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Short-term Load Forecasting Based on Load Profiling

2013-07Conference object Restricted access
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dc.contributor.authorRamos, Sérgio
dc.contributor.authorSoares, João
dc.contributor.authorVale, Zita
dc.contributor.authorRamos, Sandra
dc.date.accessioned2015-05-04T15:36:32Z
dc.date.available2015-05-04T15:36:32Z
dc.date.issued2013-07
dc.description.abstractLoad forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.por
dc.identifier.doi10.1109/PESMG.2013.6672439
dc.identifier.urihttp://hdl.handle.net/10400.22/5899
dc.language.isoengpor
dc.publisherIEEEpor
dc.relation.ispartofseriesPES;2013
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6672439&queryText%3DShort-term+Load+Forecasting+Based+on+Load+Profilingpor
dc.subjectLoad forecastingpor
dc.subjectNeural Networkspor
dc.subjectExponential smoothingpor
dc.subjectLoad profilingpor
dc.titleShort-term Load Forecasting Based on Load Profilingpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceVancouver, British Columbia, Canadapor
oaire.citation.endPage25por
oaire.citation.startPage21por
oaire.citation.title2013 IEEE PES GMpor
person.familyNameCarvalho Ramos
person.familyNameSoares
person.familyNameVale
person.givenNameSérgio Filipe
person.givenNameJoão
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-id6D1F-C495-6660
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-1120-5656
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
rcaap.rightsclosedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicationf01a54a0-e6c0-4cf3-afd8-5a664bbac7b4
relation.isAuthorOfPublication9ece308b-6d79-4cec-af91-f2278dcc47eb
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryf01a54a0-e6c0-4cf3-afd8-5a664bbac7b4

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