Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters

TL;DR

This paper introduces a deep recurrent neural network-based electricity theft detector for smart grids, utilizing time series analysis and random hyper-parameter tuning to improve detection accuracy over existing methods.

Contribution

It presents a generalized RNN-based detector with random hyper-parameter tuning, outperforming shallow and static detectors in electricity theft detection within AMI networks.

Findings

Superior detection performance compared to state-of-the-art methods

Effective use of time series data for theft detection

Hyper-parameter random tuning enhances model accuracy

Abstract

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, hence, improving the detection performance. In addition, the proposed RNN-based detector adopts a random search analysis in its learning stage to appropriately fine-tune its hyper-parameters. Extensive test studies are carried out to investigate the detector's performance using publicly available real data of 107,200 energy consumption days from 200 customers. Simulation results demonstrate the superior performance of the proposed detector compared with state-of-the-art electricity theft detectors.