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An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context
dc.contributor.author | Díaz-Longueira, Antonio | |
dc.contributor.author | Rubiños, Manuel | |
dc.contributor.author | Arcano-Bea, Paula | |
dc.contributor.author | Calvo-Rolle, José Luis | |
dc.contributor.author | Quintián, Héctor | |
dc.contributor.author | Zayas-Gato, Francisco | |
dc.date.accessioned | 2024-06-04T07:18:39Z | |
dc.date.available | 2024-06-04T07:18:39Z | |
dc.date.issued | 2024-05 | |
dc.identifier.citation | Díaz-Longueira, A.; Rubiños, M.; Arcano-Bea, P.; Calvo-Rolle, J.L.; Quintián, H.; Zayas-Gato, F. An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context. Energies 2024, 17, 2706. https://doi.org/10.3390/en17112706 | es_ES |
dc.identifier.issn | 1996-1073 | |
dc.identifier.uri | http://hdl.handle.net/2183/36792 | |
dc.description.abstract | [Abstract] Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A2023072 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN853C 2022/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2023/49 | es_ES |
dc.description.sponsorship | Interreg Atlantic Area; EAPA_00192022 | es_ES |
dc.description.sponsorship | Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the “Axudas á etapa predoutoral” grant with reference: ED481A2023072. This work has been supported by Xunta de Galicia through Axencia Galega de Innovación (GAIN) by grant IN853C 2022/01, Centro Mixto de Investigación UDC-NAVANTIA “O estaleiro do futuro”, which is ongoing until the end of September 2025. The support was inherited from both the starting and consolidation stages of the same project throughout 2015–2018 and 2018–2021, respectively. This stage is also co-funded by ERDF funds from the EU in the framework of program FEDER Galicia 2021–2027. CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS). Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49). This research is co-fnanced by the Interreg Atlantic Area Programme through the European Regional Development Fund, EAPA_00192022 SAtComm project. | |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation.uri | https://doi.org/10.3390/en17112706 | es_ES |
dc.rights | Creative Commons Attribution (CC BY) license https://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Energy efficiency | es_ES |
dc.subject | Geothermal heat exchanger | es_ES |
dc.subject | Prediction | es_ES |
dc.subject | Random forest | es_ES |
dc.subject | SVR | es_ES |
dc.subject | MLP | es_ES |
dc.title | An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Energies | es_ES |
UDC.volume | 17 | es_ES |
UDC.issue | 11 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 15 | es_ES |
dc.identifier.doi | https://doi.org/10.3390/en17112706 |
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