基于Bi-LSTM特征融合和FT-FSL的非侵入式负荷辨识OA

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.