bility of the results, the choice of the thresholds and
the choice of the right features is critical but hard to
achieve in general. While one reason might be the
absence of ground truth information another reason
might be the diversity of the consumption patterns of
the households.
This work sets the starting point for holiday detec-
tion and raises a number of technical issues for future
work: modeling and removal of background appli-
ances, choice of thresholds, feature selection, proper
modeling and smoothing of the day-dependent night
distributions, inclusion of other predictive variables
like day of the week and of course evaluation for la-
beled datasets.
Considering the privacy perspective it would be
interesting to investigate possible privacy conse-
quences apart from the detection of secondary resi-
dences.
ACKNOWLEDGEMENTS
The financial support by the Austrian Federal Min-
istry of Science, Research and Economy, the Aus-
trian National Foundation for Research, Technology
and Development and the Federal State of Salzburg is
gratefully acknowledged.
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