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

Authors:
Anthony Setiawan;Irena Koprinska;Vassilios G. Agelidis
Affiliations:
School of Electrical and Information Engineering, University of Sydney, NSW, Australia;School of Information Technologies, University of Sydney, NSW, Australia;School of Information and Electrical Engineering, University of Sydney, NSW, Australia
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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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.