Transcript Slide 1

Thermal Comfort Control Based on
Neural Network for HVAC Application
Jian Liang and Ruxu Du
Dept. of Automation and Computer-Aided Engineering
The Chinese University of Hong Kong
August 2005
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Outline
 Introduction
 Design of the thermal comfort Controller
 Models of the thermal comfort Controller
 Design of the Neural Networks controller
 Simulation of the thermal comfort Controller
 Conclusion and further research
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Introduction
 The Heating, Ventilating and Air Conditioning (HVAC) plays an important
role in energy consumption
 In China, it takes 15% of the building energy
 In United States, it takes 44%
 Development of air-conditioning control:
 First generation: ON / OFF switch based on the sensation of the users
 Second generation: ON / OFF control assisted by a temperature sensor
 Third generation, digital control assisted by electronic thermostat, and
humidity was also taken into consideration
 Fourth generation: intelligent control (fuzzy control, adaptive control and etc.)
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Introduction
 Background
 Most of the HVAC systems still adopt the temperature / humidity
controllers
 Thermal comfort control is necessary for higher comfort level
 Thermal comfort indices
 Standard Effective Temperature (SET) (Gagge, 1971)
 Predicted Mean Vote (PMV) (Fanger, 1970): predict the mean thermal
sensation vote on a standard scale for a large group of persons
 PMV have been adopted by ISO 7730 standard, and ISO recommends to
maintain PMV at 0 with a tolerance of 0.5 as the best thermal comfort
 Thermal comfort concept: for long exposure to a constant thermal
environment with a constant metabolic rate, a heat balance can be
established for the human body and the bodily heat production is equal to
its heat dissipation
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Introduction
 Background
 Thermal comfort variables for PMV calculation
 Four environmental-dependent variables: air temperature Ta, relative air
humidity RH, relative air velocity Vair, mean radiant temperature Tmrt
 Two personal-dependent variables: activity level , clo-value (related to
clothing worn by the occupants)
 As a measure for the thermal comfort, one can use the seven point
psycho-physical ASHRAE scale:
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Introduction
 Air conditioning controller
 Most of the AC controllers are air temperature regulator (ATR)
These regulators control the indoor temperature / humidity. Since comfort level is
determined by six variables, thus these regulators can’t provide high comfort level
 Comfort index regulators were proposed (CIR) (MacArthur, 1986; Scheatzle,
1991)
These regulators are based on PMV / SET. The default reference input is 0 (neutral).
Occupant serves as a supervisory controller by adjusting the reference value
 User-adaptable comfort controller (UACC) (Federspiel and Asada, 1994 )
These controllers are based on a simplified PMV-like index proposed by Federspiel. It
can tune the PMV model parameters by learning the specific occupant’s thermal
sensation.
 Some thermal comfort sensing systems were designed (J. Kang and S. Park,
2000)
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Introduction
 Our objective: design an intelligent thermal comfort controller
based on neural networks for HVAC application
 High comfort level
Learn the comfort zone from the user’s preference, and guarantee the
high comfort level and good dynamic performance
 Energy saving
Combine the thermal comfort control with a energy saving strategy
 Air quality control
Provide variable air volume (VAV) control, and adjust the fresh air and return
air mix ratio to guarantee the required fresh air
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Thermal comfort controller design
 Block diagram of the thermal comfort control system
Human Supervision
Occupant
Comfort Zone
Learning
Occupant
demand
Decision
Thermal
sensation
Perception
Minimum Power
Control Strategy
Reference
-×
+
Direct NN
Controller
Thermal
index
Environmental
HVAC System
Thermal Space
Variables
Thermal Sensation
Model
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Thermal comfort controller design
Comfort zone learning logic
Immediate Response
•Cool room
•Maintain response
for duration=T1
Cooler
No
Yes
Time since last Repeat“cooler” request> time
Yes
Lower personal
comfort zone
Warmer
Maintain Personal
Comfort Zone
Determine need to lower
personal comfort zone
Time
Adaptsince arrive> time
User request?
No Time within
Hold time
Comfort zone >
Determine need to raise
personal comfort zone
No
Energy-Conserving Response
•Let temperature drift at
controlled rate
•Remain within limits of energy
-conserving deadband
Time
Adaptsince arrive> time
Yes
Yes
No
Immediate Response
•Heat room
•Maintain response
for duration=T1
No
Time since last Repeat“warmer” request> time
Yes
Raise personal
comfort zone
Maintain Energy
Conserving deadband
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Models of the thermal comfort controller
 Thermal sensation model
 The PMV formula proposed by Fanger (1970):
PMV  (0.028 0.3033e 0.036 M )  {( M  W )
Internal heat production
 0.42[(M  W )  58.15]
 3.05[5.733 0.000699( M  W )  Pa]
Heat loss by skin diffusion
 0.0173M (5.867  Pa)
 0.0014M (34  Ta )
Latent respiration heat loss
 3.96108  fcl[(Tcl  273) 4  (Tmrt  273) 4 ]
 fcl  hc (Tcl  Ta )}
Dry respiration heat loss
Heat loss by radiation
Heat loss by convection
where: M: metabolism (w/m2)
W: external work, equal to zero for most activity (w/m2)
M: metabolism (w/m2)
Icl: thermal resistance of clothing (clo)
fcl: ratio of body’s surface area when fully clothed to body’s surface area when nude
Pa: partial water vapor pressure (Pa)
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Models of the thermal comfort controller
 Thermal sensation model
 The personal-dependant variables, activity level and the clo-value can’t be
measured directly, and hence, in the practical design, they are set as constant
parameters according to different season
 The PMV calculation formula is nonlinear and necessitate iterative calculation.
In the simulation, a computer calculation model proposed by D. Int-Hout is
used
 If high real time performance is required, we can also adopt the PMV-like
index (Federspiel and Asada, 1994):





2
3
air



V   0   1 pv   2 Ta   3 Tmrt  V ( 4   5 pv   6 Ta )
 Or we can also use Neural Network to build a PMV calculation model
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Models of the thermal comfort controller
 Thermal space model
 A lumped parameter single-zone house model is built
 The sensible and latent energy exchange is taken into consideration
 The indoor air velocity is assumed proportional to the input airflow rate
 A uniform wall temperature is assumed and regarded equal to the mean radiant
temperature, etc.
Energy Input
HVAC System
Pump
Flow
mixer
Fresh
Air
Qin
Ts
Tmix
Heat exchanger
Fan
The
Ps
Roof
Return
air damper
Qr
Supply
Air
Exhaust
Air
Thermal Space
Flow Splitter
Qwall
Wall
To
RHo
T
Po T
vo
Tw
Air velocity Vair
Air temperauture Ta
Radiant Air temperauture Tmrt
Air humidity RHa (Pv)
Thermal load Qload
Qwin
Window
o
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Models of the thermal comfort controller
 Thermal space model
 Three input variables: cooling capacity, air flow rate, fresh air and return air mix ratio
 Three disturbances: indoor heat load, ambient temperature and humidity
f H K
 f mix 1
h V '2 / 3 Ahe
h V ' 2 / 3 Ahe min[ p (The )  ps , 0] 
r 1
1
r 1
[( To 
Ta )  Ts ]  mix fg wv [( po 
pa )  ps ]  he air
(The  Ts )  he air


Vhe r
r
C pVhe
r
r
 C pVhe
 C pVhe


  
2/3

f
H
K
T
f
Q
(
h

h
V
)
 s   mix (Ts  Ta )  mix fg wv ( ps  pa )  load  c v air

[ Aw (Tw  Ta )  Ar (Tr  Ta )  Awin (To  Ta )]
    Va
C pVa
 C pVa
 C pVa

Ta  

2/3
2/3
hheV 'air Ahe
Qin
    hheV 'air Ahe

(The  Ts ) 
min[ p(The )  ps , 0.0] 
The  

Che
Che
Che
  

2/3
Tw   (hc  hvVair ) Aw
ho Aw

(Tw  Ta ) 
(Tw  To )
  

C
C
w
w
p  

 s   h V ' 2/3 A

f
1
r

1
he
min[ p(The )  ps , 0.0]  mix [( po 
pa )  ps ]
    he air

p
Vhe r
r
 a   H fgVhe K wv

f

 mix ( ps  pa )

 Va

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Design of NN controller
 Controller design
 The conventional comfort controllers are based on the on-off control or
PI / PID control
 To overcome the nonlinear feature of PMV calculation, time delay and
system uncertainty, some advanced control algorithms have been
proposed
 Fuzzy adaptive control (Dounis and Manolakis, 2001; Calvino et al, 2004)
 Optimal comfort control (MacArthur and Grald, 1988)
 Minimum-power comfort control (Federspiel and Asada, 1994)
 A kind of direct NN controller is designed based on back-propagation
algorithm in this paper, which has been successfully applied in the
hydronic heating systems (A. Kanarachos et al, 1998)
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Design of NN controller
 NN Controller design
 A two-layer MISO NN controller is designed, which has two inputs and one output: e is
the error between the PMV set value and feedback value, is the error derivative; and u is
the output to control the HVAC system.
Initiate the Weights
e
PMV_SV +
×
−
w11

I
u
HVAC
w12
w13
Derivative e
Estimator
Thermal
Space
Acquire Input Signal
Calculate Node Input I1
1
Thermal Sensation
Model
PMV
Value

I  w11e  w12 e w13
u
Calculate Node Output
1
1  exp( I 2 )
Updates the Weights
Output Control Signal u
wij  
E
E PMV u
E u
 
  *
wij
PMV u wij
PMV wij
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Simulation of the thermal comfort controller
 I. Settings of major simulation parameters
 Heating and cooling performance are investigated
 CAV (constant-air-volume) and VAV (variable-air-volume) applications are investigated
Simulation Parameter
Settings (Cooling)
Settings (Heating)
Dimension of thermal space
5m × 5m × 3m
5m × 5m × 3m
Clo-value
0.6
1.3
Activity level (Metabolic rate)
1.0Met (W/m2)
1.0Met (W/m2)
Cooling / heating load QLoad
0.8KW
–1.6KW
HVAC capacity
-8KW
12KW
Desired minimum fresh air flow rate (for
VAV)
150m3/h
(0.042 m3/s)
150m3/h
(0.042 m3/s)
Air flow rate fmix (for CAV)
980 m3/h
(0.272 m3/s)
980 m3/h
(0.272 m3/s)
Mixed air ratio r (for CAV)
4
4
Outdoor temperature range To
25oC~33oC
4oC~12oC
Outdoor Humidity range RHo
65%~85%
45%~65%
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Simulation of the thermal comfort controller
 II. System performance under thermal comfort control and
temperature control
 For the temperature control, the reference input is 23oC (cooling) and 25oC (heating)
 For the comfort control, the reference input is 0
Temperature (oC)
30
25
Thermal comfort control (cooling)
20
Thermal comfort control (heating)
Temperature control (cooling, 23oC)
15
Temperature control (heating, 25oC)
0
5
10
15
20
10
15
Time (hour)
20
PMV
0.5
0
Thermal comfort control (cooling)
-0.5
Thermal comfort control (heating)
Temperature control (cooling, 23oC)
-1
o
Temperature control (heating, 25 C)
0
5
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Simulation of the thermal comfort controller
 III. System performance under direct NN control and PI
control
1
u

K
[
1

]
c
 For the well-tuned PI controller with integral anti-windup,
Ti s
When the control output reaches the limitation, the integral action is cut off
 For the comfort controller, the learning coefficient is set as η* = 0.315
0.8
Direct NN control
Direct NN control
PI control (anti-w indup)
PI control (anti-w indup)
1
0.6
0.8
PMV
Control signal
0.4
0.2
0.4
0.2
0
-0.2
0.6
0
0
20
40
60
80
Time (minute)
100
120
0
20
40
60
80
Time (minute)
100
120
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Simulation of the thermal comfort controller
 IV. Cooling / heating response under thermal comfort control
100
35
Supply air temperature
Supply air humidity
Indoor air temperature
30
Temperature (oC)
Indoor air humidity
90
Heat exchanger temperature
Wall temperature
80
Humidity (%)
25
20
70
15
60
10
50
5
0
20
40
60
80
Time (minute)
100
40
120
100
0
20
100
120
60
Supply air temperature
90
Supply air humidity
Indoor air temperature
Indoor air humidity
Heat exchanger temperature
80
50
Wall temperature
70
Humidity (%)
Temperature (oC)
40
60
80
Time (minute)
60
50
40
40
30
20
30
10
20
10
0
20
40
60
80
Time (minute)
100
120
0
0
20
40
60
80
Time (minute)
100
120
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Simulation of the thermal comfort controller
 V. Minimum-power control strategy under VAV Control
 By adjusting the air flow rate fmix, mixed air ratio r, and the PMV value according to
the user’s comfort zone, energy saving can be obtained
Start up
QuickCool Mode
fmix is set at the high level
PMV is set at the lower limit
Comfort Mode
fmix is set at the medium level
PMV is set at the highest comfort level
Energy Saving Mode
fmix is set at the low level
r is set at the high level
PMV value increases to the limit of comfort zone
End
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Simulation of the thermal comfort controller
 VI. System Performance under CAV and VAV Control
 Within 12 hours, cooling power consumed by VAV and CAV systems are
25.93KWh and 28.93KWh respectively, and hence, 3KWh cooling power
can be saved
30
0.4
25
PMV
0.2
0
-0.2
VAV control
-0.4
Cooling Power (KWh)
0.6
20
15
10
VAV control
5
CAV control
0
5
Time (hour)
10
CAV control
0
0
5
Time (hour)
10
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Conclusion and further work
 Conclusion
 The conventional temperature controller (on / off control or PI control ), can’t guarantee
the high comfort level (PMV = 0)
 The thermal comfort controller can keep the thermal environment at the highest level
 The designed NN controller has good control performance and disturbance rejection
ability, and easy to fine tune in practice
 The proposed minimum-power control strategy can achieve high comfort level as well as
the energy saving at the same time
 Further work
 Measurement of the activity level and the clo-value
 Location of sensor
 Development of the cost-effective thermal comfort control system
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