  Detecting behind-the-meter (BTM) equipment and major appliances at the
residential level and tracking their changes in real time is important for
aggregators and traditional electricity utilities. In our previous work, we
developed a systematic solution called IRMAC to identify residential users' BTM
equipment and applications from their imported energy data. As a part of IRMAC,
a Similarity Profile (SP) was proposed for dimensionality reduction and
extracting self-join similarity from the end users' daily electricity usage
data. The proposed SP calculation, however, was computationally expensive and
required a significant amount of memory at the user's end. To realise the
benefits of edge computing, in this paper, we propose and assess three
computationally-efficient updating solutions, namely additive, fixed memory,
and codebook-based updating methods. Extensive simulation studies are carried
out using real PV users' data to evaluate the performance of the proposed
methods in identifying PV users, tracking changes in real time, and examining
memory usage. We found that the Codebook-based solution reduces more than 30\%
of the required memory without compromising the performance of extracting
users' features. When the end users' data storage and computation speed are
concerned, the fixed-memory method outperforms the others. In terms of tracking
the changes, different variations of the fixed-memory method show various
inertia levels, making them suitable for different applications.
