A transfer learning based method for evaluating the operational status of smart energy meters has been developed to address the issues of single dimensions and imbalanced on-site state data.Firstly,the operational data,environmental data,and attribute data of the electric energy meter under different operating states are obtained through experimental simulation.The Adaboost algorithm is used to construct the correlation model between operational features,environmental features,attribute features,and operational states.Secondly,the Adaboost evaluation model established in the laboratory is adaptively adjusted through transfer learning to be suitable for evaluating the actual operating status of electric energy meters on site.The results show that the proposed method has good recognition performance in precision,recall,and harmonic score indicators of intelligent energy meter state recognition.
关键词
智能电能表/状态评估/迁移学习/Adaboost算法
Key words
smart energy meter/state assessment/transfer learning/Adaboost algorithm
维普
万方数据