Scholarlink

<p>With the increasing complexity of smart grid data, detecting fraudulent activities such as electricity theft has become increasingly challenging. Smart grids facilitate real-time monitoring, providing a valuable platform for identifying anomalous consumption patterns. To address this issue, we propose a deep learning-based framework that integrates an LSTM-Autoencoder model for electricity theft detection. The model effectively captures long-term temporal dependencies and identifies persistent anomalies, enhancing the standard LSTM architecture’s capability to model sequential data. For robust security and transparency, the framework incorporates blockchain technology, establishing a decentralized logging mechanism that prevents data tampering and ensures a trustworthy audit trail. This integration enables secure, verifiable, and transparent recording of detected anomalies and operational events. Furthermore, the proposed approach is implemented in Python using deep learning frameworks such as TensorFlow and Keras, with optional PyTorch support. Extensive experiments demonstrate that the combined LSTM-Autoencoder and blockchain framework achieves 95% accuracy, outperforming traditional and hybrid detection methods. The solution is scalable, privacy-preserving, and provides a resilient, intelligent, and transparent ecosystem for smart grid operations, offering a significant advancement in electricity theft detection and operational reliability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

查看全文
  • Fadia Bibi,
  • Saif Ur Rehman,
  • Sarfraz Bibi,
  • Kaman Aziz,
  • Ahmad Alshammari,
  • Vincent Karovič

摘要

With the increasing complexity of smart grid data, detecting fraudulent activities such as electricity theft has become increasingly challenging. Smart grids facilitate real-time monitoring, providing a valuable platform for identifying anomalous consumption patterns. To address this issue, we propose a deep learning-based framework that integrates an LSTM-Autoencoder model for electricity theft detection. The model effectively captures long-term temporal dependencies and identifies persistent anomalies, enhancing the standard LSTM architecture’s capability to model sequential data. For robust security and transparency, the framework incorporates blockchain technology, establishing a decentralized logging mechanism that prevents data tampering and ensures a trustworthy audit trail. This integration enables secure, verifiable, and transparent recording of detected anomalies and operational events. Furthermore, the proposed approach is implemented in Python using deep learning frameworks such as TensorFlow and Keras, with optional PyTorch support. Extensive experiments demonstrate that the combined LSTM-Autoencoder and blockchain framework achieves 95% accuracy, outperforming traditional and hybrid detection methods. The solution is scalable, privacy-preserving, and provides a resilient, intelligent, and transparent ecosystem for smart grid operations, offering a significant advancement in electricity theft detection and operational reliability.