Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67534
DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineering-
dc.creatorChai, S-
dc.creatorXu, Z-
dc.creatorLai, LL-
dc.creatorWong, KP-
dc.date.accessioned2017-07-27T08:33:38Z-
dc.date.available2017-07-27T08:33:38Z-
dc.identifier.isbn978-1-4673-7221-3 (electronic)-
dc.identifier.isbn978-1-4673-7220-6 (CD-ROM)-
dc.identifier.urihttp://hdl.handle.net/10397/67534-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectProbabilistic forecasten_US
dc.subjectWind power forecasten_US
dc.subjectPoint forecasten_US
dc.subjectInterval forecasten_US
dc.titleAn overview on wind power forecasting methodsen_US
dc.typeConference Paperen_US
dc.identifier.spage765-
dc.identifier.epage770-
dc.identifier.doi10.1109/ICMLC.2015.7340651-
dcterms.abstractWith the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using reliable forecasting tools. As the conventional point forecasting can only provide a deterministic predicted value, instead, the probabilistic interval forecasting was attracted broad attention in the last few years since it can reflect the information of the uncertainties associated with wind power generation, which can significantly facilitate a large number of decision-making problems in power system operation. This paper presents an overview of current methods used in wind power forecasting. First of all, the frequently-used traditional point forecasting methods are reviewed Afterwards, various state-of-the-art techniques in terms of probabilistic forecasting are discussed. The indications for future development in wind power forecasting approaches and conclusions are given in the end.-
dcterms.bibliographicCitation2015 International Conference on Machine Learning and Cybernetics (ICMLC), No. 1Guangzhou, China, 12-15 July 2015, p.765-770-
dcterms.issued2015-
dc.relation.conferenceInternational Conference on Machine Learning and Cybernetics-
dc.identifier.rosgroupid2015005535-
dc.description.ros2015-2016 > Academic research: refereed > Refereed conference paper-
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