Title :
Energy disaggregation using ensemble of classifiers
Author :
Shahriar, M.S. ; Rahman, Aminur
Author_Institution :
ICT Centre, Intell. Sensing & Syst. Lab. (ISSL), CSIRO, Hobart, TAS, Australia
Abstract :
We study an approach towards energy disaggregation using ensemble of classifiers, a supervised machine learning method. Specifically we identify different appliance loads from the aggregated power usage data. Experimental results on a public data sets show the accuracy of ensemble of classifiers using diverse features in identifying appliance loads.
Keywords :
learning (artificial intelligence); pattern classification; aggregated power usage data; classifiers; energy disaggregation; machine learning; Accuracy; Bagging; Context; Feature extraction; Home appliances; Sensors; Training;
Conference_Titel :
TENCON Spring Conference, 2013 IEEE
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4673-6347-1
DOI :
10.1109/TENCONSpring.2013.6584433
