Formation-aware Cloud Segmentation of Ground-based Images with Applications to PV Systems

J. Andrade, Sameeksha Katoch, P. Turaga, A. Spanias, C. Tepedelenlioğlu, Kristen Jaskie
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引用次数: 5

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.
基于地形感知的地面图像云分割及其在光伏系统中的应用
与卫星或机载天空成像系统相比,地面天空成像因其更高的时空分辨率而广受欢迎。云的识别和分割是许多领域的第一步,例如气候研究和最近的光伏发电预测。云天分割涉及太阳位置、云的类型和高度等多个变量。我们提出了一种基于适应云形成条件的阈值的无训练云/天分割方法。实验结果表明,与现有算法相比,该方法具有更高的检测精度;此外,还提供了半球面高动态范围(HDR)天空图像的定性结果。提出的云分割方法可用于光伏系统遮阳预测。
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