  In recent years, smart meters have been widely adopted by electricity
suppliers to improve the management of the smart grid system. These meters
usually collect energy consumption data at a very low frequency (every 30min),
enabling utilities to bill customers more accurately. To provide more
personalized recommendations, the next step is to detect the appliances owned
by customers, which is a challenging problem, due to the very-low meter reading
frequency. Even though the appliance detection problem can be cast as a time
series classification problem, with many such classifiers having been proposed
in the literature, no study has applied and compared them on this specific
problem. This paper presents an in-depth evaluation and comparison of
state-of-the-art time series classifiers applied to detecting the
presence/absence of diverse appliances in very low-frequency smart meter data.
We report results with five real datasets. We first study the impact of the
detection quality of 13 different appliances using 30min sampled data, and we
subsequently propose an analysis of the possible detection performance gain by
using a higher meter reading frequency. The results indicate that the
performance of current time series classifiers varies significantly. Some of
them, namely deep learning-based classifiers, provide promising results in
terms of accuracy (especially for certain appliances), even using 30min sampled
data, and are scalable to the large smart meter time series collections of
energy consumption data currently available to electricity suppliers.
Nevertheless, our study shows that more work is needed in this area to further
improve the accuracy of the proposed solutions. This paper appeared in ACM
e-Energy 2023.
