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Related Experiment Video

Updated: Jul 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Reinforcing smart grid resilience through blockchain-supported deep learning models for theft detection.

Fadia Bibi1, Saif Ur Rehman2, Sarfraz Bibi1

  • 1University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan.

Scientific Reports
|March 18, 2026
PubMed
Summary

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This study introduces a deep learning framework using LSTM-Autoencoder and blockchain to detect electricity theft in smart grids. The innovative approach achieves 95% accuracy, enhancing grid security and operational reliability.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Smart grid complexity increases challenges in detecting electricity theft.
  • Real-time monitoring in smart grids offers opportunities for anomaly detection.
  • Existing methods struggle with the scale and temporal nature of smart grid data.

Purpose of the Study:

  • To develop a deep learning framework for accurate electricity theft detection in smart grids.
  • To enhance anomaly detection by capturing long-term temporal dependencies.
  • To ensure data integrity and transparency through blockchain integration.

Main Methods:

  • Utilized a Long Short-Term Memory-Autoencoder (LSTM-Autoencoder) model for anomaly detection.
  • Integrated blockchain technology for secure, decentralized, and tamper-proof logging.
Keywords:
Blockchain integrationData privacy and integrityElectricity theftLSTM-autoencoderSmart gridTemporal dependencies

Related Experiment Videos

Last Updated: Jul 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K
  • Implemented the framework in Python using TensorFlow and Keras (with optional PyTorch support).

    • Main Results:

    • Achieved 95% accuracy in electricity theft detection.
    • Demonstrated superior performance compared to traditional and hybrid detection methods.
    • Validated the framework's scalability and privacy-preserving capabilities.

    Conclusions:

    • The LSTM-Autoencoder and blockchain framework offers a resilient, intelligent, and transparent solution for smart grid operations.
    • This approach significantly advances electricity theft detection and improves operational reliability.
    • The integrated system provides a trustworthy audit trail for detected anomalies and operational events.