Deep Learning Based Active Power Disaggregation of Appliances for Personalized Energy Feedback System

Abstract

Energy consumption feedback with an appliance-level feedback system can reduce consumption by a maximum of 12%. In this study, we proposed a data acquisition and training framework for configuring deep learning based on Non-Intrusive Load Monitoring (NILM) for a personalized and appliance-level energy consumption feedback system. To construct a training dataset, an aggregation of active power data from four types of home appliances (refrigerator, induction, TV, washing machine) was performed for approximately three weeks. LSTNet was applied to extract and recognize the features of active power data and the state of each home appliance. With an accuracy metric of more than 90% of the disaggregation result, the applicability of the appliance-level active power feedback system was verified.