Saqib Sheikh Muhammad, Mazhar Tehseen, Iqbal Muhammad, Shahazad Tariq, Almogren Ahmad, Ouahada Khmaies, Hamam Habib
Department of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.
Department of Computer Science, Virtual University of Pakistan, Lahore, 51000, Pakistan.
Heliyon. 2024 Jul 26;10(15):e35167. doi: 10.1016/j.heliyon.2024.e35167. eCollection 2024 Aug 15.
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.
在发展中国家，智能电网并不存在，而且窃电行为严重阻碍了电力供应。本研究引入了一种轻量级深度学习模型，该模型将每月客户读数作为输入数据。通过运用精心设计的直接和间接特征工程技术，包括主成分分析（PCA）、t分布随机邻域嵌入（t-SNE）、UMAP（均匀流形近似与投影）以及重采样方法，如随机欠采样器（RUS）、合成少数过采样技术（SMOTE）和随机过采样器（ROS），提出了一种有效的解决方案。先前的研究表明，模型对于非窃电（0）类别能实现高精度、召回率和F1分数，但对于窃电（1）类别表现不佳，甚至达到0%。通过参数调整并采用随机过采样器（ROS），对于窃电（1）类别，在准确率、精确率（89%）、召回率（94%）和F1分数（91%）方面取得了显著提升。结果表明，所提出的模型优于现有方法，展示了其在非智能电网环境中检测窃电行为的有效性。
