Nonintrusive Real Time Classification of Home and Office Appliances from Smart Meter by Using Machine Learning Techniques
Noninvasive load monitoring have been investigated by researchers for decades due to its cost-effective benefits. Upon introduction of smart meters, obtaining data about power consumption of households became easier. Numerous different techniques have been applied on the power consumption data to gain useful information out of it. This study applies machine learning techniques (Bayes network, random forest and rotational forest) to determine the operation state of households, where households are assumed to be either in ON or OFF state. Tracebase power consumption signature repository was used to train and test proposed machine learning models. Tracebase dataset was preprocessed to generate 4 different datasets. Test results have shown that these machine learning algorithms are able to estimate operation state with high accuracy and Bayes network shows outstanding performance among them with overall accuracy of 95%. Proposed method is extremely cost-effective for load monitoring and could replace some of the physical sensors in the smart houses.