An Incremental Learning Scheme with Adaptive Earlystopping for AMI Datastream Processing

Streaming data on power usage delivered through the Advanced Metering Infrastructure (AMI) inherent the concept drift problem, in which the shape of the data changes over time. This phenomenon causes performance degradation in the processing of AMI data using deep learning models. In order to overcome this, updates of deep learning (DL) models using dataflow in real-time should be performed. Although there have been many studies tried to handle the issue so far, the problem with existing methodologies is that they have not fully considered the factors that affect the training efficiency of online learning in an environment where concept drift exists.