ELECTRICITY THEFT DETECTION IN SMART GRIDS BASED ON DEEP NEURAL NETWORK

Authors

  • Dr. Mrs.DEEPA PATNAIK, MIRIYALA SINDHU PRIYA, PRIYANKA KUMARI, ELIGETI MEGHANA

Keywords:

Electricity theft detection, smart grids, deep neural networks, Convolutional Neural Networks, Long Short-Term Memory, hybrid models, anomaly detection, smart meters, machine learning, data analytics, non-technical losses, grid security, energy management, predictive modeling, feature extraction, time series analysis, model evaluation, system optimization, real-time monitoring, intelligent systems.

Abstract

Electricity theft poses a significant challenge to the efficiency and sustainability of smart grids, leading to substantial financial losses and operational inefficiencies. Traditional methods of detecting electricity theft often fall short in terms of accuracy and scalability, necessitating the development of more sophisticated approaches.

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References

Kulkarni, Y., Hussain, S. theft Z., Ramamritham, K., & Somu, N. (2021). EnsembleNTLDetect: An intelligent framework for electricity theft detection in smart grid. arXiv preprint arXiv:2110.04502. Retrieved from https://arxiv.org/abs/2110.04502

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Published

2023-12-24

How to Cite

Dr. Mrs.DEEPA PATNAIK, MIRIYALA SINDHU PRIYA, PRIYANKA KUMARI, ELIGETI MEGHANA. (2023). ELECTRICITY THEFT DETECTION IN SMART GRIDS BASED ON DEEP NEURAL NETWORK . Pegem Journal of Education and Instruction, 13(4), 569–576. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4021

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

License

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