广西师范大学学报（自然科学版）2026，Vol.44Issue(1)：33-44,12.DOI:10.16088/j.issn.1001-6600.2025030701
基于Bi-LSTM特征融合和FT-FSL的非侵入式负荷辨识
Non-intrusive Load Identification Based on Bi-LSTM Feature Fusion and FT-FSL
摘要
Abstract
Non-intrusive load monitoring(NILM)facilitates the rational energy allocation and fine-grained management using real-time load data monitor and analysis.To improve load identification performance in NILM under conditions of limited labeled data,this paper presents a novel method based on Bi-LSTM feature fusion and fine-tuned few-shot learning(FT-FSL).First,weighted pixel voltage-current(V-I)image features and multidimensional time-frequency sequence features are fused using Bi-LSTM feature fusion method.Then,FT-FSL is employed to enable the load classification model to be trained with only a small number of labeled samples.Finally,the proposed method is evaluated on the PLAID dataset and compared with four mainstream FSL approaches(Matching Network,Prototypical Network,Relation Network,and MAML).Experimental results show that the proposed method achieves an accuracy of 92.46%,outperforming the comparison models by 12.21,4.18,5.90,and 9.04 percentage points,respectively.These results demonstrate the effectiveness of the proposed approach in identifying load types with limited labeled data.关键词
非侵入式负荷监测/负荷辨识/小样本学习/Bi-LSTM/微调Key words
non-intrusive load monitoring/load identification/few-shot learning/Bi-LSTM/fine-tuning分类
信息技术与安全科学引用本文复制引用
张竹露,李华强,刘洋,许立雄..基于Bi-LSTM特征融合和FT-FSL的非侵入式负荷辨识[J].广西师范大学学报（自然科学版）,2026,44(1):33-44,12.基金项目
四川省科技计划项目(2023YFG0132) （2023YFG0132）
