  Home absence detection is an emerging field on smart home installations.
Identifying whether or not the residents of the house are present, is important
in numerous scenarios. Possible scenarios include but are not limited to:
elderly people living alone, people suffering from dementia, home quarantine.
The majority of published papers focus on either pressure / door sensors or
cameras in order to detect outing events. Although the aforementioned
approaches provide solid results, they are intrusive and require modifications
for sensor placement. In our work, appliance electrical use is investigated as
a means for detecting the presence or absence of residents. The energy use is
the result of power disaggregation, a non intrusive / non invasive sensing
method. Since a dataset providing energy data and ground truth for home absence
is not available, artificial outing events were introduced on the UK-DALE
dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several
machine learning algorithms were evaluated using the generated dataset.
Benchmark results have shown that home absence detection using appliance power
consumption is feasible.
