Title :
Power decomposition based on SVM regression
Author :
Li, Jiaming ; West, Sam ; Platt, Glenn
Author_Institution :
CSIRO ICT Centre, Australia
Abstract :
For more efficient energy consumption, it is crucial to have accurate information on how power is being consumed through a single power measurement. It benefits both market participants such as retailers, network businesses and also power consumers. This means that the metering device must not only be able to distinguish between different loads on a common circuit, but also decipher their respective power consumption. This paper gives our investigation on power load signature and power decomposition. In particular, the paper focuses on the development of power decomposition algorithm based on support vector machine (SVM) regression, i.e, estimating the power proportion of constant power (CP) loads to constant impedance (CI) loads. The power decomposition algorithm in this paper works on steady-state power load signal.
Keywords :
Power Decomposition; Power Load Classification; Power Load Signature; SVM Regression;
Conference_Titel :
Modelling, Identification & Control (ICMIC), 2012 Proceedings of International Conference on
Conference_Location :
Wuhan, Hubei, China
Print_ISBN :
978-1-4673-1524-1