PREDICTIVE PRE-COOLING CONTROL
FOR LOW LIFT RADIANT COOLING
USING BUILDING THERMAL MASS
Motivation: energy and climate
Addressing energy, climate and development challenges

Buildings use 40% of energy and 70% of electricity1

Buildings represent low cost carbon emission reduction potential2

Most rapidly developing cities in cooling-dominated climates3

Increasing demand for A/C where grids are already stressed4
1.
2.
3.
4.
USDOE 2006. Building Energy Databook
IPCC 2007. Fourth Assessment Report
Sivak 2009. Energy Policy 37
McNeil and Letschert 2007. ECEEE 2007 Summer Study
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?
Low lift cooling systems (LLCS)
Cooling strategy that leverages existing technologies:






Variable speed compressor
Variable flow hydronic
distribution
Radiant cooling
Thermal energy storage (TES)
Pre-cooling control
Dedicated outdoor air system
(DOAS)
…to save cooling energy:
Operate chillers at part load
and low lift more of the time by
predictive pre-cooling control






Night time operation
Load leveling
Radiant cooling
Reduce transport energy req’d
to deliver cooling to a space
Efficient dehumidification
45000
ML\wk\BT\finalFY07\TOSenergy\TOSmoEnergy5.xls kWh|case5C
2-Speed Chiller, VAV
Var-Speed Chiller, VAV
40000
2-Speed Chiller, VAV, TES
Var-Speed Chiller, VAV, TES
2-Speed Chiller, RCP/DOAS
System Input Energy (kWh/yr)
35000
Var-Speed Chiller, RCP/DOAS
2-Speed Chiller, RCP/DOAS, TES
30000
Var-Speed Chiller, RCP/DOAS, TES
25000
20000
15000
10000
5000
0
Houston
Memphis
Los Angeles
Baltimore
Climate (Represented by City)
Chicago
Simulation of LLCS shows significant
cooling energy savings potential
Simulated 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 Atlanta
Simulated 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)
Low lift vapor compression cycle requires less work
Vapor compression cycles shown under typical and low-lift conditions
T - Temperature (°C)
60
40
700 psia
Low-lift refers to a lower
temperature difference between
evaporation and condensation
600
500
400
300
Predictive pre-cooling
of thermal storage and
variable speed fans
200
Variable-speed compressor
adapts exactly and
efficiently to conditions
20
100
Radiant cooling and
variable speed pump
0
1
1.2
1.4
S - Entropy (kJ/kg-K)
1.6
1.8
do o
r
LLCS operates a chiller at low lift more of the time
0.5
Tx (C)
24
Chiller System Specific Power, 1/COP (kW/kW)
Q(t )
J 
t 1 COP (t )
where COP = f(Tx,Tz,Q) [kWth/kWe],
subject to just satisfying the daily load requirement:
24
Q
t 1
24
Load
(t )   Q(t )
t 1
0.45
43.33
37.78
32.22
26.67
21.11
15.56
10
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Chicago---baseline
0.1
0.2
0.3
0.4
0.5
0.6
0.7 with
0.8 RCP/DOAS
0.9
1
Chicago---variable-speed
chiller
0
Part-Load Fraction
45
50
40
50
35
40
40
25
20
20
10
15
10
ou t
do o
0
rd
5 ry -bu
0
0
tem
30
20
10
dry
-bu
lb
FLEOH
FLEOH
30
30
pe r
atu
re
50
( de
gF
0.5
100
)
1
pa
ad
rt lo
io
r at
0
0
lb t
50
em
pe r
atu
re (
deg
F
0
0.5
)
100
1
dr
loa
t
r
pa
atio
Are the simulation results REAL?
Develop the pre-cooling control and experimentally test an LLCS
Optimize 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
Low lift chiller performance
Predictive pre-cooling control requires a chiller model to
predict chiller power consumption, cooling capacity and
COP at low-lift
To identify a chiller model under low lift conditions:



Build a heat pump test stand
Experimentally test the performance of a heat pump at low
pressure ratios, which was later converted to a chiller for LLCS
Identify empirical model of chiller performance useful for
predictive control
Measured heat pump performance at
many steady state conditions
Tested 131 combinations of the
following conditions
Outdoor
temp (C)
Indoor
temp (C)
Compressor
speed (Hz)
Fan
speed
(RPM)
15
22.5
30
37.5
45
14
24
34
19
30
60
95
300
450
600
750
900
1050
1200
To model chiller power, cooling rate, and
COP as a function of all 4 variables
4
Pa)
Compress
5
1
2
3
4
Low lift operation
Typicalratio
operation
Pressure
(kPa/kPa)
COP ~ 5-10 COP ~ 3.5
15
5
Outdoor unit COP (kWth/kWe)
4
Pa)
Test results
show expected higher COPs at
5
low lift 0conditions
5
EER
51
10
34
5
17
0
1
2
3
4
Pressure ratio (kPa/kPa)
5
Empirical models accurately represent
chiller cooling capacity, power and COP
4-variable cubic polynomial models
P  f(Toutdoorair , Tevaporation , compressor , fan )
QC  g(Toutdoorair , Tevaporation , compressor , fan )
Measured vs Predicted Cooling Capacity
5000
1500
Model predicted cooling capacity (W)
Model predicted power consumption (W)
Measured vs Predicted Power Consumption
2000
Relative RMSE = 5.5 %
Absolute RMSE = 27 W
1000
500
0
0
500
1000
Measure power consumption (W)
1500
4000
Relative RMSE = 1.7 %
Absolute RMSE = 40 W
3000
2000
1000
0
0
1000
2000
3000
4000
Measured cooling capacity (W)
5000
Zone temperature model identification
LLCS 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
Existing transfer function modeling methods
can be applied to predict zone temperature
OPT(t) 
t N
t N
t N
t N
t N
 a OPT(k)   b OAT(k)  c AAT(k)   d QI(k)   e QC(k)
k
k  t 1
OPT =
OAT =
AAT =
QI
=
QC
=
a,b,c,d,e =
k
k t
k
kt
k
k t
k
k t
operative temperature
outdoor air temperature
adjacent zone air temperature
heat rate from internal loads
cooling rate from mechanical system
weights for time series of each variable

(Inverse) comprehensive room transfer function (CRTF) [Seem 1987]

Steady state heat transfer physics constrain CRTF coefficients
Evaporating temperature is predicted from
intermediate temperature response models
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
RWT(t) 
t 2
t 2
 f RWT(k)   g UST(k)   h QC(k)
k
k  t 1

t 2
k
k t
k
kt
Superheat relates RWT to evaporating temperature (EVT)
Data-driven models identified for a test
chamber with a radiant concrete floor
Temperature sensors: OPT, OAT, AAT, UST, RWT
Power to internal loads: QI
Radiant concrete floor cooling rate: QC
Models validated based on accuracy of
predicting different data 24-hours-ahead
Cooling validation data temperatures
305
Sample validation
temperature data
300
temp (K)
295
290
OpT
ZAT
MRT
AAT
OAT
UST
RWT
Cooling validation data heat inputs
1000
285
Internal load
Floor cooling
280
500
heat rate (W)
275
0
20
40
60
hour
Sample validation
thermal load data
-500
-1000
-1500
0
0
20
40
60
hour
80
100
120
80
100
120
temp (K)
temp (K)
296
Transfer function models accurately predict
zone temperatures 24-hours-ahead
296
294
0
10
20
294
0
5
10
15
hour
20
Under-slab temperature (UST)
290
288
0
5
10
15
hour
25
292
294
20
25
286
UST
p-UST
0
5
10
15
hour
20
25
Return water temperature (RWT)
295
Root
mean
square
(RMSE)
Validation
data RMSEs
for 24error
hour look
ahead:
for 24 hour ahead prediction of
RMSE ZAT = 0.06
K
validation
data
RWT
p-RWT
290
RMSE MRT = 0.09 K
RMSE OPT = 0.08 K
OPT
RMSE
RMSE UST
= 0.15=
K 0.08
RMSE RWT
= 0.84=K0.15
UST
RMSE
285
280
275
292
25
OpT
p-OpT
296
292
temp (K)
15
hour
Operative temperature (OPT)
298
temp (K)
5
temp (K)
292
294
0
2
4
6
hour
8
10
K
K
RWT
RMSE = 0.84 K
p = N step ahead predicted variable
Optimization minimizes energy, maintains
comfort, and avoids freezing the chiller
24



arg min  J   rtPt ()  PENOPTt ()  PENEVTt ()
t 1
rt =
Pt =
 =
PENOPTt =
PENEVTt =
 =

electric rate at time t, or one for energy optimization
system power consumption as a function of past
compressor speeds and exogenous variables
weight for operative temperature penalty
operative temperature penalty when OPT exceeds
ASHRAE 55 comfort conditions
evaporative temperature penalty for
temperatures below freezing
Vector of 24 compressor speeds, one for each hour
of the 24 hours ahead
Perform optimization at every hour with
current building data and new forecasts


initial  i124 0  0i124 0
Pattern search initial guess at current
hour
24-hour-ahead forecasts of
outdoor air temperature,
adjacent zone temperature, and
internal loads (OAT, AAT, QI)


initial  i224 optimal ,0
Pattern search algorithm determines
optimal compressor speed schedule
for the next 24 hours

optimal  i124 optimal
Operate chiller for one hour at
optimal state
  1, optimal
f  f(1, optimal , OAT, RWT)
Pre-cooling the concrete floor maintains
comfort and reduces energy consumption
Chiller control schedule
Zone temperature response
40
20
15
10
OPT
OAT
RWT
UST
EVT
OPTmax
30
20
OPTmin
10
5
0
6 pm
12 am
6 am
hour
12 pm
0
6 pm
6 pm
Chiller power
6 am
hour
12 pm
6 pm
2000
Cumuluative energy
consumption (Wh)
200
150
100
50
0
6 pm
12 am
Chiller energy consumption
250
Chiller Power (W)
Occupied
25
Temperature (C)
Compressor speed (Hz)
30
12 am
6 am
Hour
12 pm
6 pm
1500
1000
Total energy
consumption over
24 hours = 1921 Wh
500
0
6 pm
12 am
6 am
Hour
12 pm
6 pm
Pre-cooling the concrete floor maintains
comfort and reduces energy consumption
Chiller control schedule
Zone temperature response
40
20
15
10
OPT
OAT
RWT
UST
EVT
OPTmax
30
20
OPTmin
10
5
0
6 pm
12 am
6 am
hour
12 pm
0
6 pm
6 pm
Chiller power
6 am
hour
12 pm
6 pm
2000
Cumuluative energy
consumption (Wh)
200
150
100
50
0
6 pm
12 am
Chiller energy consumption
250
Chiller Power (W)
Occupied
25
Temperature (C)
Compressor speed (Hz)
30
12 am
6 am
Hour
12 pm
6 pm
1500
1000
Total energy
consumption over
24 hours = 1921 Wh
500
0
6 pm
12 am
6 am
Hour
12 pm
6 pm
Experimental assessment of LLCS
Prior 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 floor
How real are these savings?
What practical technical obstacles exist?
Building and testing a Low-Lift System

Build chiller by modifying an inverter heat pump outdoor unit

Install a radiant concrete floor in MIT and Masdar test rooms

Implement the pre-cooling optimization control


Test LLCS under a typical summer week in Atlanta (and next
Phoenix) subject to internal loads—then in real AD weather
Compar the LLCS performance to a baseline system - a high
efficiency (SEER~16, SCOP~4.7) variable capacity split-system
air conditioner (SSAC)
LLCS and SSAC use the same outdoor unit
IDENTICAL FOR LLCS AND BASE CASE SSAC
FROM RADIANT
FLOOR
CONDENSER
FROM INDOOR UNIT
(CLOSED)
BPHX
TO INDOOR UNIT
(CLOSED)
COMPRESSOR
TO RADIANT
FLOOR
TEST CHAMBER
CLIMATE CHAMBER
ELECTRONIC EXPANSION
VALVE
LLCS provides chilled water to a radiant
concrete floor (thermal energy storage)
17’
EXPANSION TANK
FILTER
TO CHILLER
RADIANT FLOOR
12’
BPHX
RADIANT
MANIFOLD
FROM CHILLER
WATER PUMP
TEST CHAMBER
CLIMATE CHAMBER
Chiller/heat pump
Radiant concrete floor
Tested LLCS for a typical summer week in
Atlanta subject to standard internal loads
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
Outdoor climate conditions
Internal loads
40
35
600
Atlanta typical summer week OAT
30
40
25
35
20
30
0
25
Load (W)
500
400
300
20
40
60
80
100
120
Hours
Phoenix typical summer week OAT
140
160
200
100
0
0
20
40
35
60
80
100
120
140
Hours
load schedule
Phoenix typical Standard
summerefficiency
week OAT
160
30
40
25
35
20
30
0
25
5
700
700
600
600
400
100
120
140
160
300
20
0
20
40
200
60
100
0
Peak load density = 2 W/sqft
500
80
Hours
Load (W)
60
20
High efficiency load schedule
500
40
15
800
Peak load density = 3.4 W/sqft
20
10
Hour
800
Load (W)
Temperature (C)
Temperature (C)
Peak load density = 3.4 W/sqft
700
20
40
Phoenix test
Standard efficiency load schedule
800
Temperature (C)
Temperature (C)
Atlanta test
Atlanta typical summer week OAT
400
300
80
Hours
100
5
10
120
140
160
200
100
15
Hour
20
0
5
10
15
Hour
20
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)
SSAC (SEER~16) energy consumption (Wh)
Measured
Deducting latent
1
2
cooling1
LLCS energy
consumption (Wh)
Measured
10,982
14,645
25%
14,053
22%2
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.
Assuming no latent cooling by the LLCS
Predictive pre-cooling control can be
applied to other systems to achieve low lift


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
SSAC
SSAC
Radiant panel
Thermostatic
control
Predictive
control
predictive
control
4.32
4.97
7.46
Cooling delivered (Wh)
-47,940
-39,920
-39,420
Simulated energy (Wh)
11,110
8,038
5,285
Measured energy (Wh)
14,053
n/a
n/a
Error in simulation
20.9%
n/a
n/a
Savings relative to simulated base
case
base
27.6%
52.3%
Weekly average COP
Masdar Test Building
Slab Temperatures
Freshly Poured
Test Building Instrumentation
MASDAR CITY PHASE 1B DEMO
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
Summary



Detailed data on the performance of an inverter-driven rollingpiston 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. >50% clearly achievable.