Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Anthropologic: Architecture and Fabrication in the cognitive age |
| Untertitel | Proceedings of the 38th eCAADe Conference |
| Herausgeber/-innen | Liss C. Werner, Dietmar Koering |
| Erscheinungsort | Berlin |
| Seiten | 79-87 |
| Band | 2 |
| ISBN (elektronisch) | 978-9-49120-721-1 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 38th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2020 - Berlin, Deutschland Dauer: 16 Sept. 2020 → 17 Sept. 2020 |
Publikationsreihe
| Name | eCAADe proceedings |
|---|---|
| ISSN (Print) | 2684-1843 |
Abstract
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Anthropologic: Architecture and Fabrication in the cognitive age: Proceedings of the 38th eCAADe Conference. Hrsg. / Liss C. Werner; Dietmar Koering. Band 2 Berlin, 2020. S. 79-87 (eCAADe proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
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TY - GEN
T1 - Applying Deep Learning and Databases for Energyefficient Architectural Design
AU - Geyer, Philipp Florian
AU - Singh, Manav Mahan
AU - Schneider-Marin, Patricia
AU - Harter, Hannes
AU - Lang, Werner
PY - 2020
Y1 - 2020
N2 - The reduction of energy consumption of buildings requires consideration in early design phases. However, modelling and computation time required for dynamic energy simulations makes them inappropriate in the early phases. This paper presents a performance prediction approach for these phases that is embedded in a multi-level-of-development modelling approach. First, parametric pre-trained modular deep learning components are embedded in the building elements. The energy performance is predicted by composing these components. Second, embodied energy assessment is performed by extracting the information from a database. A calculation module queries the database and calculates the embodied energy. Both, embodied and operational, energy are assembled to predict lifecycle energy demand. The method has been implemented prototypically in a digital modelling environment Revit. A case study serves to demonstrate the application process, the user interaction and the information flows. It shows energy prediction in early design phases to enhance the environmental performance of the building
AB - The reduction of energy consumption of buildings requires consideration in early design phases. However, modelling and computation time required for dynamic energy simulations makes them inappropriate in the early phases. This paper presents a performance prediction approach for these phases that is embedded in a multi-level-of-development modelling approach. First, parametric pre-trained modular deep learning components are embedded in the building elements. The energy performance is predicted by composing these components. Second, embodied energy assessment is performed by extracting the information from a database. A calculation module queries the database and calculates the embodied energy. Both, embodied and operational, energy are assembled to predict lifecycle energy demand. The method has been implemented prototypically in a digital modelling environment Revit. A case study serves to demonstrate the application process, the user interaction and the information flows. It shows energy prediction in early design phases to enhance the environmental performance of the building
U2 - 10.52842/conf.ecaade.2020.2.079
DO - 10.52842/conf.ecaade.2020.2.079
M3 - Conference contribution
VL - 2
T3 - eCAADe proceedings
SP - 79
EP - 87
BT - Anthropologic: Architecture and Fabrication in the cognitive age
A2 - Werner, Liss C.
A2 - Koering, Dietmar
CY - Berlin
T2 - 38th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2020
Y2 - 16 September 2020 through 17 September 2020
ER -