Online Identification of Non-Invasive Electric Bicycle Charging Behavior Based on Ⅴ-Ⅰ Trajectory
To prevent the safety hazards,the Ⅴ-Ⅰ trajectory features and improved MobileNetv2 model are used for the online identification of the household charging behavior.The experimental scenarios are designed to validate the model performance from four aspects:sampling rate selection,transfer learning,generalization,and comparison of different networks.Finally,the model is deployed to the computer and the K210 chip.The online recognition system based on the upper computer can accurately identify electric bicycles when charging separatly,and the recognition accuracy is over 98％when charging behavior is mixed with commonly used household loads.
国家科技期刊平台
NSTL
万方数据
