Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70099
DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineering-
dc.creatorMeng, K-
dc.creatorDong, ZY-
dc.creatorWang, H-
dc.creatorWang, Y-
dc.date.accessioned2017-11-13T02:16:41Z-
dc.date.available2017-11-13T02:16:41Z-
dc.identifier.isbn978-3-642-01506-9-
dc.identifier.isbn978-3-642-01507-6-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10397/70099-
dc.descriptionInternational Symposium on Neural Networks, ISNN 2009, Wuhan, China, 26-29 May 2009en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectElectricity reference price forecastingen_US
dc.subjectSupport vector machineen_US
dc.subjectRelevance vector machineen_US
dc.titleComparisons of machine learning methods for electricity regional reference price forecastingen_US
dc.typeConference Paperen_US
dc.identifier.spage827-
dc.identifier.epage835-
dc.identifier.volume5551-
dc.identifier.doi10.1007/978-3-642-01507-6_93-
dcterms.abstractEffective 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.-
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5551, p. 827-835-
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)-
dcterms.issued2009-
dc.relation.conferenceInternational Symposium on Neural Networks [ISNN]-
dc.identifier.eissn1611-3349-
dc.identifier.rosgroupidr42548-
dc.description.ros2008-2009 > Academic research: refereed > Refereed conference paper-
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