Research on Abnormal Electricity Identification Based on Weighted Broad Learning System
Aiming at the problem of unbalanced relationship between abnormal electricity consumption and normal electricity consumption sample categories,time-consuming training and lack of scalability of existing models,an abnormal electricity consumption identification model based on Weighted Broad Learning System(WBLS)was proposed.Firstly,considering the class imbalance relationship between samples,the sample weight is used in the objective function to constrain the contribution of each class to the model,and the sample weight is personalized according to the distribution of samples,and the generalized inverse WBLS identification model is established efficiently by ridge regression.Secondly,based on the newly added electricity consumption sample data,the model is updated and reconstructed by the incremental learning algorithm.The experimental results show that the model improves the identification accuracy of abnormal electricity samples,and can quickly update and expand the old model with the increase of electricity samples.
abnormal power consumptionweighted broad learning systemclass imbalanceincremental learning
国家科技期刊平台
NSTL
万方数据
维普
