- -

Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

Show simple item record

Files in this item

dc.contributor.author Hernandez, Luis es_ES
dc.contributor.author Baladron, Carlos es_ES
dc.contributor.author Aguiar, Javier M. es_ES
dc.contributor.author Carro, Belen es_ES
dc.contributor.author Sanchez-Esguevillas, Antonio es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2015-09-14T15:18:14Z
dc.date.available 2015-09-14T15:18:14Z
dc.date.issued 2014-10-01
dc.identifier.issn 0360-5442
dc.identifier.uri http://hdl.handle.net/10251/54606
dc.description.abstract The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Energy es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial neural network es_ES
dc.subject Short-term load forecasting es_ES
dc.subject Microgrid es_ES
dc.subject Pattern recognition es_ES
dc.subject Self-organizing map es_ES
dc.subject k-Means algorithm es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.energy.2014.07.065
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 Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.energy.2014.07.065 es_ES
dc.description.upvformatpinicio 252 es_ES
dc.description.upvformatpfin 264 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 75 es_ES
dc.relation.senia 287606


This item appears in the following Collection(s)

Show simple item record