@inproceedings{dd453bb3ef2b4138b2c8b774188c8bf7,
title = "Formation-aware Cloud Segmentation of Ground-based Images with Applications to PV Systems",
abstract = "Ground-based sky imaging has won popularity due to its higher temporal and spatial resolution when compared with satellite or air-borne sky imaging systems. Cloud identification and segmentation is the first step in several areas, such as climate research and lately photovoltaic power generation forecast. Cloud-sky segmentation involves several variables including sun position and type and altitude of clouds. We proposed a training-free cloud/sky segmentation based on a threshold that adapts to the cloud formation conditions. Experimental results show that the proposed method reaches higher detection accuracy against state-of-the-art algorithms; additionally, qualitative results over hemispherical high dynamic range (HDR) sky images are provided. The proposed cloud segmentation method can be applied to shading prediction for photovoltaic (PV) systems.",
keywords = "Cloud segmentation, Curve fitting, Ground-based sky imaging, PV systems, Solar arrays, Training-free, Whole sky imager",
author = "Juan Andrade and Sameeksha Katoch and Pavan Turaga and Andreas Spanias and Cihan Tepedelenlioglu and Kristen Jaskie",
note = "Funding Information: ACKNOWLEDGMENT This work was supported in part by the NSF CPS award 1646542 and the ASU SenSIP Center. Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
doi = "10.1109/IISA.2019.8900762",
language = "English (US)",
series = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",
}