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result of vague descriptions of the respondent’s activities. In the second place, some
time-use surveys register for a whole week whilst other chose to register one weekday
and one weekend day. The Belgian TUS uses a weekday/weekend day registration,
which complicates the transition to weeks since days are unlikely to be identical and the
‘joint’ between two days is never a perfect match. To some extent, this issue is dealt with
in the Belgian TUS diaries by starting registration at 4 AM instead of midnight. Since at 4
AM the vast majority of people are asleep, fewer discontinuities are to be expected when
putting multiple days together.
The conversion from daily patterns to weekly patterns is needed to accurately introduce
the results in dynamic building simulation software such as TRNSYS or EnergyPlus. On
the one hand, the occupancy model can be introduced to steer lighting or heating
schedules. On the other hand, the activities will be converted to energy demand in order
to incorporate dynamic energy demand patterns instead of static electricity load
schedules. The approach will allow designers to base their decisions on more realistic
and diverse behaviour, as well as anticipate on future behaviour changes.
Conclusions
In this paper, a model is presented that generates user profiles for residential buildings.
The model is based on a combined Belgian time-use survey and household budget
survey that was collected in 2005. We present a three state occupancy model that
produces occupancy patterns for individual household members and an activity model
that generates activity patterns for nine energy-consuming activities with a time
resolution of 10 minutes. Household types are defined in order to study the behaviour of
subgroups of the population. Since occupancy largely affects household energy use, we
use household structure and the employment type as key variables to define household
types.
The results from the models are presented on an individual as well as the aggregate
household level. Individual household occupancy and activity results show realistic
patterns, affirming us that the model captures the most important features of household
behaviour modelling. For the verification of the model on the aggregate household level,
we compared the measured data from the TUS and HBS to the modelled data. Close
correlations are found between the aggregated datasets, indicating that the proposed
model adequately reproduces patterns that are representative for the population.
Seasonal effects on household behaviour are briefly addressed. Although there are
some indications that fewer activities occur in summer time, this was not taken into
account in the model since it would have led to very small datasets. Further research is
necessary to determine the seasonal effects thoroughly.
Future work will include the conversion from activities to energy demand and the
integration of the obtained household behaviour patterns in dynamic energy balance
simulations. The aim is to provide designers with more accurate user profiles to be used
in the design and optimization of buildings and to enable them to anticipate on future
behavioural changes.
Acknowledgement
We gratefully acknowledge the financial support received for this work from the Brussels
Institute for Research and Innovation (InnovIris).
References
GLORIEUX, I., MINNEN, J., 2008, ‘
Website Belgisch tijdsbudgetonderzoek
’
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use.be> retrieved on June 20, 2012.
