An Intelligent Algorithm for Non-Intrusive Appliance Load Monitoring System

Monitoring electrical power consumption has become an important research issue in order to reduce electricity expense and avoid unnecessary electrical operation. This study is based on the architecture of Non-intrusive load monitoring (NILM) system to monitor the operation status of home appliances. The proposed system automatically identifies the electrical appliance in starting status. This method takes advantage of the combination of the fuzzy theory and neural network theory for system identification. The measurement of the RMS voltage, RMS current, RMS active power and the RMS apparent power are used for feature extraction by using a simple home Smart Meter. In this study, it shows 100% recognition rate in three experiments with different hybrid starting procedure.