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. 29/05/2018 © The University of Sheffield

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

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: 2008-13)

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.

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

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

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

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

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

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] 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

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

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!