A hybrid model for smart grid theft detection based on deep learning
A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network(AlexNet)to tackle the curse of dimensionality,sig-nificantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differen-tiating between normal and abnormal electricity usage using adaptive boosting(AdaBoost).To resolve the issue of class imbalance,undersampling techniques were utilized,ensuring balanced performance across various data classes.Additionally,the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet,effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models,this hybrid model achieves accuracy,precision,recall,F1-score,Matthews correlation coefficient(MCC),and area under the curve-receiver op-erating characteristic curve(AUC-ROC)scores of 88%,86%,84%,85%,78%,and 91%,respectively.The proposed model not only increases the accuracy of electricity usage monitoring,but also offers a new perspective for intelligent analysis in power systems.
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