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Improving the identification of RBF predictive models to forecast the Portuguese electricity consumption

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dc.contributor.authorFerreira, P. M.
dc.contributor.authorRuano, Antonio
dc.contributor.authorPestana, Rui
dc.date.accessioned2013-01-30T13:53:51Z
dc.date.available2013-01-30T13:53:51Z
dc.date.issued2010
dc.date.updated2013-01-26T17:02:49Z
dc.description.abstractAbstract The Portuguese power grid company wants to improve the accuracy of the electricity load demand forecast within an horizon of 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present updated results on the identi cation of radial basis function neural network load demand predictive models. The methodology follows the principles already employed by the authors in di erent applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modi ed training criterion, and the model structure (number of neurons and input terms) is evolved using a multi-objective genetic algorithm. The set of goals and objectives used in the model optimisation re ect di erent requirements in the design: obtaining good generalisation ability, good balance between one- step-ahead prediction accuracy and model complexity, and good multi-step prediction accuracy. In this work the prediction horizon was increased, the model tness assessment was altered, and the model structure search space was enlarged. Results are also presented for a predictive nearest neighbour type approach, which establishes a baseline for predictive methods comparison.por
dc.identifier.citationFerreira, P. M.; Ruano, A. E. Pestana, Rui. Improving the identification of RBF predictive models to forecast the Portuguese electricity consumption, Trabalho apresentado em Control Methodologies and Technology for Energy Efficiency, In IFAC Conference on Control Methodologies and Technology for Energy Efficiency (2010), Vilamoura, 2010.por
dc.identifier.isbn9783902661685
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2142
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevier, IFACpor
dc.subjectElectricity load demandpor
dc.subjectRadial basis functionspor
dc.subjectNeural networkspor
dc.subjectPredictionpor
dc.subjectModellingpor
dc.titleImproving the identification of RBF predictive models to forecast the Portuguese electricity consumptionpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceVilamourapor
oaire.citation.endPage6por
oaire.citation.startPage1por
oaire.citation.titleIFAC Conference on Control Methodologies and Technology for Energy Efficiency (2010)por
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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