A hybrid appliance identification method by using grey relational artificial neural network

Nowadays, everything is getting smarter such as mobile phones, cars, watches and home appliances. Our powerlines are also getting smarter. There are many smart grid and smart home applications. Designing of recognition devices to identify appliances for these smart networks is a new task to do it. There are many different approaches on recognition and identification these power consumer devices and appliance. This study aims to develop an effective method that does not require any additional hardware. This method has been developed by using powerline parameters such as current, phase angle, voltage, active and reactive power. These data have been classified and normalized by using a validation method and grey relational analysis to train an artificial neural network. This neural network was trained by using power parameters of many different common appliances like heater, coffee machine, television, radio, lamp, computer, fan, refrigerator etc. This identification algorithm can be used within a low-cost embedded system for collecting appliance information over a powerline to provide info for smart homes and smart grids.