Samenvatting
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.
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.
| Originele taal-2 | English |
|---|---|
| Pagina's (van-tot) | 67-78 |
| Aantal pagina's | 12 |
| Tijdschrift | Building and Environment |
| Volume | 75 |
| Nummer van het tijdschrift | May |
| Status | Published - 2014 |