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

• Published in: IEEE design and test Vol. 38; no. 4; pp. 102 - 108
• Main Authors: Manimuthu, Arunmozhi, Dharshini, Venugopal
• Format: Magazine Article
• Language: English
• Published: Piscataway IEEE Computer Society 01-08-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Cluster analysis; Clustering; Electric power distribution; Electronic devices; Embedded systems; Energy; Gateway; Indexes; Intelligent sensors; IoT; Machine Learning; Monitoring; Ovens; Power consumption; Power demand; Power management; Smart grid; Support vector machines; Training; Vector quantization
• ISSN: 2168-2356; 2168-2364
• DOI: 10.1109/MDAT.2020.3021029

Editor's notes: In home area networks, 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 authors study and evaluate two machine learning 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. - Axel Jantsch, Royal Institute of Technology