I n d i v i d u a l H o u s e h o l d B e h a v i o u r M o d e l l i n g
527
INDIVIDUAL HOUSEHOLD BEHAVIOUR MODELLING AS A
PRECURSOR FOR ENERGY USE MODELLING
Dorien Aerts
1
, Filip Descamps
1
, Ine Wouters
1
, Joeri Minnen
2
, Ignace Glorieux
2
1
Department of Architectural Engineering, University of Brussels, Belgium
2
TOR, Department of Sociology, University of Brussels, Belgium
Abstract
Household behaviour plays a key role in the energy demand of residential buildings, and
its importance will increase when moving towards nearly zero energy buildings. However,
little information is available on how users interact with their homes. In building
performance simulations, user behaviour is often included through simplified and
undifferentiated user profiles. More detailed information on building occupancy and use
of appliances is required.
In this paper, a model is presented that generates individual household profiles for
residential buildings. The calculation algorithm is calibrated with an extensive Belgian
time-use and household budget dataset describing the activities and whereabouts of
household members. The first aim is to present a three state occupancy model that
produces occupancy patterns for individual household members. The second is to
present an activity model that generates household activity patterns for nine energy-
consuming activities with a time resolution of 10 minutes. The central variables of the
model are occupancy, the probability to start a certain occupancy state and the activity
duration probability.
In order to provide more realistic user profiles for subgroups of the population, we have
defined and analyzed different household types. Since household energy consumption
depends largely on the presence of household members in the building, household types
are defined based on the household structure and the employment type of the adult(s).
These user profiles can be implemented in both stationary energy performance
modelling and dynamic energy balance simulations.
Keywords:
behaviour modelling, occupancy, activity patterns, user profiles, dynamic
energy balance simulation
Introduction
Accurate prediction and modelling methods for energy demand are needed when
moving towards nearly zero energy buildings. Current modelling methods often only
take into account building related characteristics, while the importance of user behaviour
is neglected or included as simplified and undifferentiated user profiles. Since nearly
zero energy buildings will be almost exclusively heated by the sun, the metabolic heat of
its users and the heat emitted from its electrical home appliances, the importance of user
behaviour should not be underestimated and more detailed information on building
occupancy and use of appliances is required. Furthermore, when designing and
operating utility systems on a district level – such as smart-grids or district heating
systems – knowledge on the variability and spread of user behaviour is highly valuable.
User behaviour influences the energy demand of a building on different levels. On the
one hand, the presence of people in a building will lead to passive effects such as
heating or cooling demand, depending on the hygro-thermal conditions in the building.
