Data Science for Sciences (DS4S 2024)
Abstract Building-integrated photovoltaics (BIPV) façades are becoming increasingly important in achieving zero-carbon targets. Several methods estimate BIPV potential and take into account the overall size, orientation, and shading aspects of individual façades in Switzerland. However, various architectural façade components, such as windows and balconies, are neglected in the estimations despite their significant impact on the overall BIPV energy production. Therefore, we propose an approach that integrates multiple open data sources and deep learning techniques to acquire and analyze façade features essential for estimating facade BIPV potential accurately.
Keywords: Feature extraction, Building facade, Open data, BIPV
Citation
@inproceedings{duran2024extraction,
title={Extraction of fa{\c{c}}ade features from multiple open data sources for BIPV potential},
author={Duran, Ay{\c{c}}a and Mirabian, Pedram and Waibel, Christoph and Schlueter, Arno},
booktitle={Data Science for the Sciences Conference (DS4S 2024)},
year={2024},
organization={ETH Zurich, Architecture and Building Systems}
}
title={Extraction of fa{\c{c}}ade features from multiple open data sources for BIPV potential},
author={Duran, Ay{\c{c}}a and Mirabian, Pedram and Waibel, Christoph and Schlueter, Arno},
booktitle={Data Science for the Sciences Conference (DS4S 2024)},
year={2024},
organization={ETH Zurich, Architecture and Building Systems}
}