Feature extraction for enhancing data-driven urban building energy models
dc.contributor.author | Bolluk, Muhammed Said | |
dc.contributor.author | Seyis, Senem | |
dc.contributor.author | Aydoğan, Reyhan | |
dc.date.accessioned | 2024-02-16T10:10:37Z | |
dc.date.available | 2024-02-16T10:10:37Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-070170273-1 | |
dc.identifier.issn | 2684-1150 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/9158 | |
dc.identifier.uri | https://ec-3.org/publications/conference/paper/?id=EC32023_291 | |
dc.description.abstract | Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model that estimates the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score. Therefore, this study shows that building energy consumption estimation can be enhanced via new feature extraction equipped with domain knowledge. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | European Council on Computing in Construction (EC3) | en_US |
dc.relation.ispartof | Proceedings of the European Conference on Computing in Construction | |
dc.rights | restrictedAccess | |
dc.title | Feature extraction for enhancing data-driven urban building energy models | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-9133-9899 & YÖK ID 258625) Seyis, Senem | |
dc.contributor.authorID | (ORCID 0000-0002-5260-9999 & YÖK ID 145578) Aydoğan, Reyhan | |
dc.contributor.ozuauthor | Seyis, Senem | |
dc.contributor.ozuauthor | Aydoğan, Reyhan | |
dc.identifier.doi | 10.35490/EC3.2023.291 | en_US |
dc.subject.keywords | Machine learning | en_US |
dc.subject.keywords | Urban building energy demand prediction | en_US |
dc.subject.keywords | Feature extraction | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85177215718 | |
dc.contributor.ozugradstudent | Bolluk, Muhammed Said | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and PhD Student |
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