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Predictive Pre-cooling Control For Low Lift Radiant cooling USING BUILDING THERMAL MASS. Nick Gayeski, PhD candidate in Building Technology August 2010, Dissertation Defense. Topics. 1.Thesis 2.Motivation 3.Low lift cooling systems (LLCS) 3.1Low lift chiller performance - PowerPoint PPT Presentation
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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
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
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
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
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
Pressure ratio (kPa/kPa)
Out
door
uni
t EIR
(kW
e/kW
th)
1 2 3 4 50
5
10
15
Pressure ratio (kPa/kPa)
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
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
10/25
Compressor Speed (Hz)
1/C
OP
(We/W
th)
1/COP vs Compressor Speed at Fan Speed = 750 RPM Te/To
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
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
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
OpTZATMRTAATOATUSTRWT
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
Internal loadFloor cooling
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
OpTZATMRTAATOATUSTRWT
0 20 40 60 80 100 120-1500
-1000
-500
0
500
1000
hour
heat
rate
(W)
Cooling validation data heat inputs
Internal loadFloor cooling
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
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
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
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
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
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
OPTLLCS = 22.9OPTSPLIT = 22.8
hour
Wednesday
0 5 1018
19
20
21
22
23
24
25
26
OPTLLCS = 22.9OPTSPLIT = 22.8
hour
Thursday
0 5 1018
19
20
21
22
23
24
25
26
OPTLLCS = 23.1OPTSPLIT = 22.7
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
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
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]