1. Multi-Agent Stochastic Simulation of Occupants’ Behaviours
Darren Robinson
Sheffield School of Architecture

2. 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.
29/05/2018
© The University of Sheffield

3. Methods Three modelling tools: Bernoulli process
Discrete time random process: Markov chain
Continuous time random process: Survival analysis
Applying:
Forward selection
(Cluster analysis)
k-fold cross validation

4. Presence Short-term Presence profile: Pij(t)
Current models have duration of a single presence as 4hours (am or pm)….often less than this.
Red = simulated, blue=measured.
Page, Robinson, Morel and Scartezzini, Energy & Buildings 40(2), 2008
(5th most cited paper: )

5. Activities Aggregate activity model: Pj(t) & Dj(t) [UK]
Simulated and observed plot for one model only as an example for the aggregated population.
WP means washing appliance.

6. Is culture important? American TUS! Measurements in France
Simulated and observed plot for one model only as an example for the aggregated population.
WP means washing appliance.
Measurements in France

7. Model robustness: model applied to Germany, France, & Spain

8. Appliance
Activity-dependent appliance modelling: D(t) & Pij(t...t+D) | P(t)=1
Duration and power transition given that appliance is on

9. Windows Window openings: Pij(occ), Dj | P(t)=1
Haldi and Robinson, Building and Environment : 44(12), 2009
Best Paper Prize: 2009

10. 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.

11. Blinds Blind position: Pij(t)… Haldi and Robinson, JBPS : 3(2), 2010
Best Paper Prize:

12. Lights Lights (Lightswitch 2002): Pj(t)… Hunt Pigg
On arrival switch-on probabilities
Measured switch-off probabilities as a function of absence duration
Pigg
Reinhart
Within-day switch-on probabilities

13. 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

14. Example results: two collocated office occupants [distributions]
The left graph presents stochacity due to variations in models, for a house and office in two locations (Nottingham, Geneva)
On the right we have variations due to archetypical window models.
Monthly heating energy demands: office
♯window openings for different agents

15. Example results: interacting collocated occupants
Managing negotiations
Weighted Voting System
Window opening
Agent a
Likes window open
Agent b
Opens window less
Action with most votes takes place
AGENT B CURTAILS WINDOW OPENING DURATION WHEN MOBILE.
The archetypical behaviours create conflict between agents that need to be managed.
We employ a weighted voting conflict system.
Each agent is asigned a power, power wieghted votes are counted and the action with most votes wins the action
Here agent a prefers the window open, even with the heating on.
Agent b prefers the window closed most of the year.
Different weights give different results, however the voting system appears to do a good job at managing the conflict with results generally a compromise between the two. a b
a>b
a<b

16. Example results: DSM
Sancho-Tomás, Chapman and Robinson, Proc. Building Simulation 2017

17. 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!