在非智能电网环境中基于深度学习的电力盗窃预测
Sheikh Muhammad Saqib1, Tehseen Mazhar2, Muhammad Iqbal1
1Department of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.
Heliyon
|August 21, 2024
概括
这项研究开发了一种轻量级的深度学习模型,用于在没有智能电网的地区检测电力盗窃. 该模型显著提高了盗窃检测率,超过了以前的方法.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 发展中国家往往缺乏智能电网,导致由于电力盗窃导致的电力供应问题很大.
- 现有的电力盗窃检测模型在盗窃类别的准确性方面扎,尽管非盗窃实例的性能很高.
研究的目的:
- 提出一个轻量级的深度学习模型,用于在非智能电网环境中有效检测电力盗窃.
- 提高电力盗窃类别的检测准确度,该类别通常代表性不足,检测不良.
主要方法:
- 利用每月的客户电力读数作为轻量级深度学习模型的输入.
- 采用先进的特征工程技术:主要组件分析 (PCA),t分布式静态邻居嵌入 (t-SNE) 和统一的多重近似和投影 (UMAP).
- 应用了重新采样技术,包括随机低采样 (RUS),合成少数人过量采样技术 (SMOTE) 和随机过量采样 (ROS),重点关注不平衡数据的ROS.
主要成果:
- 在检测电力盗窃类 (类1) 中取得了显著的改进.
- 报告的盗窃类的高性能指标:精度89%,回忆94%,参数调整后和使用随机重采样器 (ROS) 后的F1得分为91%.
- 与电力盗窃检测现有方法相比,拟议的模型表现出优越的性能.
结论:
- 开发的轻量级深度学习模型在缺乏智能电网基础设施的地区有效地检测电力盗窃.
- 特性工程和先进的重新采样技术,特别是ROS,对于改善少数盗窃类别的检测至关重要.
- 这项研究提供了一种可行的解决方案,以减轻发展中国家因电力盗窃造成的供电损失.
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