Hybrid approach to representative building archetypes development for urban models – A case study in Andorra.
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| Title: | Hybrid approach to representative building archetypes development for urban models – A case study in Andorra. |
|---|---|
| Authors: | Borges, Patricia1,2 (AUTHOR) pborges@ari.ad, Travesset-Baro, Oriol1,2 (AUTHOR), Pages-Ramon, Anna3 (AUTHOR) |
| Source: | Building & Environment. May2022, Vol. 215, pN.PAG-N.PAG. 1p. |
| Subject Terms: | *Urban growth, *Energy consumption, Archetypes, Data mining, Machine learning, Multipurpose buildings |
| Geographic Terms: | Andorra |
| Abstract: | Building archetypes development has been the focus of numerous research works over the past decades and are considered one of the biggest challenges, as well as one of the main sources of inaccuracies in Urban Buildings Energy Models (UBEM). The development of machine learning and data mining techniques, such as clustering, as well as the access to building data at disaggregated scales, has opened new horizons in this field. With the aim to reduce additional simulation errors derived from the fragmentation process of the archetype approach in UBEMs, this paper presents an alternative hybrid approach to identify representative building archetypes combining the classic deterministic and a data-driven clustering approach using building data at both building and cadastral unit scales. The methodology was tested in the Escaldes-Engordany building stock, a city of the Principality of Andorra, and the resulting archetypes have been compared with the archetypes obtained by the application of both approaches applied separately. A total of 71 archetypes were identified in the Escaldes-Engordany building stock. The results show that both approaches complement each other and allow to overcome the identified barriers when applied separately. In addition, the results also reveal that there is an important heterogeneity of certain building aspects not only between buildings, but also within buildings, which cannot be detected if the fragmentation is still carried out at the building scale. • Deterministic and clustering approaches' respective limitations offset once applied simultaneously. • Clustering allows hidden structures identification since membership is not an input. • Energy use data enables a building stock fragmentation based on empirical data. • Buildings' heterogeneity is not only between buildings but within buildings. • Disaggregated data is required to adequately mimic the building stock energy reality. [ABSTRACT FROM AUTHOR] |
| Copyright of Building & Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 156198413 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid approach to representative building archetypes development for urban models – A case study in Andorra. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Borges%2C+Patricia%22">Borges, Patricia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pborges@ari.ad</i><br /><searchLink fieldCode="AR" term="%22Travesset-Baro%2C+Oriol%22">Travesset-Baro, Oriol</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pages-Ramon%2C+Anna%22">Pages-Ramon, Anna</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Building+%26+Environment%22">Building & Environment</searchLink>. May2022, Vol. 215, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Urban+growth%22">Urban growth</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Archetypes%22">Archetypes</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multipurpose+buildings%22">Multipurpose buildings</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Andorra%22">Andorra</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Building archetypes development has been the focus of numerous research works over the past decades and are considered one of the biggest challenges, as well as one of the main sources of inaccuracies in Urban Buildings Energy Models (UBEM). The development of machine learning and data mining techniques, such as clustering, as well as the access to building data at disaggregated scales, has opened new horizons in this field. With the aim to reduce additional simulation errors derived from the fragmentation process of the archetype approach in UBEMs, this paper presents an alternative hybrid approach to identify representative building archetypes combining the classic deterministic and a data-driven clustering approach using building data at both building and cadastral unit scales. The methodology was tested in the Escaldes-Engordany building stock, a city of the Principality of Andorra, and the resulting archetypes have been compared with the archetypes obtained by the application of both approaches applied separately. A total of 71 archetypes were identified in the Escaldes-Engordany building stock. The results show that both approaches complement each other and allow to overcome the identified barriers when applied separately. In addition, the results also reveal that there is an important heterogeneity of certain building aspects not only between buildings, but also within buildings, which cannot be detected if the fragmentation is still carried out at the building scale. • Deterministic and clustering approaches' respective limitations offset once applied simultaneously. • Clustering allows hidden structures identification since membership is not an input. • Energy use data enables a building stock fragmentation based on empirical data. • Buildings' heterogeneity is not only between buildings but within buildings. • Disaggregated data is required to adequately mimic the building stock energy reality. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Building & Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.buildenv.2022.108958 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Urban growth Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Archetypes Type: general – SubjectFull: Data mining Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Multipurpose buildings Type: general – SubjectFull: Andorra Type: general Titles: – TitleFull: Hybrid approach to representative building archetypes development for urban models – A case study in Andorra. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Borges, Patricia – PersonEntity: Name: NameFull: Travesset-Baro, Oriol – PersonEntity: Name: NameFull: Pages-Ramon, Anna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 03601323 Numbering: – Type: volume Value: 215 Titles: – TitleFull: Building & Environment Type: main |
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