  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.
