A Deep Neural Network Based on Bayesian Optimizer for Electricity Theft Detection

A Deep Neural Network Based on Bayesian Optimizer for Electricity Theft Detection

Publication Date : 2023-08-05
Author(s) :

Ashna S Kumar, Sujitha R
Conference Name :

International Conference on scientific innovations in Science, Technology, and Management (NGCESl-2023)
Abstract :

Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. It addresses dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. It analyse and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance(mrmr) scheme for validating the most important features. It improves the electricity theft detection performance by optimizing hyper parameters using a Bayesian optimizer and they employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. On validation, had obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.

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