Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings
This work addresses the problem of accurately simulating occupant behavior for building energy and environmental management, but it appears incremental as it builds on existing Bayesian network methods.
The paper tackles modeling human behavior in office buildings by proposing a Bayesian network framework that integrates expert knowledge and observations, and demonstrates its application in co-simulation to assess CO2 concentration.
This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations. The approach has been used in the co-simulation of building physics and human behaviour in order to assess the CO 2 concentration in an office.