Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:26:03Z
dc.date.accessioned2022-10-05T18:29:22Z
dc.date.available2014-05-27T11:26:03Z
dc.date.available2022-10-05T18:29:22Z
dc.date.created2014-05-27T11:26:03Z
dc.date.issued2011-10-05
dc.identifier2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.
dc.identifierhttp://hdl.handle.net/11449/72741
dc.identifier10.1109/PTC.2011.6019428
dc.identifier2-s2.0-80053350091
dc.identifier7166279400544764
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3921780
dc.description.abstractThis paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.
dc.languageeng
dc.relation2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectArtificial Neural Networks
dc.subjectMoving Average Filter
dc.subjectShort Term Load Forecasting
dc.subjectSignal Processing
dc.subjectTraining Dataset
dc.subjectAbnormal data
dc.subjectElectrical substations
dc.subjectFilter-based
dc.subjectGeneral regression neural network
dc.subjectLoad data
dc.subjectLoad forecasting
dc.subjectMissing data
dc.subjectMoving average filter
dc.subjectNew zealand
dc.subjectForecasting
dc.subjectNeural networks
dc.subjectSignal processing
dc.subjectSustainable development
dc.subjectElectric load forecasting
dc.titlePreprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
dc.typeTrabalho apresentado em evento


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