A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison

• We developed a probabilistic model which generates realistic occupancy sequences.
• We identified seven characteristic occupancy patterns using hierarchical clustering.
• Calibrating the model to these patterns limits the variability of the sequences.
• The method enables to create synthetic peers as a basis for feedback on energy use.
• The methodology can easily be repeated for different countries.

User behaviour plays a key role in the energy demand of residential buildings, and its importance will only increase when moving towards nearly Zero-Energy homes. However, little detailed information is available on how users interact with their homes. Due to the lack of information, user behaviour is often included in building performance simulations through one standard user pattern. To obtain more accurate energy demand simulations, user patterns are needed that capture the wide variations in behaviour without making simulations overly complicated. To this end, we developed a probabilistic model which generates realistic occupancy sequences that include three possible states: (1) at home and awake, (2) sleeping or (3) absent. This paper reports on the methodology used to construct this occupancy model based on the 2005 Belgian time-use survey. Using hierarchical clustering, we were able to identify seven typical occupancy patterns. The modelling of individual occupancy sequences based on this method enables to include highly differentiated yet realistic behaviour that is relevant to building simulations and can be used for individualised feedback based on peer comparison. The model's calibration data is available for download [1].