Appliance recognition using Hall Effect sensors and k-nearest neighbors for power management systems

Power management systems employ appliance recognition such that the burden of manually configuring the system for each appliance is lifted from the user. This research then aims to develop an appliance recognition functionality through current readings gathered from a data acquisition (DAQ) device consisting of Hall Effect current sensors, and through a machine learning classification algorithm called k-nearest neighbors. Ten appliances were tested, comprising of 6,500 samples of test data in the four outlets tested. The average accuracy for the trials is 92.73%. In addition, the appliance recognition functionality was embedded to a cloud-based power management system following an Internet of Things (IoT) architecture. In the end, the developed system can gather data from plugged appliances, perform recognition, and carry out various power management functionalities such as monitoring and appliance-level smart-recommendations.