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Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

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Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

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dc.contributor.author Hernández, Luis es_ES
dc.contributor.author Baladrón Zorita, Carlos es_ES
dc.contributor.author Aguiar Pérez, Javier Manuel es_ES
dc.contributor.author Calavia Domínguez, Lorena es_ES
dc.contributor.author Carro Martínez, Belén es_ES
dc.contributor.author Sanchez-Esguevillas, Antonio es_ES
dc.contributor.author Sanjuan, Javier es_ES
dc.contributor.author Gonzalez, Alvaro es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2014-10-10T15:56:35Z
dc.date.available 2014-10-10T15:56:35Z
dc.date.issued 2013-09
dc.identifier.issn 1996-1073
dc.identifier.uri http://hdl.handle.net/10251/43122
dc.description.abstract Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model. es_ES
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Energies es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject artificial neural network es_ES
dc.subject short-term load forecasting es_ES
dc.subject microgrid es_ES
dc.subject multilayer perceptron es_ES
dc.subject peak load forecasting es_ES
dc.subject valley load forecasting es_ES
dc.subject next day’s total load es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/en6094489
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres es_ES
dc.description.bibliographicCitation Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Sanjuan, J.... (2013). Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment. Energies. 6(9):4489-4507. doi:10.3390/en6094489 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/en6094489 es_ES
dc.description.upvformatpinicio 4489 es_ES
dc.description.upvformatpfin 4507 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 9 es_ES
dc.relation.senia 265802
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