Support Vector Machine Binary Classifiers of Home Presence Using Active Power

Abstract

The intelligent analysis of electrical parameters has been facilitated by the Internet of Things (IoT), with capabilities to access a lot of data with customized sampling times. On the contrary, binary classifiers using support vector machines (SVM) resolve nonlinear cases through kernel functions. This work presents two binary classifiers of presence in the home using total household active power data obtained from the automated reading of an IoT device. The classifiers consisted of SVM using kernel functions, a linear function, and a nonlinear function. The data was acquired with the Emporia Gen 2 Vue energy monitor for 20 days without interruption, obtaining averaged readings every 15 min. Of these data, 75% was for training the classifiers, and the rest of the data was for validation. Contrary to expectations, the evaluation yielded accuracies of 91.67% for the nonlinear SVM and 92.71% for the linear SVM, concluding that there was similar performance.

Keywords

• support vector machine
• Internet of Things
• kernel functions
• active power
• home presence