maintaining thermal comfort Informed by data-driven zone temperature

response models and forecasts of climate conditions and loads

Informed by a chiller performance model that predicts chiller power and cooling rate at future conditions for a chosen control

Topics




3.1 Low lift chiller performance

Predictive pre-cooling control requires a chiller model to predict chiller power consumption, cooling capacity and COP at low-liftTo identify a chiller model under low lift conditions: Built a heat pump test stand Experimentally tested the performance of a heat

pump at low pressure ratios, which was later converted to a chiller for LLCS

Identified an empirical model of chiller performance useful for predictive control

Outdoor temp

(C)

Indoor temp (C)

Compressor

speed (Hz)

Fan speed (RPM)

1522.530

37.545

142434

19306095

30045060075090010501200

Measured heat pump performance at many steady state conditions

Tested 131 combinations of the following conditions

To identify a model of chiller power, cooling rate, and COP as a function of all 4 variables

1 2 3 4 50

0.2

0.4

0.6

0.8

Pressure ratio (kPa/kPa)

Com

pres

sor E

IR (k

We/

kWth

)

1 2 3 4 50

5

10

15

20

25

Pressure ratio (kPa/kPa)C

ompr

esso

r CO

P (k

Wth

/kW

e)

1 2 3 4 50

0.2

0.4

0.6

0.8


Out

door

uni

t EIR

(kW

e/kW

th)

1 2 3 4 50

5

10

15


Out

door

uni

t CO

P (k

Wth

/kW

e)EER

34

17

51

Typical operationCOP ~ 3.5

Low lift operation

COP ~ 5-10

Test results show expected higher COPs at low lift conditions

4-variable cubic polynomial models

0 500 1000 15000

500

1000

1500

2000Measured vs Predicted Power Consumption

Measure power consumption (W)

Mod

el p

redi

cted

pow

er c

onsu

mpt

ion

(W)

Relative RMSE = 5.5 %Absolute RMSE = 27 W

0 1000 2000 3000 4000 50000

1000

2000

3000

4000

5000Measured vs Predicted Cooling Capacity

Measured cooling capacity (W)

Mod

el p

redi

cted

coo

ling

capa

city

(W)

Relative RMSE = 1.7 %Absolute RMSE = 40 W

),,T,T(fP fancompressornevaporatiooutdoorair ),,T,T(gQC fancompressornevaporatiooutdoorair

Empirical models accurately represent chiller cooling capacity, power and COP

20 30 40 50 60 70 80 900

0.1

0.2

0.3

0.4

0.55/35

Compressor Speed (Hz)

1/C

OP

(We/W

th)

1/COP vs Compressor Speed at Fan Speed = 750 RPM Te/To

20 30 40 50 60 70 80 900

0.1

0.2

0.3

0.4

0.5

5/25

5/35


1/C

OP

(We/W

th)


20 30 40 50 60 70 80 900

0.1

0.2

0.3

0.4

0.5

5/25

5/35

10/25


1/C

OP

(We/W

th)


Night time operation

Radiant cooling

Te = Evaporating temperature, To = Outdoor air temperature

Load spreading

LLCS controls enable the chiller to operate at low lift conditions and higher COPs

Topics




3.2 Zone temperature model identificationLLCS control requires zone temperature response models to predict temperatures and chiller performance Develop data-driven models from which to predict

Zone operative temperature (OPT) The temperature underneath the concrete slab (UST) Return water temperature (RWT) and ultimately chiller

evaporating temperature (EVT) from which chiller power and cooling rate can be calculated

Assume ideal forecasts of outdoor climate and internal loads

Implement data-driven modeling on a real test chamber

OPT = operative temperatureOAT = outdoor air temperatureAAT = adjacent zone air temperatureQI = heat rate from internal loadsQC = cooling rate from mechanical

systema,b,c,d,e = weights for time series of

each variable

(Inverse) comprehensive room transfer function (CRTF) [Seem 1987]

Steady state heat transfer physics constrain CRTF coefficients

Nt

tkk

Nt

tkk

Nt

tkk

Nt

1tk

Nt

tkkk )k(QCe)k(QId)k(AATc)k(OATb)k(OPTa)t(OPT

Existing transfer function modeling methods can be applied to predict zone temperature

Chiller power and cooling rate depend on evaporating temperature, which is coupled to return water temperature, and thus to the state of thermal energy storage, in this case a radiant

concrete floor

Predict concrete floor under-slab temperature (UST) using a transfer function model

Predict return water temperature (RWT) using a low-order transfer function model in UST and cooling rate QC

Superheat relates RWT to evaporating temperature (EVT)

2t

tkk

2t

tkk

2t

1tkk )k(QCh)k(USTg)k(RWTf)t(RWT

Evaporating temperature is predicted from intermediate temperature response models

Temperature sensors: OPT, OAT, AAT, UST, RWTPower to internal loads: QIRadiant concrete floor cooling rate: QC

Data-driven models identified for a test chamber with a radiant concrete floor

Typical temperature measurements at each location below: Outdoor air temperature Surface temperature Zone air temperature Concrete floor temperature

In a real building: Outdoor air temperature Zone globe temperature Zone air temperature Concrete slab temperature

x x X X

XX

XX

X X

Xx

x

xx

X

XX

South Wall

Floor/Ceiling

East Wall

West Wall

North Wall

Data-driven models can be identified from a small set of temperature measurements

17 ft

8 ft

12 ft

0 20 40 60 80 100275

280

285

290

295

300

305

hour

tem

p (K

)

Cooling training data temperatures

OpTZATMRTAATOATUSTRWT

0 20 40 60 80 100-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

hour

heat

rate

(W)

Cooling training data heat inputs

Internal loadFloor cooling

Sample training temperature data

Sample training thermal load data

Models trained using a few days of data

0 20 40 60 80 100275

280

285

290

295

300

305

hour

tem

p (K

)

Cooling training data temperatures


Sample training temperature data

Sample training thermal load data

0 20 40 60 80 100-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

hour

heat

rate

(W)

Cooling training data heat inputs


0 20 40 60 80 100292

294

296

298

hour

tem

p (K

)

ZATp-ZAT

0 20 40 60 80 100292

294

296

298

hour

tem

p (K

)

MRTp-MRT

0 20 40 60 80 100292

294

296

298

hour

tem

p (K

)

OpTp-OpT

0 20 40 60 80 100285

290

295

300

hour

tem

p (K

)

USTp-UST

0 10 20 30 40275

280

285

290

295

300

hour

tem

p (K

)

Training data RMSEs RMSE ZAT = 0.04 KRMSE MRT = 0.02 KRMSE OPT = 0.03 KRMSE UST = 0.04 KRMSE RWT = 0.68 K p = one step ahead predicted variable

RWTp-RWT

Operative temperature (OPT) Under-slab temperature (UST)

Return water temperature (RWT)Root mean square error (RMSE) for training data

OPT RMSE = 0.03 KUST RMSE = 0.04 KRWT RMSE = 0.68 K

Transfer function models accurately predict training data temperatures

Models validated based on accuracy of predicting different data 24-hours-ahead

0 20 40 60 80 100 120275

280

285

290

295

300

305

hour

tem

p (K

)

Cooling validation data temperatures


0 20 40 60 80 100 120-1500

-1000

-500

0

500

1000

hour

heat

rate

(W)

Cooling validation data heat inputs


Sample validation temperature data

Sample validation thermal load data

0 5 10 15 20 25292

294

296

298

300

hour

tem

p (K

)

ZATp-ZAT

0 5 10 15 20 25292

294

296

298

hour

tem

p (K

)

MRTp-MRT

0 5 10 15 20 25292

294

296

298

hour

tem

p (K

)

OpTp-OpT

0 5 10 15 20 25286

288

290

292

294

hour

tem

p (K

)

USTp-UST

0 2 4 6 8 10275

280

285

290

295

hour

tem

p (K

)

Validation data RMSEs for 24 hour look ahead: RMSE ZAT = 0.06 KRMSE MRT = 0.09 KRMSE OPT = 0.08 KRMSE UST = 0.15 KRMSE RWT = 0.84 K p = N step ahead predicted variable

RWTp-RWT

Operative temperature (OPT) Under-slab temperature (UST)

Return water temperature (RWT)Root mean square error (RMSE) for 24 hour ahead prediction of validation data

OPT RMSE = 0.08 KUST RMSE = 0.15 KRWT RMSE = 0.84 K

Transfer function models accurately predict zone temperatures 24-hours-ahead

Topics




3.3 Pre-cooling control optimizationOptimize chiller operation over 24 hours to minimize energy consumption and maintain thermal comfort Employ a direct pattern search1 to minimize the

objective function by selecting an optimal schedule of 24 compressor speeds2, one for each hour

Use chiller model to calculate cooling rate and power consumption

Use temperature response models to predict zone temperatures to ensure comfort is maintained

1. See Lewis et al 1999, SIAM J. of Optimization or MATLAB Optimization Toolbox 2. Given forecasts of OAT, optimal condenser fan speeds are determined by the choice of

compressor speed

rt = electric rate at time t, or one for energy optimizationPt = system power consumption as a function of past compressor speeds and exogenous variables = weight for operative temperature penaltyPENOPTt = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditionsPENEVTt = evaporative temperature penalty for

temperatures below freezing = Vector of 24 compressor speeds, one for each hour of the 24 hours ahead

24

1ttttt )(PENEVT)(PENOPT)(PrJminarg

Optimization minimizes energy, maintains comfort, and avoids freezing the chiller

Pattern search initial guess at current hour

Pattern search algorithm determines optimal

compressor speed schedule for the next 24 hours

Operate chiller for one hour at optimal state

24-hour-ahead forecasts of outdoor air temperature, adjacent zone temperature, and internal loads (OAT, AAT, QI)

0241i0241iinitial 0

optimal241ioptimal

optimal,1

)RWT,OAT,(ff optimal,1

0,optimal242iinitial

Perform optimization at every hour with current building data and new forecasts

Predictive pre-cooling control maintains comfort and reduces energy consumption

6 pm 12 am 6 am 12 pm 6 pm0

10

20

30

40

hour

Tem

pera

ture

(C)

Zone temperature response

6 pm 12 am 6 am 12 pm 6 pm0

500

1000

1500

2000

Hour

Cum

ulua

tive

ener

gy

cons

umpt

ion

(Wh)

Chiller energy consumption

6 pm 12 am 6 am 12 pm 6 pm0

50

100

150

200

250

Hour

Chi

ller P

ower

(W)

Chiller power

6 pm 12 am 6 am 12 pm 6 pm0

5

10

15

20

25

30

hour

Com

pres

sor s

peed

(Hz)

Chiller control schedule

OPTOATRWTUSTEVTOPTmax

OPTmin

Total energy consumption over 24 hours = 1921 Wh

Occupied

Topics




4. Experimental assessment of LLCSPrior research shows dramatic savings from LLCS, but Based entirely on simulation Assumes idealized thermal storage, not a real

concrete floor Chiller power and cooling rate are not coupled to

thermal storage, as it is for a concrete radiant floorHow real are these savings?What practical technical obstacles exist?

Built a chiller by modifying the heat pump outdoor unit

Built an LLCS test system with a radiant concrete floor

Implemented the pre-cooling optimization control Tested LLCS under a typical summer week in

Atlanta (and next Phoenix) subject to internal loads Compared the LLCS performance to a baseline

system - a high efficiency (SEER ~ 16) variable capacity split-system air conditioner (SSAC)

Built and tested a near full-scale LLCS

CONDENSER

ELECTRONIC EXPANSION VALVE

COMPRESSOR

BPHX

TO RADIANT FLOOR

FROM RADIANT FLOOR

FROM INDOOR UNIT (CLOSED)

TO INDOOR UNIT (CLOSED)

TEST CHAMBER CLIMATE CHAMBER

IDENTICAL FOR LLCS AND BASE CASE SSAC

LLCS and SSAC use the same outdoor unit

TEST CHAMBER CLIMATE CHAMBER

BPHX

WATER PUMP

TO CHILLER

FROM CHILLER

FILTEREXPANSION TANK

12’

17’

RADIANT MANIFOLD

RADIANT FLOOR

LLCS provides chilled water to a radiant concrete floor (thermal energy storage)

Chiller/heat pump

Radiant concrete floor

LLCS chiller

Brazed plate heat exchanger

SSAC (SEER~16)

Standard mini-split indoor unit

Atlanta typical summer week and standard efficiency loads Based on typical meteorological year weather data Assuming two occupants and ASHRAE 90.1 2004

loads

Run LLCS for one week *(after a stabilization period)Run split-system air conditioner (SSAC) for one week* Compare sensible cooling only Mixing fan treated as an internal load

Repeat for Phoenix typical summer week, high efficiency loads – to be completed after climate chamber HVAC repairs

Tested LLCS for a typical summer week in Atlanta subject to standard internal loads

0 20 40 60 80 100 120 140 160

20

25

30

35

40

Tem

pera

ture

(C)

Hours

Atlanta typical summer week OAT

0 20 40 60 80 100 120 140 160

20

25

30

35

40

Tem

pera

ture

(C)

Hours

Phoenix typical summer week OAT

5 10 15 200

100

200

300

400

500

600

700

800

Load

(W)

Hour

Standard efficiency load schedule

Peak load density = 3.4 W/sqft

5 10 15 200

100

200

300

400

500

600

700

800

Load

(W)

Hour

High efficiency load schedule

Peak load density = 2 W/sqft

5 10 15 200

100

200

300

400

500

600

700

800

Load

(W)

Hour

Standard efficiency load schedule

Peak load density = 3.4 W/sqft

5 10 15 200

100

200

300

400

500

600

700

800

Load

(W)

Hour

High efficiency load schedule

Peak load density = 2 W/sqft

0 20 40 60 80 100 120 140 160

20

25

30

35

40

Tem

pera

ture

(C)

Hours

Atlanta typical summer week OAT

0 20 40 60 80 100 120 140 160

20

25

30

35

40

Tem

pera

ture

(C)

Hours

Phoenix typical summer week OAT

Atla

nta

test

Phoe

nix

test

Outdoor climate conditions

Internal loads

LLCS energy consumption

(Wh)

SSAC (SEER~16) energy consumption (Wh)

Measured

10,982Measured 14,645 25%Deducting latent cooling1 14,053 22%2

1 Latent cooling is deducted by measuring condensate water from the SSAC, calculating the total enthalpy associated with its condensation, and dividing it by the average SSAC COP over the week.

2 Assuming no latent cooling by the LLCS

LLCS ENERGY SAVINGS relative to SSAC in Atlanta subject to standard loads

Similar to simulated total annual cooling energy savings, 28 percent, by (Katipamula et al 2010)

0 5 1018

19

20

21

22

23

24

25

26

OPTLLCS = 23.4OPTSPLIT = 22.8

hour

tem

pera

ture

(C)

Monday

0 5 1018

19

20

21

22

23

24

25

26

OPTLLCS = 23OPTSPLIT = 22.7

hour

Tuesday

0 5 1018

19

20

21

22

23

24

25

26


hour

Wednesday

0 5 1018

19

20

21

22

23

24

25

26


hour

Thursday

0 5 1018

19

20

21

22

23

24

25

26


hour

Friday

OPTLLCS

OPTSPLIT

Standard efficiency loads result in large air temperature rise

OPT rises by as much as a 6 Celsius over 10 occupied hours

Below the ASHRAE 55 limit of 3.3 C/4 hr, but may still be a comfort issue

Occupied hours

Tem

pera

ture

(C)

67

77

°F

LLCS THERMAL PERFORMANCE relative to SSAC in Atlanta subject to standard loads

Radiant floor capacity should be increased by decreasing pipe spacing, permitting higher evaporating temperatures

Zone thermal load should be better matched to both the chiller capacity and the radiant floor capacity

Improve the design and control of the BPHX chiller COP, as-built COPs are lower than COPs measured on the test stand

Add insulation below the concrete floor to increase thermal storage efficiency and create a more representative LLCS

Improvements likely to yield better LLCS performance

Limitations of the existing LLCS test chamber

SSAC Thermostatic

control

SSAC Predictive

control

Concrete-floor predictive

control

Radiant panel predictive

control

Weekly average COP 4.32 4.97 4.80 7.46

Cooling delivered (Wh) -47,940 -39,920 -53,200 -39,420

Simulated energy (Wh) 11,110 8,038 11,072 5,285

Measured energy (Wh) 14,053 n/a 10,982 n/a

Error in simulation 20.9% n/a -0.8% n/a

Savings relative to simulated base case

base 27.6% base 52.3%

Simulated the performance of predictive pre-cooling control on the SSAC and with radiant ceiling panels

Significant savings potential for predictive control on other systems

Predictive pre-cooling control can be applied to other systems to achieve low lift

Topics




5. Future LLCS research Refine LLCS methods

Determine evaporating temperature without measuring under-slab concrete temperature

Refine temperature response model identification methods, e.g. real-time model identification with updated training data

Simplify and improve the pre-cooling optimization and control

Combine concrete-core with direct cooling (e.g. chilled beams) and adapt the predictive control algorithm

Perform testing subject to actual outdoor conditions at MASDAR

Install and test LLCS in a real building (medium size office)

Pre-cooling control for other LLCS configurations

Future of LLCS in real buildings Concrete-core and radiant systems gaining market

share, and familiarity among architects and engineers (primarily in Europe)

Automation systems are becoming more prevalent/sophisticated

Capital cost savings for LLCS in medium office buildings, -0.58 $/sqft incremental cost relative to $7.91/sqft base cost1

Adapt components of LLCS to existing buildings and different new and existing building types, e.g. Direct cooling combined with active or passive

thermal storage Radiant concrete-core using a “topping slab” for

existing buildings Adapt low-lift predictive control to existing concrete-

core buildings

1. Katipamula et al 2010, PNNL-19114

6. Summary of original contributions

Detailed data on the performance of an inverter-driven rolling-piston compressor heat pump over a wide range of conditions including low lift, over a capacity range of 5:1

Methodology for integrating chiller models and zone temperature response models into a pre-cooling optimization algorithm for controlling LLCS with real building thermal mass

Experimental validation of significant LLCS sensible cooling energy savings relative to a state-of-the-art split system air conditioner (SEER 16), 25 percent in Atlanta with standard efficiency internal loads

Thank you!Professors Leslie Norford, Leon Glicksman and Peter ArmstrongMassachusetts Institute of TechnologyMasdar Institute of Science and TechnologyStephen Samouhos and Siân KleindienstRob Darnell, Tom Pittsley, the BAC and the solar decathletesProfessor Marilyne Andersen and all the DaylightersSrinivas Katipamula and the Pacific Northwest National LaboratoryDaniel Nikovski, Ankur Jain, Chris Laughman, Mitsubishi Electric

Research LaboratoryVolker Ruhle and UponorAmanda Graham, Beth Conlin, and the Martin Family Society of

FellowsPeter Cooper, Walt Henry and MIT FacilitiesEvan Samouhos and EVCO mechanicalKathleen Ross, Ali Mulcahy, Jim Harrington, Renee CasoAll my colleagues in Building TechnologyMy friends and colleagues at MIT, especially Yanni, Saeed, Zach, and

BrandonDavid, Andrea, Yanni, Steve, Bruno for their late hour feedbackMom, Dad, Emily, Jeanie, Pat, SophiaCelina, and our dogs

Nicholas Gayeski, [email protected]

predictive pre-cooling control for low lift radiant cooling using building thermal mass

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