NDLI: Very short-term electricity load demand forecasting using support vector regression

Content Provider	IEEE Xplore Digital Library
Author	Setiawan, A. Koprinska, I. Agelidis, V.G.
Copyright Year	2009
Description	Author affiliation: School of Information Technologies, University of Sydney, NSW 2006, Australia (Koprinska, I.) \|\| School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia (Setiawan, A.; Agelidis, V.G.)
Abstract	In this paper, we present a new approach for very short term electricity load demand forecasting. In particular, we apply support vector regression to predict the load demand every 5 minutes based on historical data from the Australian electricity operator NEMMCO for 2006–2008. The results show that support vector regression is a very promising approach, outperforming backpropagation neural networks, which is the most popular prediction model used by both industry forecasters and researchers. However, it is interesting to note that support vector regression gives similar results to the simpler linear regression and least means squares models. We also discuss the performance of four different feature sets with these prediction models and the application of a correlation-based sub-set feature selection method.
Starting Page	2888
Ending Page	2894
File Size	3931012
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5179063
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Demand forecasting Load forecasting Neural networks Power generation Economic forecasting Vectors Predictive models Security Australia Electricity supply industry
Content Type	Text
Resource Type	Article

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