Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies.
Resumen:
Building energy modeling tools play a crucial role in quantifying and understanding the energy performance of buildings. These tools require substantial amounts of data, which can be challenging to obtain and are often associated with significant uncertainties. The incorporation of sensitivity analysis is a crucial step toward developing reliable models as it identifies the most critical parameters that require meticulous characterization. In this study, a sensitivity analysis based on the Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modeled in EnergyPlus. Different numbers of Morris trajectories and levels were considered to analyze the impact of the user-defined values of r and p when employing the Morris method. Convergence was achieved at r = 200 and p = 12, which are higher than the typically employed values (r = 10 and p = 4). Roof solar absorptivity, setpoint temperatures, orientation, and the roof conductance rank among the top five most influential parameters affecting thermal loads in all four of the studied typologies. Occupancy was also among the top five most relevant parameters in three of the four typologies.
| 2024 | |
|
Building energy model Sensitivity analysis Building simulation |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
|
https://hdl.handle.net/20.500.12008/45808
https://doi.org/10.3390/buildings14092804 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1872865442585903104 |
|---|---|
| author | Gervaz, Sofía |
| author2 | Favre, Federico |
| author2_role | author |
| author_facet | Gervaz, Sofía Favre, Federico |
| author_role | author |
| bitstream.checksum.fl_str_mv | 6429389a7df7277b72b7924fdc7d47a9 a0ebbeafb9d2ec7cbb19d7137ebc392c a34a400dd0046b3aef60f72471c4f991 71ed42ef0a0b648670f707320be37b90 d2bf452873c11ead7629b6c2d89cc6a2 |
| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
| bitstream.url.fl_str_mv | http://localhost:8080/xmlui/bitstream/20.500.12008/45808/5/license.txt http://localhost:8080/xmlui/bitstream/20.500.12008/45808/2/license_url http://localhost:8080/xmlui/bitstream/20.500.12008/45808/3/license_text http://localhost:8080/xmlui/bitstream/20.500.12008/45808/4/license_rdf http://localhost:8080/xmlui/bitstream/20.500.12008/45808/1/GF24.pdf |
| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Gervaz Sofía, Universidad de la República (Uruguay). Facultad de Ingeniería. Favre Federico, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Gervaz, Sofía Favre, Federico |
| dc.date.accessioned.none.fl_str_mv | 2024-09-11T14:46:43Z |
| dc.date.available.none.fl_str_mv | 2024-09-11T14:46:43Z |
| dc.date.issued.none.fl_str_mv | 2024 |
| dc.description.abstract.none.fl_txt_mv | Building energy modeling tools play a crucial role in quantifying and understanding the energy performance of buildings. These tools require substantial amounts of data, which can be challenging to obtain and are often associated with significant uncertainties. The incorporation of sensitivity analysis is a crucial step toward developing reliable models as it identifies the most critical parameters that require meticulous characterization. In this study, a sensitivity analysis based on the Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modeled in EnergyPlus. Different numbers of Morris trajectories and levels were considered to analyze the impact of the user-defined values of r and p when employing the Morris method. Convergence was achieved at r = 200 and p = 12, which are higher than the typically employed values (r = 10 and p = 4). Roof solar absorptivity, setpoint temperatures, orientation, and the roof conductance rank among the top five most influential parameters affecting thermal loads in all four of the studied typologies. Occupancy was also among the top five most relevant parameters in three of the four typologies. |
| dc.format.extent.es.fl_str_mv | 21 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Gervaz, S. y Favre, F. "Identifying key parameters in building energy models: sensitivity analysis applied to residential typologies". Buildings. [en línea] 2024 vol.14, no. 9, 2804. DOI: https://doi.org/10.3390/buildings14092804. |
| dc.identifier.doi.none.fl_str_mv | https://doi.org/10.3390/buildings14092804 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/45808 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | Buildings, vol.14, no. 9, 2024, 2804. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Building energy model Sensitivity analysis Building simulation |
| dc.title.none.fl_str_mv | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Building energy modeling tools play a crucial role in quantifying and understanding the energy performance of buildings. These tools require substantial amounts of data, which can be challenging to obtain and are often associated with significant uncertainties. The incorporation of sensitivity analysis is a crucial step toward developing reliable models as it identifies the most critical parameters that require meticulous characterization. In this study, a sensitivity analysis based on the Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modeled in EnergyPlus. Different numbers of Morris trajectories and levels were considered to analyze the impact of the user-defined values of r and p when employing the Morris method. Convergence was achieved at r = 200 and p = 12, which are higher than the typically employed values (r = 10 and p = 4). Roof solar absorptivity, setpoint temperatures, orientation, and the roof conductance rank among the top five most influential parameters affecting thermal loads in all four of the studied typologies. Occupancy was also among the top five most relevant parameters in three of the four typologies. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_88230bff06c9134b5f609f7481e9370f |
| identifier_str_mv | Gervaz, S. y Favre, F. "Identifying key parameters in building energy models: sensitivity analysis applied to residential typologies". Buildings. [en línea] 2024 vol.14, no. 9, 2804. DOI: https://doi.org/10.3390/buildings14092804. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/45808 |
| publishDate | 2024 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | karina.camps@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Gervaz Sofía, Universidad de la República (Uruguay). Facultad de Ingeniería.Favre Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.2024-09-11T14:46:43Z2024-09-11T14:46:43Z2024Gervaz, S. y Favre, F. "Identifying key parameters in building energy models: sensitivity analysis applied to residential typologies". Buildings. [en línea] 2024 vol.14, no. 9, 2804. DOI: https://doi.org/10.3390/buildings14092804.https://hdl.handle.net/20.500.12008/45808https://doi.org/10.3390/buildings14092804Building energy modeling tools play a crucial role in quantifying and understanding the energy performance of buildings. These tools require substantial amounts of data, which can be challenging to obtain and are often associated with significant uncertainties. The incorporation of sensitivity analysis is a crucial step toward developing reliable models as it identifies the most critical parameters that require meticulous characterization. In this study, a sensitivity analysis based on the Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modeled in EnergyPlus. Different numbers of Morris trajectories and levels were considered to analyze the impact of the user-defined values of r and p when employing the Morris method. Convergence was achieved at r = 200 and p = 12, which are higher than the typically employed values (r = 10 and p = 4). Roof solar absorptivity, setpoint temperatures, orientation, and the roof conductance rank among the top five most influential parameters affecting thermal loads in all four of the studied typologies. Occupancy was also among the top five most relevant parameters in three of the four typologies.Submitted by Berón Cecilia (cberon@fing.edu.uy) on 2024-09-10T17:25:28Z No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) GF24.pdf: 1649410 bytes, checksum: d2bf452873c11ead7629b6c2d89cc6a2 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2024-09-11T14:39:03Z (GMT) No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) GF24.pdf: 1649410 bytes, checksum: d2bf452873c11ead7629b6c2d89cc6a2 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2024-09-11T14:46:43Z (GMT). No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) GF24.pdf: 1649410 bytes, checksum: d2bf452873c11ead7629b6c2d89cc6a2 (MD5) Previous issue date: 202421 p.application/pdfenengMDPIBuildings, vol.14, no. 9, 2024, 2804.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Building energy modelSensitivity analysisBuilding simulationIdentifying key parameters in building energy models : Sensitivity analysis applied to residential typologies.Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGervaz, SofíaFavre, FedericoLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/45808/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/45808/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; charset=utf-820496http://localhost:8080/xmlui/bitstream/20.500.12008/45808/3/license_texta34a400dd0046b3aef60f72471c4f991MD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-824251http://localhost:8080/xmlui/bitstream/20.500.12008/45808/4/license_rdf71ed42ef0a0b648670f707320be37b90MD54ORIGINALGF24.pdfGF24.pdfapplication/pdf1649410http://localhost:8080/xmlui/bitstream/20.500.12008/45808/1/GF24.pdfd2bf452873c11ead7629b6c2d89cc6a2MD5120.500.12008/458082024-09-11 11:46:43.787oai:colibri.udelar.edu.uy:20.500.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Institucionalhttps://www.colibri.udelar.edu.uyUniversidad públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712024-09-11T14:46:43COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. Gervaz, Sofía Building energy model Sensitivity analysis Building simulation |
| status_str | publishedVersion |
| title | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| title_full | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| title_fullStr | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| title_full_unstemmed | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| title_short | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| title_sort | Identifying key parameters in building energy models : Sensitivity analysis applied to residential typologies. |
| topic | Building energy model Sensitivity analysis Building simulation |
| url | https://hdl.handle.net/20.500.12008/45808 https://doi.org/10.3390/buildings14092804 |