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dc.contributor.authorHaroldo, L. M. Do Amaral
dc.contributor.authorSouza, Andre N. De [UNESP]
dc.contributor.authorGastaldello, Danilo S.
dc.contributor.authorPalma, Thiago X. Da S. [UNESP]
dc.contributor.authorMaranho, Alexander Da S. [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.date.accessioned2019-10-06T17:04:39Z
dc.date.available2019-10-06T17:04:39Z
dc.date.issued2019-01-25
dc.identifierhttp://dx.doi.org/10.1109/INDUSCON.2018.8627295
dc.identifier.citation2018 13th IEEE International Conference on Industry Applications, INDUSCON 2018 - Proceedings, p. 85-90.
dc.identifier.urihttp://hdl.handle.net/11449/190173
dc.description.abstractSmart grids are becoming increasingly closer to consumers, especially residential consumers, bringing with them a wide range of possibilities. The level of information obtained on a smart grid will be much higher when compared to a traditional network and at this point, more informed consumers tend to consume more efficiently, bringing benefits to themselves and to the system. An interesting fact for control within a residence is forecasting consumption, allowing the consumer to know in advance how much to consume up to a certain period. Artificial neural networks are one of several methods used to forecast time series, however, require a high volume of historical data for the training of the network, given that these may not be accessible or even exist. At this point, the objective of this work is to evaluate the use of load curves obtained through computational tools for the pre-training of artificial neural networks used in the consumption forecast. A tool is used to create random load curves according to the region and socioeconomic characteristics. The load curves are transformed into cumulative consumption curves and used as training vectors of the artificial neural network. The results of the tests were very promising, they showed that the pretraining with the virtual data makes possible the forecast of the time series even in the absence of real data for the training, showing that the methodology developed has great potential of application in works related to the forecast consumption.en
dc.format.extent85-90
dc.language.isoeng
dc.relation.ispartof2018 13th IEEE International Conference on Industry Applications, INDUSCON 2018 - Proceedings
dc.sourceScopus
dc.subjectArtificial neural networks
dc.subjectLoad curves
dc.subjectLoad forecasting
dc.subjectSmart grids
dc.titleUse of virtual load curves for the training of neural networks for residential electricity consumption forecasting applicationsen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionSacred Heart University - USC
dc.description.affiliationLaboratory of Power Systems and Intelligent Techniques - LSISPOTI University of São Paulo - USP
dc.description.affiliationLaboratory of Power Systems and Intelligent Techniques - LSISPOTI São Paulo State University - UNESP
dc.description.affiliationLaboratory of Power Systems and Intelligent Techniques - LSISPOTI Sacred Heart University - USC
dc.description.affiliationUnespLaboratory of Power Systems and Intelligent Techniques - LSISPOTI São Paulo State University - UNESP
dc.identifier.doi10.1109/INDUSCON.2018.8627295
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-85062540331
unesp.author.lattes8212775960494686[2]
unesp.author.orcid0000-0002-8617-5404[2]
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