Electrical Devices Identification Through Power Consumption Using Machine Learning Techniques

This research discusses a way to identify electrical devices in real time using intelligent techniques through data analysis. The electrical device identification process is initiated by collecting information related to power consumption of electrical appliances which are used in domestic life. In this the main attention is paid to identify an electrical device quickly with a higher accuracy. And the main objective behind the research is to provide a way to calculate power consumption of electrical devices separately with an intelligent approach with less human involvement. A prototype data acquisition system was implemented to extract parameters such as active power, reactive power, phase shift, root mean square voltage and current from the appliances connected to it. The analysis is done using neural networks, support vector machines, k-means, mean-shift and silhouette classifiers. The purpose of this study is to select the best classifier which produces the optimum results in detecting and identifying electrical appliances in real time from their electric parameters. In the selection of the classifiers for the research, basically the supervised and unsupervised learning criteria were taken into consideration. The selected classifier is used to determine a power consumption pattern (signature) for different electric appliances.