摘要
窃电对社会和经济发展造成了重大损害。如何基于电力大数据来检测用户恶意窃电行为,已受到学术界和工业界的广泛关注。针对传统方法依赖于手工特征、行为序列表征不足和检测精度差等问题,提出了一种基于多头注意力机制的窃电检测模型(Electricity Theft Detection Based on Multi-Head Attention, ETD-MHA)。该模型基于双向门控循环神经网络(Bidirectional Gated Recurrent Unit, BiGRU)充分捕获用户用电行为序列的时序特征,引入多头注意力机制来进一步增强关键特征的区分度,并通过加深网络来提高学习效果。在爱尔兰和中国国家电网智能电表数据集上进行了大量的实验,结果表明,相比传统的逻辑回归(Linear Regression, LR)、支持向量机(Support Vector Machine, SVM)、随机森林(Random Forest, RF)等多种算法,所提模型展现出了明显的优势。例如,在爱尔兰智能电表数据集上,其AUC值相比LR算法最高提升了34.6%。
Electricity theft causes significant damage to social and economic development.How to detect malicious electricity theft based on power big data has been widely concerned by academia and industry.Aiming at the problems of traditional methods relying on manual features,insufficient behavior sequence representation,poor detection accuracy,etc.,this paper proposes an electricity theft detection model based on multi-head attention mechanism (ETD-MHA).The bidirectional gated recurrent unit is used to fully capture the time features of the electricity consumption behavior sequence,and the distinction of key features is gradually enhanced in the multi-head attention mechanism,and finally,the learning effect is improved by deepening the networks.Extended experiments are conducted on the smart meter datasets of Ireland and China State Grid.The results show that the proposed method achieves better performance compared with the linear regression (LR),support vector machine(SVM),random forest(RF),and other traditional algorithms.For example,the AUC value of the proposed model is improved by up to 34.6%compared to the LR algorithm.
作者
肖丁
张玙璠
纪厚业
XIAO Ding;ZHANG Yu-fan;JI Hou-ye(School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《计算机科学》
CSCD
北大核心
2022年第1期140-145,共6页
Computer Science
基金
国家自然科学基金(U20B2045,61772082)。
关键词
智能电网
窃电检测
深度学习
门控循环神经网络
多头注意力机制
Smart grids
Electricity theft detection
Deep learning
Gated recurrent neural network
Multi-head attention mechanism