  Modern power systems are experiencing the challenge of high uncertainty with
the increasing penetration of renewable energy resources and the
electrification of heating systems. In this paradigm shift, understanding
electricity users' demand is of utmost value to retailers, aggregators, and
policymakers. However, behind-the-meter (BTM) equipment and appliances at the
household level are unknown to the other stakeholders mainly due to privacy
concerns and tight regulations. In this paper, we seek to identify residential
consumers based on their BTM equipment, mainly rooftop photovoltaic (PV)
systems and electric heating, using imported/purchased energy data from utility
meters. To solve this problem with an interpretable, fast, secure, and
maintainable solution, we propose an integrated method called Interpretable
Refined Motifs And binary Classification (IRMAC). The proposed method comprises
a novel shape-based pattern extraction technique, called Refined Motif (RM)
discovery, and a single-neuron classifier. The first part extracts a
sub-pattern from the long time series considering the frequency of occurrences,
average dissimilarity, and time dynamics while emphasising specific times with
annotated distances. The second part identifies users' types with linear
complexity while preserving the transparency of the algorithms. With the real
data from Australia and Denmark, the proposed method is tested and verified in
identifying PV owners and electrical heating system users.
