Latest data indicates that the EU will not achieve its energy-efficiency goals if only technology-oriented strategies are deployed and the human dimension is still ignored. Recent studies show that user behaviour can account for high variances in heat, electricity, and water consumption. By providing the occupants with smart and personalized information about their energy consumption, up to 2-7% energy could be saved. The proposed concept uses knowledge based modelling, which shifts the simulation process from generalizing codes and standards towards flexible and adaptive strategies. Throughout this paper, the concept for a Bayesian Network model is introduced which weights, evaluates and combines occupant behaviour parameters via causal relations and reliability schemes. The network will handle three types of data: average distributed building and behaviour data from standards and norms like ASHRAE, distributed data from locally specific simulations and norms and most important, deterministic data about occupant behaviour and needs, provided by the users themselves. Last mentioned data is always treated with evidence and overrides all other datasets. An accompanying parameter study identifies the most influential behaviour characteristics and provides possible structures for databases. The model is highly dynamic and new, updated data can always be implemented in the modelling scheme. The outputs of the Bayesian Network will be scripted and calculated in an EnergyPlus module. A next step is the feedback of the energy impact of user-induced actions back to the occupant. Sociological and psychological theory will contribute to the right communication means to reach the desired behavioural change. A main theory behind the concept is nudging and an occupant, which takes responsibility of his actions.     «

Latest data indicates that the EU will not achieve its energy-efficiency goals if only technology-oriented strategies are deployed and the human dimension is still ignored. Recent studies show that user behaviour can account for high variances in heat, electricity, and water consumption. By providing the occupants with smart and personalized information about their energy consumption, up to 2-7% energy could be saved. The proposed concept uses knowledge based modelling, which shifts the sim...     »