dc.creator | Lillo Bravo, Isidoro | es |
dc.creator | Vera-Medina, J. | es |
dc.creator | Fernández-Peruchena, C. | es |
dc.creator | Perez Aparicio, Elena | es |
dc.creator | López Álvarez, José Antonio | es |
dc.creator | Delgado Sánchez, José María | es |
dc.date.accessioned | 2023-09-27T17:17:32Z | |
dc.date.available | 2023-09-27T17:17:32Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Lillo Bravo, I., Vera-Medina, J., Fernández-Peruchena, C., Perez Aparicio, E., López Álvarez, J.A. y Delgado Sánchez, J.M. (2023). Random Forest model to predict solar water heating system performance. Renewable Energy, 216, 119086. https://doi.org/10.1016/j.renene.2023.119086. | |
dc.identifier.issn | 0960-1481 | es |
dc.identifier.uri | https://hdl.handle.net/11441/149185 | |
dc.description | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | es |
dc.description.abstract | This research proposes a Random Forest RF model to replace the experimental tests required by the ISO
9459–5:2007 for predicting the annual energy supplied and the solar fraction covered by a thermosiphon solar
water heating system (TSWHS) for the same locations and daily load volumes that this standard. 38 TSWHS have
been tested according to the procedures outlined in the standard ISO 9459-5 and two more have been selected
from the Solar Keymark database to get the training and testing data set. From these, data from 36 of the TSWHS
were used for RF model training, while data from the remaining four TSWHS were used for its testing. To assess
the performance of the RF model, three statistical indicators were calculated: mean absolute percentage error
(MAPE), mean absolute error (MAE) and the determination coefficient (R-square). Results show MAPE between
2.94% and 5.86% for the annual energy supplied and the solar fraction and R-Square between 0.995 and 0.998
for the annual energy supplied and between 0.973 and 0.976 for the solar fraction for all locations and daily load
volume. Consequently, the RF model could be used successfully to replace the experimental tests required by the
Standard. | es |
dc.format | application/pdf | es |
dc.format.extent | 9 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Renewable Energy, 216, 119086. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Solar thermal energy systems | es |
dc.subject | Artificial intelligence | es |
dc.subject | Random Forest | es |
dc.subject | Solar water heating | es |
dc.title | Random Forest model to predict solar water heating system performance | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Energética | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Física Aplicada I | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0960148123010005 | es |
dc.identifier.doi | 10.1016/j.renene.2023.119086 | es |
dc.contributor.group | Universidad de Sevilla. TEP122: Termodinámica y Energías Renovables | es |
dc.journaltitle | Renewable Energy | es |
dc.publication.volumen | 216 | es |
dc.publication.initialPage | 119086 | es |