Mostrar el registro sencillo del ítem
dc.contributor.author | Hernández Callejo, Luis | |
dc.contributor.author | Baladrón García, Carlos | |
dc.contributor.author | Aguiar Pérez, Javier Manuel | |
dc.contributor.author | Carro Martínez, Belén | |
dc.contributor.author | Sánchez Esguevillas, Antonio Javier | |
dc.contributor.author | Lloret, Jaime | |
dc.date.accessioned | 2024-10-04T11:54:09Z | |
dc.date.available | 2024-10-04T11:54:09Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Energy, October 2014, vol. 75, p. 252-264. | es |
dc.identifier.issn | 0360-5442 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/70424 | |
dc.description | Producción Científica | es |
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. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.classification | Artificial neural network | es |
dc.subject.classification | Short-term load forecasting | es |
dc.subject.classification | Microgrid | es |
dc.subject.classification | Pattern recognition | es |
dc.subject.classification | Self-organizing map | es |
dc.subject.classification | k-Means algorithm | es |
dc.title | Artificial neural networks for short-term load forecasting in microgrids environment | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © Elsevier | es |
dc.identifier.doi | 10.1016/j.energy.2014.07.065 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0360544214008871?via%3Dihub#ack0010 | es |
dc.identifier.publicationfirstpage | 252 | es |
dc.identifier.publicationlastpage | 264 | es |
dc.identifier.publicationtitle | Energy | es |
dc.identifier.publicationvolume | 75 | es |
dc.peerreviewed | SI | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | es |
dc.subject.unesco | 3306 Ingeniería y Tecnología Eléctricas | es |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
La licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional