Detecting the occupancy in households is becoming increasingly important for enabling context-aware applications in smart homes. For example, smart heating systems,
which aim at optimising the heating energy, often use the
occupancy to determine when to heat the home. The occupancy schedule of a household can be inferred from the
electricity consumption, as its changes indicate the presence or absence of inhabitants. As smart meters become
more widespread, the real-time electricity consumption of
households is often available in digital form. For such data,
supervised classifiers are typically employed as occupancy
detection mechanisms. However, these have to be trained
on data labelled with the occupancy ground truth. Labelling
occupancy data requires a high effort, sometimes it even may
be impossible, making it difficult to apply these methods
in real-world settings. Alternatively, one could use unsupervised classifiers, which do not require any labelled data
for training. In this work, we introduce and explain several
unsupervised occupancy detection algorithms. We evaluate
these algorithms by applying them to three publicly available
datasets with ground truth occupancy data, and compare them
to one existing unsupervised classifier and several supervised
classifiers. Two unsupervised algorithms perform the best
and we find that the unsupervised classifiers outperform the
supervised ones we compared to. Interestingly, we achieve a
similar classification performance on coarse-grained aggregated datasets and their fine-grained counterparts.