Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70099
Title: Comparisons of machine learning methods for electricity regional reference price forecasting
Authors: Meng, K
Dong, ZY
Wang, H
Wang, Y
Issue Date: 2009
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5551, p. 827-835
Abstract: Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. In this paper, we investigate two state-of-the-art statistical learning based machine learning techniques for electricity regional reference price forecasting, namely support vector machine (SVM) and relevance vector machine (RVM). The study results achieved show that, the RVM outperforms the SVM in both forecasting accuracy and computational cost.
Keywords: Electricity reference price forecasting
Support vector machine
Relevance vector machine
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 978-3-642-01506-9
978-3-642-01507-6
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-01507-6_93
Description: International Symposium on Neural Networks, ISNN 2009, Wuhan, China, 26-29 May 2009
Appears in Collections:Conference Paper

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