Multi-Agent Stochastic Simulation of
Occupants’ Behaviours
Darren Robinson
Sheffield School of Architecture
Stochastic simulation
• Peoples’ decisions depend on both deterministic
and random responses to stimuli: they are
stochastic in nature.
• The same occupant may respond differently, on
different occasions, even in response to identical
stimuli.
• We may also encounter considerable differences in
response between individuals to identical stimuli.
• This randomness can have significant implications
for comfort and for buildings’ energy and other
resource demands.
14/07/2017
© The University of Sheffield
2
Methods
• Three modelling tools:
• Bernoulli process
• Discrete time random process: Markov chain
• Continuous time random process: Survival analysis
24°C
24.5°C
25°C
25.5°C
26°C
26.5°C
27°C
27.5°C
28°C
28.5°C
29°C
29.5°C
30°C
80
100
0.8
0.9
1.0
21.5°C
22°C
22.5°C
23°C
23.5°C
0.6
0.7
Fitted survival function
1.1
19°C
19.5°C
20°C
20.5°C
21°C
0
20
40
60
Follow-up time (min.)
• Applying:
• Forward selection
• (Cluster analysis)
• k-fold cross validation
120
Presence
Short-term Presence profile: Pij(t)
Page, Robinson, Morel and Scartezzini, Energy & Buildings 40(2), 2008
(5th most cited paper: 2008-13)
5
Activities
Aggregate activity model: Pj(t) & Dj(t) [UK]
Home activities TUD
1
sleep
passive
audio/visual
IT
cooking
cleaning
other washing
metabolic
WP
Probability
0.8
0.6
0.4
0.2
0
5
10
15
20
Time
Home activities Simulation
1
sleep
passive
audio/visual
IT
cooking
cleaning
other washing
metabolic
WP
Probability
0.8
0.6
0.4
0.2
0
5
10
15
Time
20
6
Is culture important?
American TUS!
Measurements in France
Model robustness: model applied to
Germany, France, & Spain
Home activities TUD
1
sleep
passive
audio/visual
IT
cooking
cleaning
other washing
metabolic
washing appliance
Probability
0.8
0.6
0.4
0.2
0
0
18
12
Time
6
0
Home activities Simulation
1
sleep
passive
audio/visual
IT
cooking
cleaning
other washing
metabolic
washing appliance
Probability
0.8
0.6
0.4
0.2
0
Model
Spain
Germany
France
0
18
12
Time
6
0
TPR
FPR
ACC
D
B
0.321
0.114
0.7892
0.124
0.831
0.309
0.136
0.7621
0.130
0.845
0.337
0.106
0.8134
0.128
0.822
Parameters
nt  240
nt  240
nt  240
Appliance
Activity-dependent appliance modelling: D(t) & Pij(t...t+D) | P(t)=1
12000
11000
10000
9000
8000
7000
Wh
6000
5000
4000
3000
2000
1000
0
-1000
CO'sim
CO'ob
MW'sim
MW'ob
TV'sim
TV'ob
WM'sim
WM'ob
Windows
Window openings: Pij(occ), Dj | P(t)=1
Haldi and Robinson, Building and Environment : 44(12), 2009
Best Paper Prize: 2009
Windows: diversity beware!
Conventional behaviour
• Actions increase with qin and qout.
Predicitve thermal behaviours
• Similar, but decreased actions for
high qout to avoid overheating.
Non-thermal behaviours
• almost independent of thermal
stimuli.
Blinds
Blind position: Pij(t)…
Haldi and Robinson, JBPS : 3(2), 2010
Best Paper Prize: 2010-2011
11
Lights
Lights (Lightswitch 2002): Pj(t)…
user does not consider daylight
Hunt
0.8
0.6
1
0.4
user considers daylight
0.2
0
0
100
200
300
400
minimum work plane illuminance [lux]
500
intermediate switch-on probability
On arrival switch-on probabilities
0.03
0.0027+0.017/{-64.19(log10Emin-2.41} for Emin=0
P(Imin)=
1 for Emin=0
switch-off probability when leaving
switch-on probability at arrival
1
0.75
no controls
occ. sensor
indirect dimmed lighting
Pigg
0.5
0.25
0
0.02
<30 minutes
1-2 hours
4-12 hours
24+ hours
30-59 minutes
2-4 hours
12-24 hours
Reinhart
Measured switch-off probabilities as a function of
absence duration
0.01
0
0
100
200
300
400
500
600
minimum work plane illuminance [lux], Emin
Within-day switch-on probabilities
700
No-MASS framework
–Synthetic population generator
–Appliance allocation / use
– Large
– small
–Activities (homes)
–Short absences (workplaces)
–Long absenses
–Location
–Metabolic gains
–Heating use (machine learning)
–Hot water use
–Use of shading
–Use of window
–Use of lights
–Adaptive comfort
–Social Interactions
–BDI rules
–Extension to DSM (and LVN)
Chapman and Robinson, JBPS (under review), 2017
Example results: two collocated
office occupants [distributions]
75
Heating demand (kWh/m2.y)
70
65
60
55
50
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
45
Monthly heating energy
demands: office
♯window openings for different agents
• Action with most votes
takes place
80
75
70
65
60
55
50
Heating demand (kWh/m2.y)
70
65
60
55
• Agent b
Opens window less
50
• Agent a
Likes window open
45
• Window opening
Heating demand (kWh/m2.y)
75
• Weighted Voting System
45
• Managing negotiations
80
Example results: interacting
collocated occupants
013
013
014
a
13
014
> 14
b
13
14>>14
13
14Equal
> 13
a>b a<b
Equal
Example results: DSM
Sancho-Tomás, Chapman and Robinson, Proc. Building Simulation 2017
In conclusion…
• Good progress has been made in modelling:
• Synthetic population generation and attribution
• Presence chains and activities
• Behavioural actions (aggregated): envelope + personal
characteristics
• Appliance ownership and use (homes)
• Social interactions
• We still have lots to do:
•
•
•
•
•
•
•
long-term absences
Use of electrical appliances in workplaces
Completion and validation of DSM framework
Rigorous empirical basis to negotiated behaviour modelling
Population diversity (rigorously)
Adaptive comfort and overheating
Ensemble validation
• But this stuff is fun!