  Over the past decade, millions of smart meters have been installed by
electricity suppliers worldwide, allowing them to collect a large amount of
electricity consumption data, albeit sampled at a low frequency (one point
every 30min). One of the important challenges these suppliers face is how to
utilize these data to detect the presence/absence of different appliances in
the customers' households. This valuable information can help them provide
personalized offers and recommendations to help customers towards the energy
transition. Appliance detection can be cast as a time series classification
problem. However, the large amount of data combined with the long and variable
length of the consumption series pose challenges when training a classifier. In
this paper, we propose ADF, a framework that uses subsequences of a client
consumption series to detect the presence/absence of appliances. We also
introduce TransApp, a Transformer-based time series classifier that is first
pretrained in a self-supervised way to enhance its performance on appliance
detection tasks. We test our approach on two real datasets, including a
publicly available one. The experimental results with two large real datasets
show that the proposed approach outperforms current solutions, including
state-of-the-art time series classifiers applied to appliance detection. This
paper appeared in VLDB 2024.
