Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality

TL;DR

This paper introduces a Bayesian network-based method to model and simulate occupant behaviors affecting indoor air quality in office buildings, integrating probabilistic cause-effect relations with observational data.

Contribution

It presents a novel approach using Bayesian networks to simulate human behavior and its impact on indoor air quality in office environments.

Findings

Effective co-simulation of CO2 levels and occupant behavior

Probabilistic modeling captures human decision-making

Method integrates knowledge and observational data

Abstract

This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour with probabilistic cause-effect relations based on knowledge, but also with conditional probabilities coming either from knowledge or deduced from observations. This approach has been applied to the co-simulation of the CO2 concentration in an office coupled with human behaviour.