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data. It surpasses the available model in accuracy
and false positive rate. Additionally, we consider the
time series data factor through LSTM, unlike other
proposed models. Therefore, it is a first of its kind
for anomaly detection of smart meter data, keeping in
mind their resource constrained nature. In the near
future, we would focus more on the causes of anoma-
lies like anomalies due to faulty meter and anomalies
caused by theft using LSTM-DAE. Thereby, specifi-
cally focusing on anomaly due to attacks and not due
to meter faults.
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