Applying Deep Learning and Databases for Energyefficient Architectural Design

authored by

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

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

External Organisation(s)

Technische Universität Berlin
KU Leuven
Technical University of Munich (TUM)

Type

Conference contribution

Volume

2

Pages

79-87

Publication date

2020

Publication status

Published

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.52842/conf.ecaade.2020.2.079 (Access: Open)