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

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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


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


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


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

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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%

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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)


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


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


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


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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


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

1 thermal comfort control based on neural network for hvac application jian liang and ruxu du dept....

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