CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id ecaade2020_404
authors Singh, Manav Mahan, Schneider-Marin, Patricia, Harter, Hannes, Lang, Werner and Geyer, Philipp
year 2020
title Applying Deep Learning and Databases for Energy-efficient Architectural Design - Abstract
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 79-87
summary 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.
keywords BIM; Operational Energy; Embodied Energy; Life-cycle Energy Demand; Early Design Phases
series eCAADe
full text file.pdf (1,915,437 bytes)
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100%; open Abualdenien, J, Schneider-Marin, P, Zahedi, A, Harter, H, Exner, H, Steiner, D, Mahan Singh, M, Borrmann, A, Lang, W, Petzold, F, K├Ânig, M, Geyer, P and Schnellenbach-Held, M (2020) Find in CUMINCAD Consistent management and evaluation of building models in the early design stages , Journal of Information Technology in Construction, 25, pp. 212-232

100%; open Ahn, KU, Kim, YJ, Park, CS, Kim, I and Lee, K (2014) Find in CUMINCAD BIM interface for full vs. semi-automated building energy simulation , Energy and Buildings, 68(PART B), pp. 671-678

100%; open Corder, GW and Foreman, DI (2014) Find in CUMINCAD Nonparametric statistics for non-statisticians: a step-by-step approach , Wiley

100%; open Dixit, MK (2017) Find in CUMINCAD Life cycle embodied energy analysis of residential buildings: A review of literature to investigate embodied energy parameters , Renewable and Sustainable Energy Reviews, 79, pp. 390-413

100%; open Geyer, P and Singaravel, S (2018) Find in CUMINCAD Component-based machine learning for performance prediction in building design , Applied Energy, 228, pp. 1439-1453

100%; open Geyer, P, Singh, MM and Singaravel, S (2018) Find in CUMINCAD Component-Based Machine Learning for Energy Performance Prediction by MultiLOD Models in the Early Phases of Building Design , Smith, I F C and Domer, Bernd (eds), Advanced Computing Strategies for Engineering, Springer, Lausanne, Switzerland, pp. 516-534

100%; open Hemsath, TL and Alagheband Bandhosseini, K (2015) Find in CUMINCAD Sensitivity analysis evaluating basic building geometry's effect on energy use , Renewable Energy, 76, pp. 526-538

100%; open Negendahl, K (2015) Find in CUMINCAD Building performance simulation in the early design stage: An introduction to integrated dynamic models , Automation in Construction, 54, pp. 39-53

100%; open Schneider-Marin, P, Harter, H, Tkachuk, K and Lang, W (2020) Find in CUMINCAD Uncertainty Analysis of Embedded Energy and Greenhouse Gas Emissions Using BIM in Early Design Stages , Sustainability, 12(7), p. 2633

100%; open Singaravel, S, Suykens, J and Geyer, P (2018) Find in CUMINCAD Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction , Advanced Engineering Informatics, 38, pp. 81-90

100%; open Singh, MM and Geyer, P (2019) Find in CUMINCAD Statistical decision assistance for determining energy-efficient options in building design under uncertainty , 26th International Workshop on Intelligent Computing in Engineering, Leuven

100%; open Singh, MM and Geyer, P (2020) Find in CUMINCAD Information requirements for multi-level-of-development BIM using sensitivity analysis for energy performance , Advanced Engineering Informatics, 43, p. 101026

100%; open Singh, MM, Singaravel, S and Geyer, P (2019) Find in CUMINCAD Improving Prediction Accuracy of Machine Learning Energy Prediction Models , Proceedings of the 36th CIB W78 2019 Conference, Newcastle, UK, pp. 102-112

100%; open Tian, W, Heo, Y, de Wilde, P, Li, Z, Yan, D, Park, CS, Feng, X and Augenbroe, G (2018) Find in CUMINCAD A review of uncertainty analysis in building energy assessment , Renewable and Sustainable Energy Reviews, 93, pp. 285-301

100%; open Van Gelder, L, Janssen, H and Roels, S (2014) Find in CUMINCAD Probabilistic design and analysis of building performances: Methodology and application example , Energy and Buildings, 79, pp. 202-211

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