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Deep learning-based electricity theft prediction in non-smart grid environments

Affiliations
  • 1Department of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.
  • 2Department of Computer Science, Virtual University of Pakistan, Lahore, 51000, Pakistan.
  • 3School of Electrical Engineering, Dept. of Electrical and Electronic Eng. Science, University of Johannesburg, Johannesburg, 2006, South Africa.
  • 4Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia.
  • 5Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
  • 6Hodmas University College, Taleh Area, Mogadishu, Banadir, 521376, Somalia.
  • 7Bridges for Academic Excellence, Tunis, Centre-Ville, 1002, Tunisia.

Published on:

August 21, 2024

Abstract

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling methods such as Random-Under-Sampler (RUS), Synthetic Minority Over-sampling Technique (SMOTE), and Random-Over-Sampler (ROS), an effective solution is proposed. Previous studies indicate that models achieve high precision, recall, and F1 score for the non-theft (0) class, but perform poorly, even achieving 0 %, for the theft (1) class. Through parameter tuning and employing Random-Over-Sampler (ROS), significant improvements in accuracy, precision (89 %), recall (94 %), and F1 score (91 %) for the theft (1) class are achieved. The results demonstrate that the proposed model outperforms existing methods, showcasing its efficacy in detecting electricity theft in non-smart grid environments.

Keywords:

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