1 Thermal Comfort Control Based on Neural Network for HVAC Application Jian Liang and Ruxu Du Dept....
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Transcript of 1 Thermal Comfort Control Based on Neural Network for HVAC Application Jian Liang and Ruxu Du Dept....
1
Thermal Comfort Control Based on
Neural Network for HVAC Application
Jian Liang and Ruxu Du
Dept. of Automation and Computer-Aided EngineeringThe Chinese University of Hong Kong
August 2005
2
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
3
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.)
Introduction
4
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
Introduction
5
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:
Introduction
6
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)
Introduction
7
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
Introduction
8
Block diagram of the thermal comfort control system
Thermal comfort controller design
Human Supervision
Decision PerceptionThermal sensation
Occupant
Comfort Zone Learning
Minimum Power Control Strategy
Occupant demand
Direct NN Controller
HVAC System Thermal Space
Thermal Sensation
Model
×+-Environmental
Variables
Thermal index
Reference
9
Comfort zone learning logic
User request? WarmerCooler
Immediate Response•Heat room•Maintain response for duration=T1
Immediate Response•Cool room•Maintain response for duration=T1
Determine need to lower personal comfort zone
Time since arrive
Adapt-time>
Time since last “cooler” request
Repeat-time>
Lower personal comfort zone
Yes
Yes
Determine need to raise personal comfort zone
Time since arrive
Adapt-time>
Time since last “warmer” request
Repeat-time>
Raise personal comfort zone
Yes
Yes
Maintain PersonalComfort Zone
Time within Comfort zone
Energy-Conserving Response•Let temperature drift at controlled rate•Remain within limits of energy -conserving deadband
Yes
No
No
No
No
No Hold time>
Maintain Energy Conserving deadband
Thermal comfort controller design
10
Thermal sensation model The PMV formula proposed by Fanger (1970):
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)
)}(
])273()273[(1096.3
)34(0014.0
)867.5(0173.0
])(000699.0733.5[05.3
]15.58)[(42.0
){()3033.0028.0(
448
036.0
aclc
mrtcl
a
M
TThfcl
TTfcl
TM
PaM
PaWM
WM
WMePMV
Heat loss by convection
Heat loss by radiation
Dry respiration heat loss
Heat loss by skin diffusion
Latent respiration heat loss
Internal heat production
Models of the thermal comfort controller
11
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):
Or we can also use Neural Network to build a PMV calculation model
)( 6543
2
3210 avairmrtav TpVTTpV
Models of the thermal comfort controller
12
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.
Heat exchanger
Returnair damper
Flowmixer
HVAC SystemEnergy Input
Thermal Space
Supply AirExhaust
Air
FreshAir
Flow Splitter
Window
Roof
Wall
Qwin
Qr
Qwall
Pump
Air velocity Vair
Air temperauture Ta
Radiant Air temperauture Tmrt
Air humidity RHa (Pv)Thermal load Qload
Tw
Ts
The
To
RHo ToPvo To
Tmix
Qin
Ps
Fan
Models of the thermal comfort controller
13
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
2/3 2 /3' ' min[ ( ) ,0]1 1 1 1[( ) ] [( ) ] ( )
( )
mix fg wvmix he air he he air he he so a s o a s he s
he p he p he p he
mix fg wvs mixs a
a pa
he
w
s
a
f H Kf h V A h V A p T pr rT T T p p p T T
V r r C V r r C V C V
f H KT fT T
V CT
T
T
p
p
2/3
2 /3 2/3
2 /3
2 /3
( )( ) [ ( ) ( ) ( )]
' '( ) min[ ( ) ,0.0]
( )( ) ( )
'
load c v airs a w w a r r a win o a
a p a p a
he air he he air he inhe s he s
he he he
c v air w o ww a w o
w w
he air he
f
Q h h Vp p A T T A T T A T T
V C V C V
h V A h V A QT T p T p
C C C
h h V A h AT T T T
C C
h V A
H
1 1min[ ( ) ,0.0] [( ) ]
( )
mixhe s o a s
g he wv he
mixs a
a
f rp T p p p p
V K V r r
fp p
V
Models of the thermal comfort controller
14
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)
Design of NN controller
15
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.
Design of NN controller
+
−
PMV_SV × Thermal Space
Thermal SensationModel
Derivative Estimator
HVACu
PMV Value
w12
w13
Iw11
1
e
e
131211 wewewI
)exp(1
12I
u
ijijij
ij w
u
PMV
E
w
u
u
PMV
PMV
E
w
Ew
*
Calculate Node Input I1
Calculate Node Output
Updates the Weights
Output Control Signal u
Initiate the Weights
Acquire Input Signal
16
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 of the thermal comfort controller
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%
17
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
0 5 10 15 20
15
20
25
30
Tem
pera
ture
(o C)
0 5 10 15 20
-1
-0.5
0
0.5
Time (hour)
PM
V
Thermal comfort control (cooling)
Thermal comfort control (heating)
Temperature control (cooling, 23oC)
Temperature control (heating, 25oC)
Thermal comfort control (cooling)
Thermal comfort control (heating)
Temperature control (cooling, 23oC)
Temperature control (heating, 25oC)
Simulation of the thermal comfort controller
18
III. System performance under direct NN control and PI
control For the well-tuned PI controller with integral anti-windup,
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 20 40 60 80 100 120-0.2
0
0.2
0.4
0.6
0.8
Time (minute)
PM
V
0 20 40 60 80 100 120
0
0.2
0.4
0.6
0.8
1
Time (minute)
Con
trol
sig
nal
Direct NN control
PI control (anti-w indup)
Direct NN control
PI control (anti-w indup)
]1
1[sT
Kui
c
Simulation of the thermal comfort controller
19
IV. Cooling / heating response under thermal comfort control
0 20 40 60 80 100 1205
10
15
20
25
30
35
Time (minute)
Tem
pera
ture
(o C)
0 20 40 60 80 100 12040
50
60
70
80
90
100
Time (minute)
Hum
idity
(%
)
Supply air humidity
Indoor air humidity
Supply air temperature
Indoor air temperature
Heat exchanger temperature
Wall temperature
0 20 40 60 80 100 120
10
20
30
40
50
60
70
80
90
100
Time (minute)
Tem
pera
ture
(o C)
0 20 40 60 80 100 1200
10
20
30
40
50
60
Time (minute)
Hum
idity
(%
)Supply air temperature
Indoor air temperature
Heat exchanger temperature
Wall temperature
Supply air humidity
Indoor air humidity
Simulation of the thermal comfort controller
20
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
fmix is set at the high level PMV is set at the lower limit
Comfort Mode
QuickCool Mode
Energy Saving Mode
fmix is set at the medium level PMV is set at the highest comfort level
fmix is set at the low levelr is set at the high level
PMV value increases to the limit of comfort zone
End
Start up
Simulation of the thermal comfort controller
21
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
0 5 10
-0.4
-0.2
0
0.2
0.4
0.6
Time (hour)
PM
V
0 5 100
5
10
15
20
25
30
Time (hour)
Coo
ling
Pow
er (
KW
h)
VAV control
CAV controlVAV control
CAV control
Simulation of the thermal comfort controller
22
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
23