Framework for Load Power Consumption in HANs Using Machine Learning and IoT Assistance

In home area networks (HANs), many appliances share a power distribution network and all are potentially the cause and victims of sudden current, voltage, and power spikes. This article proposes a monitoring framework to protect the devices and the network against damage and to optimize power consumption. The method proposed in this article gives way for the use of the smart sensor for a cluster of loads, where the subroutines of every load are logged with separate data preamble size set. Researchers study and evaluate two machine learning (ML) algorithms, support vector machine and k-means clustering, for identifying anomalies and misbehavior, and find that support vector machines seem to be better suited for this application.