three views on occupant behavior and their relevance to
TRANSCRIPT
Microsoft PowerPoint - Occupant Behavior Cheolsoo Park
vs-11Building Simulation Lab (bs.skku.ac.kr)
Three views on occupant behavior and their relevance to building simulation
2014.11.29 Prof. Cheol Soo Park ()
In collaboration with Young Jin Kim, Ph.D. Deuk Woo Kim, Ph.D.
Ki Cheol Kim, M.S.
Building Simulation Lab (bs.skku.ac.kr) 2
Traditional approach: too simple to be true
1. very deterministic !! 2. occupancy pattern only. 3. occupants are regarded as ‘static’.
2014-12-08
2
Y, output
Real world
variables
• Modeling real-world
To deal with uncertainty,
However, we don’t know much about Occupant Behavior (OB)
Temperature & light control, windows/doors open/close, occupancy pattern (movement)
Model
Knowledge
f(X,θ)
Noise
pdf
a posteriori distribution of physical parameters is influenced by scenarios (weather, occupant behavior)
pdf
Three views with experimental evidence in South Korea
• Stochastic approach: Markov chain
Literature: occupancy pattern in residential BLDG.
Building Simulation Lab (bs.skku.ac.kr) 8
Measured occupancy pattern for 30 households
Room #2 Living room
Master bedroom Room #1
Markov chain
Results
• Occupancy pattern in master bedroom (Household #1, Household #2)
– (a) prediction by Markov Chain, (b) measured data at household #1(#2), (c) average of measurement for 30 households
Master Bedroom in Household #1
(b)- (c) l (b)- (a) l
Household #1 0.41 0.08
Household #2 0.36 0.11
2014-12-08
6
Sensitivity analysis: CO2 simulation
X1 Flow exponent 0.6 0.65 0.7 [11]
X2 Cd, Discharge coefficient 0.6 0.675 0.75 [12, 13]
X3 Cp, wind pressure coefficient 0 0.5 1 [13]
X4 Wind velocity profile exponent 0.33 0.33 0.4 [11, 13]
X5 Local terrain constant 0.28 0.28 0.40 [11, 13]
X6 Wind speed (m/s) 1.10 2.15 5.20 [14]
X7 Outdoor temperature () 2.5 10.6 25.3 [14]
X8 Front door leakage area (cm2/EA) 24 41.8 248.6 [14-16]
X9
MR 0.42 0.83 1.54
X11 BR2 0 0.38 0.71
X12 LR 0.37 1.17 2.33
X13 Window leakage area (cm2/m2) 1.9 4.3 9 [12, 15]
X14
[7] X15 LR BR1 -2.60 0.50 1.40
X16 LR BR2 -2.60 0.50 1.40
Spearman Coefficient by Simlab 2.2
Building Simulation Lab (bs.skku.ac.kr) 12
Lessons from this case study Household #
CAV [kWh/day]
1 261.8 241.6
2 278.1 257.9
3 296.3 266.2
4 294.8 270.0
5 280.1 260.1
6 257.8 241.4
7 292.5 269.0
8 282.4 265.0
9 262.2 251.7
10 285.8 266.1
11 281.6 264.9
12 289.2 270.8
13 266.6 244.8
14 302.1 275.9
15 290.0 271.6
16 283.5 255.2
17 252.1 231.7
18 316.2 283.7
19 275.2 250.3
20 234.6 219.8
21 243.3 224.1
22 247.7 232.9
23 286.8 266.7
24 250.4 241.4
25 290.2 269.0
26 299.3 268.4
27 294.9 266.7
28 302.1 275.9
29 290.0 271.6
30 283.5 255.2
Min 234.6 219.8
Average 279.0 257.7
Max 316.2 283.7
1. The occupancy pattern is more influential than any other simulation inputs with regard to ventilation simulation.
2. Occupancy pattern is of strong stochastic nature and shouldn’t be treated in a deterministic fashion.
Energy simulation of ventilation system based on measured occupancy pattern
2014-12-08
7
Approach #2: agent simulation
• Agent simulation approach
– simulating the actions and interactions of autonomous agents (either individual or collective organizations or groups).
Figure above: http://www.anylogic.com/agent-based-modeling
Agent simulation: EnergyPlus model
– Desire: 3 types
• 4 occupants: 2 adults, 2 children
2014-12-08
8
Decision-making of agents
Environmental state (t)
Belief on causal relation
( , , , , , ) ( )A i A i i i
Building Simulation Lab (bs.skku.ac.kr) 16
Agent simulation: NetLogo
temperatures
BCVTB: NetLogo + EnergyPlus
2014-12-08
10
TIME AGEN
WINDOW S
A.C. WINDO
- - - -
On All closed 1,168 B mr mr 29.9 Hot On Closed 84
C Outdoor Outdoor - - - - -
D Outdoor Outdoor - - - - -
- - - -
Weakly All closed 1,107 B mr lv 27 Neutral Weakly Closed 57
C Outdoor Outdoor - - - - -
17:30
C Outdoor Outdoor - - - - -
22:00
22:15
C rm1 rm1 26.4 Neutral Off Open 36
D lv lv 25.9 Neutral Off Open 87
22:30
C rm1 rm1 26.3 Neutral Off Open 36
D lv lv 25.9 Neutral Off Open 87
words in underlined italic mean occurrence of the agent’s movement =living room, rm=bed room, mr= master bedroom, A.C.: Air-Conditioner
Building Simulation Lab (bs.skku.ac.kr) 20
Lessons from this study
– Three households (#1, #2, #3) were randomly selected
– a static occupancy pattern used with a constant room air temperature of 26.0oC when occupants are present
• Agent-based simulation:
– occupants’ behavior is reflected based on their perception, desire and belief. on/off appeared
• Significant difference between deterministic vs. agent-based
0 2 4 6 8 10 12 14 16 18 20 22 24 0
1000
2000
3000
4000
5000
6000
Time
Responsive occupants • Occupant’s response to environmental information (CO2)
• Relevance to building energy simulation
• Experiment #1: CO2 information not provided
• Experiment #2: CO2 information provided in real time.
• Experiment #3: CO2 information and its meaning provided
• Experiment #4: fake CO2
information provided (+ 500 ppm)
In-situ experiments
Building Simulation Lab (bs.skku.ac.kr) 23
Lessons from this study EXP. #1 EXP. #2 EXP. #3 EXP. #4
AVERAGE NUMBER OF OCCUPANTS 3.99 4.19 4.05 4.02 MINIMUM (PPM) 700 508 440 455
AVRERAGE (PPM) 1,131 844 688 716 MAXIMUM (PPM) 1,646 1,458 1,080 1,363
STANDARD DEVIATION (PPM) 208 275 217 317 MOST FREQUENT LEVEL (PPM) 1,000-1,099 600-699 900-999 500-599
PROBABILITY EXCEEDING 1,000PPM 76.4% 27.3% 4.2% 25.0%
EXP. #0 EXP. #1 EXP. #2 EXP. #3 EXP. #4
Day1 Day2 Day1 Day2 Day1 Day2 Day1 Day2 Day1 Day2
Energy Use(Kwh) 28.6 31.4 27.4 32.0 30.9 33.4 32.2 38.6 47.2 22.7
Compared to Exp. #0 (%)
Daily Average 30.0 29.7 32.2 35.4 35.0
Air Change/hour 0.70 0.97 1.13 1.89 2.13
< energy simulation results >
• Occupants act differently when information is provided.
• OB can cause the performance gap between simulation prediction and the reality !!!
• Cognitive response of occupants must be reflected in BS.
Building Simulation Lab (bs.skku.ac.kr) 24
Approach #3: Random walk
Is the above occupancy pattern predictable or not ?
• Observed occupancy pattern: seven different offices in the same building located in Vienna for a reference work day (Mahdavi et al. 2008)
Figure source: Mahdavi, A., Mohammadi, A., Kabir, E. and Lambeva, L., 2008. Occupants' operation of lighting and shading systems in office buildings. Journal of Building Performance Simulation, 1 (1), 57-65
2014-12-08
13
Random walk hypothesis
• A random walk is a sequence of random steps
• E.g.: search path of a foraging animal, a fluctuating stock price, a financial status of a gambler
1D 2D 3D
Building Simulation Lab (bs.skku.ac.kr) 26
Testing random walk hypothesis • H1: occupancy pattern in a room follows
random walk
• Frequency domain: Normalized Cumulative Periodogram (NCP)
1n n nx x w ACF
NCP
2014-12-08
14
Experiments Exp. A U lab. 06/20/2012
Exp. B BS lab. 02/26/2013
Exp. C, D Univ. Library 10/22/2012
CD
Observed data
Exp. A
Exp. B
2014-12-08
15
Observed data (cont.)
Auto-Correlation Function
nx nw nx nw
is random and can’t be predicted !!nw is not random, having a certain periodicity!!nx
2014-12-08
16
Normalized Cumulative Periodogram
nx nw nx nw
is random and can’t be predicted !!nw is not random, having a certain periodicity!!nx
Building Simulation Lab (bs.skku.ac.kr) 32
Conclusions
Stochastic (Markov)
Task #1: perception, desire, intention, sensation…… Task #2: conflict between agents
Agent 1n n nx x w
1n n nX P X
Random walk
Accurate modeling of occupant’s behavior is strongly required for better building performance prediction and simulation!!
Pn = f(perception, interaction … )
Q&A
Three views on occupant behavior and their relevance to building simulation
2014.11.29 Prof. Cheol Soo Park ()
In collaboration with Young Jin Kim, Ph.D. Deuk Woo Kim, Ph.D.
Ki Cheol Kim, M.S.
Building Simulation Lab (bs.skku.ac.kr) 2
Traditional approach: too simple to be true
1. very deterministic !! 2. occupancy pattern only. 3. occupants are regarded as ‘static’.
2014-12-08
2
Y, output
Real world
variables
• Modeling real-world
To deal with uncertainty,
However, we don’t know much about Occupant Behavior (OB)
Temperature & light control, windows/doors open/close, occupancy pattern (movement)
Model
Knowledge
f(X,θ)
Noise
a posteriori distribution of physical parameters is influenced by scenarios (weather, occupant behavior)
Three views with experimental evidence in South Korea
• Stochastic approach: Markov chain
Literature: occupancy pattern in residential BLDG.
Building Simulation Lab (bs.skku.ac.kr) 8
Measured occupancy pattern for 30 households
Room #2 Living room
Master bedroom Room #1
Markov chain
Results
• Occupancy pattern in master bedroom (Household #1, Household #2)
– (a) prediction by Markov Chain, (b) measured data at household #1(#2), (c) average of measurement for 30 households
Master Bedroom in Household #1
(b)- (c) l (b)- (a) l
Household #1 0.41 0.08
Household #2 0.36 0.11
2014-12-08
6
Sensitivity analysis: CO2 simulation
X1 Flow exponent 0.6 0.65 0.7 [11]
X2 Cd, Discharge coefficient 0.6 0.675 0.75 [12, 13]
X3 Cp, wind pressure coefficient 0 0.5 1 [13]
X4 Wind velocity profile exponent 0.33 0.33 0.4 [11, 13]
X5 Local terrain constant 0.28 0.28 0.40 [11, 13]
X6 Wind speed (m/s) 1.10 2.15 5.20 [14]
X7 Outdoor temperature () 2.5 10.6 25.3 [14]
X8 Front door leakage area (cm2/EA) 24 41.8 248.6 [14-16]
X9
MR 0.42 0.83 1.54
X11 BR2 0 0.38 0.71
X12 LR 0.37 1.17 2.33
X13 Window leakage area (cm2/m2) 1.9 4.3 9 [12, 15]
X14
[7] X15 LR BR1 -2.60 0.50 1.40
X16 LR BR2 -2.60 0.50 1.40
Spearman Coefficient by Simlab 2.2
Building Simulation Lab (bs.skku.ac.kr) 12
Lessons from this case study Household #
CAV [kWh/day]
1 261.8 241.6
2 278.1 257.9
3 296.3 266.2
4 294.8 270.0
5 280.1 260.1
6 257.8 241.4
7 292.5 269.0
8 282.4 265.0
9 262.2 251.7
10 285.8 266.1
11 281.6 264.9
12 289.2 270.8
13 266.6 244.8
14 302.1 275.9
15 290.0 271.6
16 283.5 255.2
17 252.1 231.7
18 316.2 283.7
19 275.2 250.3
20 234.6 219.8
21 243.3 224.1
22 247.7 232.9
23 286.8 266.7
24 250.4 241.4
25 290.2 269.0
26 299.3 268.4
27 294.9 266.7
28 302.1 275.9
29 290.0 271.6
30 283.5 255.2
Min 234.6 219.8
Average 279.0 257.7
Max 316.2 283.7
1. The occupancy pattern is more influential than any other simulation inputs with regard to ventilation simulation.
2. Occupancy pattern is of strong stochastic nature and shouldn’t be treated in a deterministic fashion.
Energy simulation of ventilation system based on measured occupancy pattern
2014-12-08
7
Approach #2: agent simulation
• Agent simulation approach
– simulating the actions and interactions of autonomous agents (either individual or collective organizations or groups).
Figure above: http://www.anylogic.com/agent-based-modeling
Agent simulation: EnergyPlus model
– Desire: 3 types
• 4 occupants: 2 adults, 2 children
2014-12-08
8
Decision-making of agents
Environmental state (t)
Belief on causal relation
( , , , , , ) ( )A i A i i i
Building Simulation Lab (bs.skku.ac.kr) 16
Agent simulation: NetLogo
temperatures
BCVTB: NetLogo + EnergyPlus
2014-12-08
10
TIME AGEN
WINDOW S
A.C. WINDO
- - - -
On All closed 1,168 B mr mr 29.9 Hot On Closed 84
C Outdoor Outdoor - - - - -
D Outdoor Outdoor - - - - -
- - - -
Weakly All closed 1,107 B mr lv 27 Neutral Weakly Closed 57
C Outdoor Outdoor - - - - -
17:30
C Outdoor Outdoor - - - - -
22:00
22:15
C rm1 rm1 26.4 Neutral Off Open 36
D lv lv 25.9 Neutral Off Open 87
22:30
C rm1 rm1 26.3 Neutral Off Open 36
D lv lv 25.9 Neutral Off Open 87
words in underlined italic mean occurrence of the agent’s movement =living room, rm=bed room, mr= master bedroom, A.C.: Air-Conditioner
Building Simulation Lab (bs.skku.ac.kr) 20
Lessons from this study
– Three households (#1, #2, #3) were randomly selected
– a static occupancy pattern used with a constant room air temperature of 26.0oC when occupants are present
• Agent-based simulation:
– occupants’ behavior is reflected based on their perception, desire and belief. on/off appeared
• Significant difference between deterministic vs. agent-based
0 2 4 6 8 10 12 14 16 18 20 22 24 0
1000
2000
3000
4000
5000
6000
Time
Responsive occupants • Occupant’s response to environmental information (CO2)
• Relevance to building energy simulation
• Experiment #1: CO2 information not provided
• Experiment #2: CO2 information provided in real time.
• Experiment #3: CO2 information and its meaning provided
• Experiment #4: fake CO2
information provided (+ 500 ppm)
In-situ experiments
Building Simulation Lab (bs.skku.ac.kr) 23
Lessons from this study EXP. #1 EXP. #2 EXP. #3 EXP. #4
AVERAGE NUMBER OF OCCUPANTS 3.99 4.19 4.05 4.02 MINIMUM (PPM) 700 508 440 455
AVRERAGE (PPM) 1,131 844 688 716 MAXIMUM (PPM) 1,646 1,458 1,080 1,363
STANDARD DEVIATION (PPM) 208 275 217 317 MOST FREQUENT LEVEL (PPM) 1,000-1,099 600-699 900-999 500-599
PROBABILITY EXCEEDING 1,000PPM 76.4% 27.3% 4.2% 25.0%
EXP. #0 EXP. #1 EXP. #2 EXP. #3 EXP. #4
Day1 Day2 Day1 Day2 Day1 Day2 Day1 Day2 Day1 Day2
Energy Use(Kwh) 28.6 31.4 27.4 32.0 30.9 33.4 32.2 38.6 47.2 22.7
Compared to Exp. #0 (%)
Daily Average 30.0 29.7 32.2 35.4 35.0
Air Change/hour 0.70 0.97 1.13 1.89 2.13
< energy simulation results >
• Occupants act differently when information is provided.
• OB can cause the performance gap between simulation prediction and the reality !!!
• Cognitive response of occupants must be reflected in BS.
Building Simulation Lab (bs.skku.ac.kr) 24
Approach #3: Random walk
Is the above occupancy pattern predictable or not ?
• Observed occupancy pattern: seven different offices in the same building located in Vienna for a reference work day (Mahdavi et al. 2008)
Figure source: Mahdavi, A., Mohammadi, A., Kabir, E. and Lambeva, L., 2008. Occupants' operation of lighting and shading systems in office buildings. Journal of Building Performance Simulation, 1 (1), 57-65
2014-12-08
13
Random walk hypothesis
• A random walk is a sequence of random steps
• E.g.: search path of a foraging animal, a fluctuating stock price, a financial status of a gambler
1D 2D 3D
Building Simulation Lab (bs.skku.ac.kr) 26
Testing random walk hypothesis • H1: occupancy pattern in a room follows
random walk
• Frequency domain: Normalized Cumulative Periodogram (NCP)
1n n nx x w ACF
NCP
2014-12-08
14
Experiments Exp. A U lab. 06/20/2012
Exp. B BS lab. 02/26/2013
Exp. C, D Univ. Library 10/22/2012
CD
Observed data
Exp. A
Exp. B
2014-12-08
15
Observed data (cont.)
Auto-Correlation Function
nx nw nx nw
is random and can’t be predicted !!nw is not random, having a certain periodicity!!nx
2014-12-08
16
Normalized Cumulative Periodogram
nx nw nx nw
is random and can’t be predicted !!nw is not random, having a certain periodicity!!nx
Building Simulation Lab (bs.skku.ac.kr) 32
Conclusions
Stochastic (Markov)
Task #1: perception, desire, intention, sensation…… Task #2: conflict between agents
Agent 1n n nx x w
1n n nX P X
Random walk
Accurate modeling of occupant’s behavior is strongly required for better building performance prediction and simulation!!
Pn = f(perception, interaction … )
Q&A