Abnormal identification of household change relationships based on bidirectional long short-term memory network and temporal convolutional network
In the operation and maintenance of power systems,accurately identifying abnormal household change relationships is crucial to ensuring the stability and efficiency of the power grid.In order to achieve precise recognition of abnormal household change relationships,this paper proposes a novel parallel neural network model based on Bidirectional Long Short-Term Memory(BiLSTM)and Temporal Convolutional Networks(TCN).The goal is to improve the accuracy and efficiency of abnormal identifica-tion.By combining the advantages of BiLSTM and TCN,the proposed model can more effectively handle time series data and cap-ture anomaly patterns.Experimental results show that,compared to traditional LSTM and BiLSTM models,the proposed BiLSTM-TCN parallel neural network model outperforms in terms of recognition accuracy and generalization ability.This research provides an effective technical solution for abnormal monitoring in power systems.
Bidirectional long short-term memory networktemporal convolutional networkhousehold change relationshipsabnormal detection
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