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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
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12:3
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13:3
0
14:3
0
15:3
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16:3
0
17:3
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20:3
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21:3
0
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23:3
0
0
1
2
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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
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0
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0
15:3
0
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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