Publication: Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms
| dc.contributor.author | Serrano-Luján, Lucía | es_ES |
| dc.contributor.author | Toledo, Carlos | es_ES |
| dc.contributor.author | Colmenar, José Manuel | es_ES |
| dc.contributor.author | Abad, José | es_ES |
| dc.contributor.author | Urbina Yeregui, Antonio | |
| dc.contributor.department | Zientziak | eu |
| dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
| dc.contributor.department | Ciencias | es_ES |
| dc.date.accessioned | 2022-09-07T06:23:20Z | |
| dc.date.available | 2024-06-01T23:00:09Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-09-07T06:11:13Z | |
| dc.description.abstract | Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure's skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days). | en |
| dc.description.sponsorship | This work was supported by Project PID2019-104272RB-C55, funded by Agencia Estatal de Investigación (AEI-MICINN, Spain, including FEDER funds); Spanish MCIU/AEI/FEDER, UE, under grant ref. PGC2018-095322-B-C22; and Comunidad de Madrid Fondos Estructurales de la Unión Europea with grant ref. P2018/TCS-4566. Carlos Toledo is grateful for postdoctoral fellowship 21227/PD/19, Fundación Séneca - Región de Murcia (Spain). | en |
| dc.embargo.lift | 2024-06-01 | |
| dc.embargo.terms | 2024-06-01 | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Serrano-Luján, L.; Toledo, C.; Colmenar, J. M.; Abad, J.; Urbina-Yeregui, A.. (2022). Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms. Applied Energy. 315, . | en |
| dc.identifier.doi | 10.1016/j.apenergy.2022.119015 | |
| dc.identifier.issn | 0306-2619 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/43974 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier Ltd | en |
| dc.relation.ispartof | Applied Energy 315 (2022) 119015 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104272RB-C55/ES/ | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095322-B-C22/ES/ | en |
| dc.relation.publisherversion | https://doi.org/10.1016/j.apenergy.2022.119015 | |
| dc.rights | © 2022 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | BIPV | en |
| dc.subject | Differential evolution | en |
| dc.subject | Grammatical evolution | en |
| dc.subject | Machine learning | en |
| dc.subject | Module temperature estimation | en |
| dc.subject | PV module temperature | en |
| dc.title | Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms | en |
| dc.type | Artículo / Artikulua | es |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | b4dc7f62-5824-4def-8434-945c0ca3ca96 | |
| relation.isAuthorOfPublication.latestForDiscovery | b4dc7f62-5824-4def-8434-945c0ca3ca96 |