| 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 | 207 | 2018 |
| Simulation-based support for product development of innovative building envelope components R Loonen, S Singaravel, M Trčka, D Cóstola, JLM Hensen Automation in Construction 45, 86-95, 2014 | 108 | 2014 |
| Component-based machine learning for performance prediction in building design P Geyer, S Singaravel Applied energy 228, 1439-1453, 2018 | 94 | 2018 |
| 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 | 39 | 2020 |
| Deep convolutional learning for general early design stage prediction models S Singaravel, J Suykens, P Geyer Advanced Engineering Informatics 42, 100982, 2019 | 23 | 2019 |
| Machine learning for early stage building energy prediction: Increment and enrichment MM Singh, S Singaravel, P Geyer Applied Energy 304, 117787, 2021 | 21 | 2021 |
| 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 | 19 | 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 | 18 | 2018 |
| Improving Prediction Accuracy of Machine Learning Energy Prediction Models MM Singh, S Singaravel, P Geyer Proceedings of the 36th CIB W 78, 2019, 2019 | 10 | 2019 |
| Deep neural network architectures for component-based machine learning model in building energy predictions S Singaravel, P Geyer, J Suykens Proceedings of the Digital Proceedings of the 24th EG-ICE International …, 2017 | 10 | 2017 |
| Simplifying Building Energy Performance Models to support an Integrated Design workflow S Singaravel, P Geyer EG-ICE 2016, 2016 | 8 | 2016 |
| Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts S Singaravel, J Suykens, H Janssen, P Geyer Design Science 6, e23, 2020 | 7 | 2020 |
| Deep learning neural networks architectures and methods: building design energy prediction by component-based models S Singaravel, J Suykens, P Geyer Advanced Engineering Informatics 38, 81-90, 2018 | 5 | 2018 |
| Machine Learning for energy performance prediction in early design stage of buildings S Singaravel KU Leuven, 2020 | 3 | 2020 |
| Deep Component-Based Neural Network Energy Modelling for Early Design Stage Prediction S Singaravel, P Geyer Design Computing and Cognition'18, 21-36, 2019 | 2 | 2019 |
| Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design MM Singh, S Singaravel, P Geyer Life Cycle Analysis and Assessment in Civil Engineering: Towards an …, 2018 | 2 | 2018 |
| Machine Learning for Occupancy Detection through Smart Home Sensor Data S Singaravel, S Delrue, I Pollet, S Vandekerckhove | 1 | 2023 |
| Hybrid machine learning for occupancy detection S Singaravel, S Delrue, I Pollet, S Vandekerckhove no. February, 2021 | 1 | 2021 |
| Parametric Building Energy Models Based on Machine Learning for Buildings Design Strategies S Singaravel, P Geyer Computing in Civil Engineering 2017, Date: 2017/01/01-2017/01/01, Location …, 2017 | | 2017 |
Philipp GeyerLeibniz University HannoverVerified email at iek.uni-hannover.de
