Logo do repositório
 
Publicação

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

2020Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorCasteleiro-Roca, José-Luis
dc.contributor.authorChamoso, Pablo
dc.contributor.authorJove, Esteban
dc.contributor.authorGonzález-Briones, Alfonso
dc.contributor.authorQuintián, Héctor
dc.contributor.authorFernández-Ibáñez, María-Isabel
dc.contributor.authorVega Vega, Rafael Alejandro
dc.contributor.authorPiñón Pazos, Andrés-José
dc.contributor.authorLópez Vázquez, José Antonio
dc.contributor.authorTorres-Álvarez, Santiago
dc.contributor.authorPinto, Tiago
dc.contributor.authorCalvo-Rolle, Jose Luis
dc.date.accessioned2022-01-11T15:50:43Z
dc.date.available2022-01-11T15:50:43Z
dc.date.issued2020
dc.description.abstractCurrently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/app10134644pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/19400
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/13/4644pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectClusteringpt_PT
dc.subjectPredictionpt_PT
dc.subjectRegressionpt_PT
dc.subjectSolar thermal collectorpt_PT
dc.subjectHybrid modelpt_PT
dc.titleSolar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimizationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue13pt_PT
oaire.citation.startPage4644pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume10pt_PT
person.familyNamePinto
person.givenNameTiago
person.identifierR-000-T7J
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.orcid0000-0001-8248-080X
person.identifier.ridT-2245-2018
person.identifier.scopus-author-id35219107600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublication.latestForDiscovery8d58ddc0-1023-47c0-a005-129d412ce98d

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
ART_GECAD_applsci_2020.pdf
Tamanho:
853.78 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

ISEP – GECAD – Artigos