Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case
  Study   Detecting inaccurate smart meters and targeting them for replacement can save
significant resources. For this purpose, a novel deep-learning method was
developed based on long short-term memory (LSTM) and a modified convolutional
neural network (CNN) to predict electricity usage trajectories based on
historical data. From the significant difference between the predicted
trajectory and the observed one, the meters that cannot measure electricity
accurately are located. In a case study, a proof of principle was demonstrated
in detecting inaccurate meters with high accuracy for practical usage to
prevent unnecessary replacement and increase the service life span of smart
meters.

25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn