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- W2983581537 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." @default.
- W2983581537 created "2019-11-22" @default.
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- W2983581537 date "2019-07-01" @default.
- W2983581537 modified "2023-10-04" @default.
- W2983581537 title "Formation-aware Cloud Segmentation of Ground-based Images with Applications to PV Systems" @default.
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- W2983581537 doi "https://doi.org/10.1109/iisa.2019.8900762" @default.
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