Technique of uncertainty & sensitivity analysis for building performance simulation and calculations Petr Kotek, CTU in Prague, Faculty of Civil Engineering,

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

1

TECHNIQUE OFTECHNIQUE OF UNCERTAINTY AND SENSITIVITY UNCERTAINTY AND SENSITIVITY ANALYSISANALYSIS FOR SUSTAINABLE BUILDING ENERGY FOR SUSTAINABLE BUILDING ENERGY

SYSTEMS PERFORMANCE CALCULATIONS SYSTEMS PERFORMANCE CALCULATIONS

Petr KOTEK

Filip JORDÁN, Karel KABELE, Jan HENSEN

poster P3-16

Czech Technical University in Prague, Faculty of Civil Engineering, Czech Republic

Technische Universiteit Eindhoven, Building Physics & Systems, Netherlands9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

Introduction

2The crucial in the optimization methods of energy consumption are uncertainty and sensitivity analyses (UA & SA) and their results.

The MonteCarlo (MCA) method was used to find out the most influential parameters of a thermal energy simulation model and simple analytical model of HVAC system

repeated simulations9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

3

Procedure – case study

sampling

software procedure

ASHREA BESTEST case600 was chosen

SSSS xxxx

xxxx

xxxx

,48,3,2,1

2,482,32,22,1

1,481,31,21,1

x48

random sampling

(crude MonteCarlo method)

S = 6 simulationssample matrix9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

3

Procedure – case study

sampling

software procedure

ASHREA BESTEST case600 was chosen

SSSS xxxx

xxxx

xxxx

,48,3,2,1

2,482,32,22,1

1,481,31,21,1

x48

LatinHypercube sampling

reduce number of simulations

sample matrix S = 6 simulations 9

1 2 3 4 5 6

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

Softwares for UA & SA - procedure

4sampling

software procedure

IES<VE>

SimLab

pre-processormodel

execution post-processor

UA & SAyk outputs

xn inputs with uncertainty

outputs.out

MS Excel

sample matrix.sam

Inputs for simulations

outputs from simulations

200 automatic simulationsex

tern

al m

od

el

heat losses

heat gains

9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

5

THERMAL SIMULATION

heating and cooling demand during the whole year

ga

ins

[

kw

]

l

os

se

s

the coldest daytime

-7

-5

-3

-1

1

3

5

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

[kW

]

results for main values of inputs

heat losses

heat gains

9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

THERMAL SIMULATION

-5

-4

-3

-2

-1

0

0:30

1:30

2:30

3:30

4:30

5:30

6:30

7:30

8:30

9:30

10:3

0

11:3

0

12:3

0

13:3

0

14:3

0

15:3

0

16:3

0

17:3

0

18:3

0

19:3

0

20:3

0

21:3

0

22:3

0

23:3

0

0

1

2

3

4

5

6

The coldest day - 4.January

kW

he

atin

gco

olin

g

cooling d.heating demand

heating and cooling demand during the whole year with uncertainty

-7

-5

-3

-1

1

3

5

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

[kW

]

results with uncertainty bound

from 200 simulations

-5

-3

-1

1

3

5

0:30

1:30

2:30

3:30

4:30

5:30

6:30

7:30

8:30

9:30

10:3

0

11:3

0

12:3

0

13:3

0

14:3

0

15:3

0

16:3

0

17:3

0

18:3

0

19:3

0

20:3

0

21:3

0

22:3

0

23:3

0

The coldest day - 4.January

cooling d.heating demand heating demandk

Wh

ea

ting

coo

ling

SAfrom

SimLab

heat losses

heat gains

9

5

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

HVAC SYSTEMS AND CALCULATIONS

6

-7

-5

-3

-1

1

3

5

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

[kW

]

heat losses

heat gains

FCU VAV

9

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

HVAC SYSTEMS AND CALCULATIONS

-7

-5

-3

-1

1

3

5

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

[kW

]

heat losses

heat gains

FCU VAV

-6

-5

-4

-3

-2

-1

0

1

2

3

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

8760 hours = year

[kW

]heatin

gcoolin

g

heating coil in AHU

cooling coil in AHU

0

1

2

3

4

5

6

7

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

8760 hours = year

[kW

]

heating coil in VAV-box

heatin

g

AH

UV

AV

-bo

x

-1.0

-0.5

0.0

0.5

1.0

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

8760 hours = year

[kW

]

heating coil in AHU

cooling coil in AHU

heat

ing

cool

ing

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401

8760 hours = year

[kW

]

heating coil in FCU

cooling coil in FCU

cool

ing

heat

ing

AH

UF

CU

LOADS with uncertainty bound LOADS with uncertainty bound

9

7

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

9

RESULTS

9.00

10.00

11.00

12.00

13.00

14.00

15.00

16.00

17.00

9

10

11

12

13

14

15

16

17

Eto

t to

tal

ener

gy

con

sum

pti

on

[M

Wh

]

FC

U

VA

V

by using VAV system we save energy, but according to the uncertainty in inputs it can be less effective than FCU

with combination of energy simulation and MonteCarlo simulations we can find out the most sensitive parameters for constructions and for HVAC components and settings.

These parameters can be optimized with GenOpt (TrnOpt), BeOpt,…

-1 -0.5 0 0.5 1

Ve

h

Tam

Ti

Te

patm

Q

Tht

-1 -0.5 0 0.5 1

Q

Tam

Ve

patm

Tht

Te

Ti

h

-1 -0.5 0 0.5 1

Tam

h

Ve

Ti

Te

patm

Q

Tht

SA

UA

8

ID609

Technique of uncertainty & sensitivity analysis for building performance simulation and calculations

Petr Kotek, CTU in Prague, Faculty of Civil Engineering, Czech Republic

INTRODUCTION

HVAC SYSTEMS

CONCLUSION

PROCEDURE OF MONTE CARLO

DISCUSSION

THERMAL SIMULATION

9

THANK YOUTHANK YOUFOR YOUR ATTENTIONFOR YOUR ATTENTION

DANK U WELDANK U WELVOOR UW AANDACHTVOOR UW AANDACHT

DĚKUJI ZA POZORNOSTDĚKUJI ZA POZORNOST

International end of presentation