华东师范大学学报（自然科学版）Issue(6)：19-28,10.DOI:10.3969/j.issn.1000-5641.2025.06.003
基于迁移学习与注意力机制混合神经网络的窃电检测
Electricity theft detection based on transfer learning and attention hybrid neural network
摘要
Abstract
In this study,several issues with current electricity theft detection methods are addressed,notably the reliance on one-dimensional electricity load time series data to develop a singular model.These approaches are often plagued by low detection accuracy,and they require extensive training parameters and a significant number of training samples when computer vision models are directly applied to two-dimensional images of electricity load time series.To overcome these challenges,a novel electricity theft detection method that utilizes a hybrid neural network,combining transfer learning and attention mechanisms,is proposed.The training demands of the ConvNeXt model are reduced via the integration of transfer learning,significantly enhancing its performance.Additionally,a bi-directional long short-term memory(BiLSTM)model is integrated to support the training of the refined ConvNeXt model by extracting global nonlinear features from one-dimensional load time-series data.Furthermore,SimAM and multi-headed self-attention(MHSA)mechanisms are incorporated to improve the feature representation capability of the hybrid model.The experimental verification of the proposed method in the China State Grid public dataset shows that AUC,MAP@100,MAP@200,and F1 metrics of the proposed model can be effectively enhanced when compared to those of other deep learning classification models.For example,F1 is improved by 9.1%compared to that obtained via t-LeNet algorithm.关键词
迁移学习/ConvNeXt/双向长短期记忆网络/注意力机制/混合神经网络Key words
transfer learning/ConvNeXt/bi-directional long short-term memory/attention mechanism/hybrid neural network分类
信息技术与安全科学引用本文复制引用
陈李燊,蒲鹏,钱江海..基于迁移学习与注意力机制混合神经网络的窃电检测[J].华东师范大学学报（自然科学版）,2025,(6):19-28,10.基金项目
华东师范大学软硬件协同设计技术与应用教育部工程研究中心开放研究基金(OP202102) （OP202102）
