| id |
ecaade2021_290 |
| authors |
Nicholas, Paul, Chen, Yu, Borpujari, Nihit, Bartov, Nitsan and Refsgaard, Andreas |
| year |
2021 |
| title |
A Chained Machine Learning Approach to Motivate Retro-Cladding of Residential Buildings |
| doi |
https://doi.org/10.52842/conf.ecaade.2021.1.055
|
| source |
Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 55-64 |
| summary |
This paper investigates how a novel approach to visualisation could help address the challenge of motivating residential retrofitting. Emerging retrofitting research and practice emphasises retro-cladding - the upgrading of the exterior facade of a building - using a modular approach. We present a machine-learning based approach aimed to motivate residential retrofitting through the generation of images and cost/benefit information describing climatically specific additions of external insulation and green roof panels to the façade of a Danish type house. Our approach chains a series of different models together, and implements a method for the controlled navigation of the principle generative styleGAN model. The approach is at a prototypical stage that implements a full workflow but does not include numerical evaluation of model predictions. Our paper details our processes and considerations for the generation of new datasets, the specification and chaining of models, and the linking of climatic data to travel through the latent space of a styleGAN model to visualise and provide a simple cost benefit report for retro-cladding specific to the local climates of five different Danish cities. |
| keywords |
Retrofitting; Machine Learning; Generative Adversarial Networks; Synthetic Datasets |
| series |
eCAADe |
| type |
normal paper |
| email |
|
| full text |
 file.pdf (18,565,772 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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