Publications

[1] M. M. Singh, C. Deb, and P. Geyer, “Early-stage design support combining machine learning and building information modelling,” Automation in Construction, vol. 136, p. 104147, Apr. 2022, doi: 10.1016/j.autcon.2022.104147.

[2] P. Geyer, M. M. Singh, and X. Chen, “Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning,” arXiv:2108.13836 [cs, eess], Jan. 2022, Accessed: Feb. 25, 2022. [Online]. Available: http://arxiv.org/abs/2108.13836

[3] X. Chen and P. Geyer, “Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty,” Applied Energy, vol. 307, p. 118240, Feb. 2022, doi: 10.1016/j.apenergy.2021.118240.

[4] M. M. Singh, S. Singaravel, and P. Geyer, “Machine learning for early stage building energy prediction: Increment and enrichment,” Applied Energy, vol. 304, p. 117787, Dec. 2021, doi: 10.1016/j.apenergy.2021.117787.

[5] P. Geyer, C. Koch, and P. Pauwels, “Fusing data, engineering knowledge and artificial intelligence for the built environment,” Advanced Engineering Informatics, vol. 48, p. 101242, Apr. 2021, doi: 10.1016/j.aei.2020.101242.

[6] X. Chen and Philipp Geyer, “Machine assistance: A predictive framework toward dynamic interaction with human decision-making under uncertainty in energy-efficient building design,” submitted, 2021.

[7] M. M. Singh, S. Singaravel, R. Klein, and P. Geyer, “Quick energy prediction and comparison of options at the early design stage,” Advanced Engineering Informatics, vol. 46, p. 101185, Oct. 2020, doi: 10.1016/j.aei.2020.101185.

[8] M. M. Singh, S. Singaravel, and P. Geyer, “Machine Learning Energy Prediction Model for Complex Building Shapes: Effect of Enriching Training Data,” Applied Energy, vol. submitted, 2020.

[9] M. M. Singh, S. Singaravel, and P. Geyer, “Development of Deep Learning based Energy Prediction Model for Complex Building Shapes: Data Enrichment vs Data Increment,” Manuskript in Vorbereitung, 2020.

[10] M. M. Singh and P. Geyer, “Information requirements for multi-level-of-development BIM using sensitivity analysis for energy performance,” Advanced Engineering Informatics, vol. 43, 2020, doi: https://doi.org/10.1016/j.aei.2019.101026.

[11] S. Singaravel, J. Suykens, H. Janssen, and P. Geyer, “Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts,” Design Science, vol. 6, p. e23, 2020, doi: 10.1017/dsj.2020.22.

[12] H. Harter, M. M. Singh, P. Schneider-Marin, W. Lang, and P. Geyer, “Uncertainty Analysis of Life Cycle Energy Assessment in Early Stages of Design,” Energy and Buildings, vol. 208, 2020, doi: https://doi.org/10.1016/j.enbuild.2019.109635.

[13] J. Abualdenien et al., “Consistent management and evaluation of building models in the early design stages,” Journal of Information Technology in Construction (ITcon), vol. 25, no. 13, pp. 212–232, Mar. 2020, doi: 10.36680/j.itcon.2020.013.

[14] D. Wang et al., “Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI,” Proc. ACM Hum.-Comput. Interact., vol. 3, no. CSCW, p. 211:1—-211:24, Nov. 2019, doi: 10.1145/3359313.

[15] M. M. Singh and P. Geyer, “Sensitivity analysis methods for building energy models at early-stage design,” 2019.

[16] S. Singaravel, J. Suykens, and P. Geyer, “Deep convolutional learning for general early design stage prediction models,” Advanced Engineering Informatics, vol. 42, 2019, doi: https://doi.org/10.1016/j.aei.2019.100982.

[17] H. Harter, M. M. Singh, P. Schneider-Marin, W. Lang, and P. Geyer, “Sensitivity Analysis of Life Cycle Based Energy Prediction in Early Design Stages of Buildings.,” 2019.

[18] M. Delwati, A. Ammar, and P. Geyer, “Economic Evaluation and Simulation for the Hasselt Case Study: Thermochemical District Network Technology vs. Alternative Technologies for Heating,” Energies, vol. 12, no. 7, 2019, doi: 10.3390/en12071260.

[19] S. Singaravel, J. Suykens, and P. Geyer, “Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction,” Advanced Engineering Informatics, vol. 38, pp. 81–90, Oct. 2018, doi: 10.1016/j.aei.2018.06.004.

[20] A. Schlueter and P. Geyer, “Linking BIM and Design of Experiments to balance architectural and technical design factors for energy performance,” Automation in Construction, vol. 86, 2018, doi: 10.1016/j.autcon.2017.10.021.

[21] P. Geyer and S. Singaravel, “Component-based machine learning for performance prediction in building design,” Applied Energy, vol. 228, pp. 1439–1453, Oct. 2018, doi: 10.1016/j.apenergy.2018.07.011.

[22] P. Geyer, A. Schlüter, and S. Cisar, “Application of clustering for the development of retrofit strategies for large building stocks,” Advanced Engineering Informatics, vol. 31, pp. 32–47, Jan. 2017, doi: 10.1016/j.aei.2016.02.001.

[23] P. Geyer, M. Buchholz, R. Buchholz, and M. Provost, “Hybrid thermo-chemical district networks – Principles and technology,” Applied Energy, vol. 186, 2017, doi: 10.1016/j.apenergy.2016.06.152.

[24] A. Schlueter, P. Geyer, and S. Cisar, “Analysis of georeferenced building data for the identification and evaluation of thermal microgrids,” Proceedings of the IEEE, vol. 104, no. 4, 2016, doi: 10.1109/JPROC.2016.2526118.

[25] Y. Shao, P. Geyer, and W. Lang, “Integrating requirement analysis and multi-objective optimization for office building energy retrofit strategies,” Energy and Buildings, vol. 82, 2014, doi: 10.1016/j.enbuild.2014.07.030.

[26] A. Schlüter and P. Geyer, “Exploration of Optimal Building Retrofit Strategies Using Design of Experiments,” Buildings and Energy, 2014.

[27] P. Geyer and A. Schlüter, “Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting,” Applied Energy, vol. 119, pp. 537–556, Apr. 2014, doi: 10.1016/j.apenergy.2013.12.064.

[28] P. Geyer, J. Stopper, W. Lang, and M. Thumfart, “A Systems Engineering Methodology for Designing and Planning the Built Environment—Results from the Urban Research Laboratory Nuremberg and Their Integration in Education,” Systems, vol. 2, no. 2, pp. 137–158, 2014, doi: 10.3390/systems2020137.

[29] A. Borrmann, P. Geyer, and C. Koch, “Advanced computing for the built environment,” Advanced Engineering Informatics, vol. 27, no. 4, 2013, doi: 10.1016/j.aei.2013.11.001.

[30] P. Geyer and M. Buchholz, “Parametric systems modeling for sustainable energy and resource flows in buildings and their urban environment,” Automation in Construction, vol. 22, 2012, doi: 10.1016/j.autcon.2011.07.002.

[31] P. Geyer, “Systems modelling for sustainable building design,” Advanced Engineering Informatics, vol. 26, no. 4, pp. 656–668, Oct. 2012, doi: 10.1016/j.aei.2012.04.005.

[32] P. Geyer, “Component-oriented decomposition for multidisciplinary design optimization in building design,” Advanced Engineering Informatics, vol. 23, no. 1, 2009, doi: 10.1016/j.aei.2008.06.008.

[33] P. Geyer, “Multidisciplinary grammars supporting design optimization of buildings,” Research in Engineering Design, vol. 18, no. 4, 2008, doi: 10.1007/s00163-007-0038-6.

[1] M. M. Singh and P. Geyer, “CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information,” 2021. [Online]. Available: https://www.researchgate.net/profile/Manav-Singh-2/publication/354420055_CNN-based_Quick_Energy_Prediction_Model_using_Image_Analysis_for_Shape_Information/links/61378bda637a811d6d580b61/CNN-based-Quick-Energy-Prediction-Model-using-Image-Analysis-for-Shape-Information.pdf

[2] X. Chen, Manav Mahan Singh, and Philipp Geyer, “Component-based machine learning for predicting representative time-series of energy performance in building design,” presented at the 28th International Workshop on Intelligent Computing in Engineering, Berlin, 2021.

[3] X. Chen, T. Guo, and P. Geyer, “A hybrid-model time-series forecasting approach for reducing the building energy performance gap,” in EG-ICE 2021 Workshop on Intelligent Computing in Engineering, 2021, p. 44.

[4] M. M. Singh, H. Harter, P. Schneider-Marin, W. Lang, and P. Geyer, “Applying Deep Learning and Databases for Energyefficient Architectural Design,” 2020.

[5] M. M. Singh, S. Singaravel, and P. Geyer, “Improving Prediction Accuracy of Machine Learning Energy Prediction Models,” in Proceedings of the 36th CIB W78 2019 Conference, Newcastle, 2019, pp. 102–112.

[6] M. M. Singh and P. Geyer, “Statistical decision assistance for determining energy-efficient options in building design under uncertainty,” in 26th International Workshop on Intelligent Computing in Engineering, 2019, vol. 2394, no. 2.

[7] S. Singaravel and P. Geyer, “Deep Component-Based Neural Network Energy Modelling for Early Design Stage Prediction,” in Design Computing and Cognition ’18, Cham, 2019, pp. 21–36.

[8] M. Delwati, D. Saelens, and Philipp Geyer, “Multi-Scale Simulation Thermo-Chemical District Network,” 2019.

[9] M. M. Singh, S. Singaravel, and P. Geyer, “Information Exchange Scenarios between Machine Learning Energy Prediction Model and BIM at Early Stage of Design,” in The Sixth International Symposium on Life-Cycle Civil Engineering, 2018, pp. 487–494.

[10] S. Singaravel, P. Geyer, and J. Suykens, “Deep neural network architectures for component-based machine learning model in building energy predictions,” 2017.

[11] S. Singaravel, P. Geyer, and J. Suykens, “Component-based Machine Learning Modelling Approach For Design Stage Building Energy Prediction,” 2017.

[12] Markus König et al., “Evaluation of building design variants in early phases on the basis of adaptive detailing strategies and system-based simulation of energy flows for such models,” Ghent, 2017. [Online]. Available: http://www.beyondbim.be/wp-content/plugins/download-attachments/includes/download.php?id=183

[13] S. Singaravel and P. Geyer, “Simplifying building energy performance models to support an integrated design workflow,” 2016.

[14] P. Geyer and A. Schlueter, “Performance-Based Clustering for Building Stock Management at Regional Level,” 2016.

[15] S. Singaravel, P. Geyer, and J. Suykens, “Component-Based Machine Learning Modelling Approach For Design Stage Building Energy Prediction: Weather Conditions And Size,” in Proceedings of the 15th IBPSA Conference, 2015, pp. 2617–2626.

[16] F. Ritter, P. Geyer, and A. Borrmann, “Simulation-based decision-making in early design stages,” in 32nd CIB W78 Conference, Eindhoven, The Netherlands, 2015, pp. 27–29.

[17] J. Maderspacher, P. Geyer, T. Auer, and W. Lang, “Comparison of different meta model approches with a detailed buiding model for long-Term simulations,” 2015.

[18] P. Geyer and F. Ritter, “Identifying thermal microgrids on the basis of spatialized fuzzy logic and metamodelling,” 2015.

[19] P. Geyer, M. Buchholz, R. Buchholz, and M. Provost, “Design and Modelling of a hybrid thermal and thermochemical network technology,” 2015.

[20] F. Ritter, G. Schubert, P. Geyer, A. Borrmann, and F. Petzold, “Design decision support - Real-time energy simulation in the early design stages,” 2014. doi: 10.1061/9780784413616.251.

[21] P. Geyer, “Using the Systems Modelling Language (SysML) for decision modelling for sustainable building design,” 2014.

[22] P. Geyer, J. Tigges, T. Zölch, and S. M. J. L. W. P. Gondhalekar D, “Integrating urban built and green structures to improve climate change mitigation and adaptation: The approach of a recently initiated centre,” 2014.

[23] P. Geyer, A. Schlüter, and S. Cisar, “A performance-based clustering method for retrofit management of building stocks,” 2014.

[24] P. Geyer, “Systems modeling for building design: A method based on the systems modeling language,” 2014.

[25] F. Ritter, P. Geyer, and A. Borrmann, “Integrating Expert Engineering Knowledge into Conceptual Architectural Design and Decision-making,” 2013.

[26] P. Geyer, M. Thumfart, and W. Lang, “Partial System Simulation for Long-term Sustainable Urban Development,” 2013.

[27] P. Geyer, “Design-oriented coupling of system models and spatial models crossing multiple scales for energy simulation in building retrofitting,” 2013.

[28] P. Geyer, I. Nemeth, W. Lang, G. Wulfhorst, J. Schinabeck, and R. Priester, “Systems modelling considering qualities and quantities for strategies of sustainable development of a liveable urban district in nuremberg,” 2012.

[29] A. Borrmann, P. Geyer, Y. Rafiq, and P. De Wilde, “Preface,” 2012.

[30] Philipp Geyer, “Modelling System Flows in Building and City Design,” presented at the CISBAT, 2011.

[31] Thumfart, M, “OpenSource Gebäudesteuerungs- und Messsystem,” presented at the Forum Bauin-formatik 2010, 2010.

[32] P. Geyer and M. Buchholz, “System-Embedded Building Design and Modeling,” 2010.

[33] P. Geyer and K. Beucke, “An Integrative Approach for Using Multidisciplinary Design Optimization (MDO) in AEC,” Nottingham, 2010.

[34] M. Buchholz, R. Buchholz, P. Geyer, and M. Schmidt, “Watergy - ein Feuchtluft-Solarkollektorsystem mit integriertem Solekreislauf zur Gebäudeheizung,” 2010.

[35] P. Geyer, “Decomposition and System Layout for Multidisciplinary Design Optimization of a Seasonal Heat Storage System Integrated in Buildings,” Berlin, 2009, pp. 124–133.

[36] M. Buchholz, R. Buchholz, P. Geyer, and M. Schmidt, “Watergy – ein Feuchtluft-Solarkollektorsystem mit saisonaler Energiespeicherung zur Gebäudeheizung,” Erfurt, 2009.

[37] P. Geyer, “The Transformation of an Architectural Design into an Optimization Model: Design Intentions and Qualitative Aspects,” Plymouth, 2008.

[38] P. Geyer, “Erweiterungen für die Industry Foundations Classes zur Einbettung von Multidisziplinärer Entwurfsoptimierung,” in Forum Bauinformatik, Dresden, 2008, pp. 157–168.

[39] P. Geyer, “Embedding Optimization in the Design Process of Buildings - A Hall Example,” Maribor, 2007, pp. 689–698.

[40] P. Geyer, “Multidisziplinäre Entwurfsoptimierung angewandt auf eine Gebäudehaut,” presented at the Forum Bauinformatik 2006, 2006.

[41] P. Geyer, “Optimierung und Ästhetik aus Sicht der Architektur,” Hamburg, 2006, pp. 113–125.

[42] P. Geyer, “Models for Multidisciplinary Design Optimization: An Exemplary Office Room,” Weimar, 2006, p. CD-ROM / online.

[43] P. Geyer, “Models And Production Systems For Multidisciplinary Optimization In Building Design,” Montréal, Canada, 2006, pp. 2677–2689.

[44] P. Geyer, “Entwurfsoptimierung von Gebäudetragwerken – ein exemplarischer Prototyp,” Weimar, 2006, pp. 337–346.

[45] P. Geyer and K. Rückert, “Entwurfsoptimierung von Gebäudetragwerken - ein exemplarischer Prototyp,” Cottbus, 2005, pp. 311–319.

[46] P. Geyer and K. Rückert, “Conceptions for MDO in Structural Design of Buildings,” Rio de Janeiro, Brazil, 2005, p. CD-ROM.

[47] Philipp Geyer and Klaus Rückert, “Application of Computer-Aided Optimization in the Process of Structural Design.,” 2004.

[48] P. Geyer, “Luftschiffhafen Berlin,” 2004.

[49] P. Geyer, “Konzeptioneller Entwurf für einen Luftschiffhafen,” 2003.

[50] P. Geyer and Y. Abdel Gadir, “Reisen im Luftschiff - Konzeptstudie und Machbarkeits¬analyse,” 2002.