Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

نویسندگان

چکیده

Electricity theft is one of the challenging problems in smart grids. The power utilities around globe face huge economic loss due to ET. traditional electricity detection (ETD) models confront several challenges, such as highly imbalance distribution consumption data, curse dimensionality and inevitable effects non-malicious factors. To cope with aforementioned concerns, this paper presents a novel ETD strategy for grids based on attacks, long short-term memory (LSTM) gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic attacks data balancing, (2) LSTM feature extraction, (3) GRU classification. used drift identification. stores extracts long-term dependency data. beneficial In way, minimum false positive rate (FPR) obtained. Moreover, dropout regularization Adam optimizer are added tackling overfitting trapping model local minima, respectively. proposed TLGRU uses realistic EC profiles Chinese utility state grid corporation China analysis solve problem. From simulation results, it exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, 91.68% area under curve obtained by model. outperforms existing terms ETD.

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ژورنال

عنوان ژورنال: Energies

سال: 2022

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en15082778