Applying Deep Learning and Databases for Energy-efficient Architectural Design Abstract

verfasst von

Manav Mahan Singh, Patricia Schneider-Marin, Hannes Harter, Werner Lang, Philipp Florian Geyer

Abstract

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.

Externe Organisation(en)

KU Leuven
Technische Universität München (TUM)
Technische Universität Berlin

Typ

Aufsatz in Konferenzband

Seiten

79-87

Anzahl der Seiten

9

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computergrafik und computergestütztes Design, Ausbildung bzw. Denomination, Architektur

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie