Laboratory Anomaly Detection of Electricity Consumption Behavior Based on Stacking Integrated Structures with Different Models
Aiming at the current abnormal electricity consumption behavior in university laboratories,this paper proposes a power anomaly detection method based on Stacking integrated with heterogeneous models.By consid-ering the diversity of the behavioral patterns of electricity usage in the laboratory,the heterogeneous base learners are selected based on their differences.Then,random forest is used as the meta-learner to fully integrate the ad-vantages of the heterogeneous base learners and compensate for their deficiencies,an integrated learning model is constructed based on Stacking heterogeneous model fusion.Finally,through comparative analysis of examples,the results show that the integrated learning model based on Stacking heterogeneous model fusion can effectively improve the classification performance of a single classifier.It outperforms other integrated learning methods such as Bagging,Voting,and Adaboost in terms of accuracy,F,score,area under ROC curve,and false positive rate,and can adapt to imbalanced sample situations.
万方数据
维普
