计算技术与自动化2025，Vol.44Issue(3)：36-43,8.DOI:10.16339/j.cnki.jsjsyzdh.202503007
蜣螂算法优化CNN-LSTM-AT模型的异常用电行为检测方法
Abnormal Electrical Behavior Detection Method of DBO-CNN-LSTM Based on Attention Mechanism
摘要
Abstract
Aiming at the problem that the prediction accuracy of the electricity consumption model is not high,resulting in low anomaly detection efficiency,this paper proposes a method for hyperparameter selection of convolutional neural net-work(CNN)-long short-term memory network(LSTM)-attention mechanism(AT)model using dung beetle optimization algorithm(DBO).Firstly,after the user's electricity data is processed,the CNN extracts the features of the data,which is used as the input of the LSTM to analyze the time series,and the attention mechanism extracts the important features of the hidden state of the LSTM,ignores the useless features,and gradually improves the prediction accuracy.Secondly,the DBO algorithm is used to optimize the size of the feature detector of the CNN convolutional layer,the neuron size of the LSTM network and the size of Dropout,so as to improve the performance of the model,and then compare the prediction results with the user's power consumption data and make abnormal judgment.Finally,the proposed method is experimented on the electricity consumption dataset of the office area,and the effectiveness of the proposed method is verified by experiments.关键词
异常用电/蜣螂优化算法/注意力机制/卷积神经网络/长短期记忆网络Key words
abnormal electricity consumption/dung beetle optimization algorithm/attention mechanisms/convolutional neural networks/long short-term memory networks分类
信息技术与安全科学引用本文复制引用
于多,钱承山,毛伟民..蜣螂算法优化CNN-LSTM-AT模型的异常用电行为检测方法[J].计算技术与自动化,2025,44(3):36-43,8.基金项目
江苏省科技厅科技项目(FZ20220289) （FZ20220289）
江苏省研究生实践创新计划资助项目(SJCX23_0397) （SJCX23_0397）
