Philipp Geyer
Philipp Geyer
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Cited by
Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction
S Singaravel, J Suykens, P Geyer
Advanced Engineering Informatics 38, 81-90, 2018
Component-oriented decomposition for multidisciplinary design optimization in building design
P Geyer
Advanced Engineering Informatics 23 (1), 12-31, 2009
Integrating requirement analysis and multi-objective optimization for office building energy retrofit strategies
Y Shao, P Geyer, W Lang
Energy and Buildings 82, 356-368, 2014
Systems modelling for sustainable building design
P Geyer
Advanced Engineering Informatics 26 (4), 656-668, 2012
Linking BIM and Design of Experiments to balance architectural and technical design factors for energy performance
A Schlueter, P Geyer
Automation in Construction 86, 33-43, 2018
Component-based machine learning for performance prediction in building design
P Geyer, S Singaravel
Applied energy 228, 1439-1453, 2018
Multidisciplinary grammars supporting design optimization of buildings
P Geyer
Research in Engineering Design 18 (4), 197-216, 2008
Automated metamodel generation for Design Space Exploration and decision-making–A novel method supporting performance-oriented building design and retrofitting
P Geyer, A Schlüter
Applied Energy 119, 537-556, 2014
Application of clustering for the development of retrofit strategies for large building stocks
P Geyer, A Schlüter, S Cisar
Advanced Engineering Informatics 31, 32-47, 2017
Simulation-based decision-making in early design stages
F Ritter, P Geyer, A Borrmann
32nd CIB W78 Conference, Eindhoven, The Netherlands, 27-29, 2015
Uncertainty analysis of life cycle energy assessment in early stages of design
H Harter, MM Singh, P Schneider-Marin, W Lang, P Geyer
Energy and Buildings 208, 109635, 2020
Information requirements for multi-level-of-development BIM using sensitivity analysis for energy performance
MM Singh, P Geyer
Advanced Engineering Informatics 43, 101026, 2020
Quick energy prediction and comparison of options at the early design stage
MM Singh, S Singaravel, R Klein, P Geyer
Advanced Engineering Informatics 46, 101185, 2020
Consistent management and evaluation of building models in the early design stages
J Abualdenien, P Schneider-Marin, A Zahedi, H Harter, H Exner, ...
Journal of Information Technology in Construction 25, 212-232, 2020
Hybrid thermo-chemical district networks–Principles and technology
P Geyer, M Buchholz, R Buchholz, M Provost
Applied Energy 186, 480-491, 2017
Parametric systems modeling for sustainable energy and resource flows in buildings and their urban environment
P Geyer, M Buchholz
Automation in construction 22, 70-80, 2012
Deep convolutional learning for general early design stage prediction models
S Singaravel, J Suykens, P Geyer
Advanced Engineering Informatics 42, 100982, 2019
Component-based machine learning modelling approach for design stage building energy prediction: weather conditions and size
S Singaravel, P Geyer, J Suykens
Proceedings of the 15th IBPSA conference, 2617-2626, 2017
Component-based machine learning for energy performance prediction by MultiLOD models in the early phases of building design
P Geyer, MM Singh, S Singaravel
Advanced Computing Strategies for Engineering: 25th EG-ICE International …, 2018
Analysis of georeferenced building data for the identification and evaluation of thermal microgrids
A Schlueter, P Geyer, S Cisar
Proceedings of the IEEE 104 (4), 713-725, 2016
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