IEEJ Transactions on Electronics, Information and Systems
Online ISSN : 1348-8155
Print ISSN : 0385-4221
ISSN-L : 0385-4221
Paper
Comparison of Machine Learning Techniques for Estimating the Power Consumption of
Household Electric Appliances
Hiroshi MurataTakashi OnodaKatsuhisa YoshimotoYukio Nakano
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JOURNALS FREE ACCESS

2003 Volume 123 Issue 7 Pages 1350-1355

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Abstract

A non-intrusive monitoring system estimates the behavior of individual electric appliances from the measurement of the total household load demand curve. The total load demand curve is measured at the entrance of the power line into the house. The power consumption of individual appliances can be estimated using several machine learning techniques by analyzing the characteristic frequency contents from the load curve of the hosehold. In this paper, we present results of applying several regression methods such as multi-layered perceptrons (MLP), radial basis function networks (RBFN) and Support Vector regressors (SVR) to estimate the power consumption of an air conditioner. Our experiments show RBFN can achieve the best accuracy for the non-intrusive monitoring system.

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© 2003 by the Institute of Electrical Engineers of Japan
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