Autoencoder Based Anomaly Detection of Electricity Theft in House Hold Consumer side of the smart grid

Abstract

Introduction: Theft of electricity continues to be an ongoing problem with serious implications, such as loss of revenue, grid instability, decreased efficiency, and higher likelihoods of system overloads. The covert operation of this act presents a tremendous challenge to global power distribution networks, both to utility companies and consumers as energy needs and expenses keep on growing.

Objectives: The objective of this study is to establish a consistent method for identifying electricity theft in a 19-bus power distribution system. The research targets the detection of energy usage anomalies that could be a sign of fraud.

Methods: The suggested method involves the use of Long Short-Term Memory (LSTM) Autoencoders, which have proved to be efficient in detecting anomalies. The model combines LSTM and Autoencoder techniques for handling time-series data. The method involves creating input sequences, an LSTM Encoder and Decoder, and using anomaly detection methods.

Results: Through model training and anomaly detection, the research renders essential energy theft patterns with the help of simulations. The performance analysis demonstrates the strength of the suggested model for anomaly detection in the power distribution system.

Conclusions: The results indicate that LSTM Autoencoders present a sound framework for electricity theft detection. The proposed method may be applied in different real scenarios, and they can help towards building more reliable and efficient power distribution systems.