Discrete occupancy profiles from time-use data for user behaviour modelling in homes

User behaviour plays a key role in the energy demand of residential buildings, and its importance will only increase when moving towards nearly-zero-homes. However, little 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 pro?le. To obtain more accurate building simulations, we need user profiles that capture the wide variations in behaviour without making simulations overly complicated. To this end, we defined realistic occupancy profiles that include three possible states: (1) at home and awake, (2) sleeping or (3) absent. This paper reports on the methodology used to obtain these occupancy profiles based on the 2005 Belgian time-use survey, that contains detailed activity data of 6400 individuals from 3474 households. Using hierarchical clustering, we found seven profiles. These profiles include highly differentiated yet general behaviour that is relevant to building simulations.