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Determining the shading correction factor using a smartphone camera with a fisheye lens

机译:使用带有鱼眼镜头的智能手机相机确定阴影校正系数

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Quasi-steady-state methods to calculate monthly heating and cooling energy needs require the quantification of solar heat gains through windows. The presence of external obstacles, such as natural topography, other buildings, overhangs, or side fins, may significantly decrease solar gains. Simplified methods to evaluate shading on windows throughout the year use the shading correction factor concept, i.e., the ratio between the surface solar irradiation in the presence of external obstacles and in their absence. This paper presents a photographic method using a low-cost fisheye lens attached to a smartphone to quantify the shading correction factor. It is based on image processing, solar geometry, and climate databases and does not require 3D building design or angle measurement of external obstacles. Both beam and diffuse correction factors are calculated monthly. The model correctly predicts the real Sun's position in the photos and the expected monthly shading of a South window with an overhang. For the diffuse correction factor, errors up to 11% are obtained when compared to the analytical solution of a window with a large overhang. Standard tabulated values for windows with multiple obstructions are conservative when compared to those of the photographic method. The tabulated approach considers the effect of various obstacles shading even when overlapping occurs, which possibly explains the difference between methods.
机译:用于计算每月供暖和制冷能源需求的准稳态方法要求量化通过窗户获得的太阳能热量。外部障碍物(例如自然地形,其他建筑物,悬垂物或侧鳍)的存在可能会大大降低太阳的吸收。全年评估窗上阴影的简化方法使用阴影校正因子概念,即存在外部障碍物和没有外部障碍物时的表面太阳辐射之比。本文提出了一种摄影方法,该方法使用连接到智能手机的低成本鱼眼镜头来量化阴影校正因子。它基于图像处理,太阳几何和气候数据库,不需要3D建筑设计或外部障碍物的角度测量。光束和漫射校正因子均按月计算。该模型可以正确预测照片中实际太阳的位置以及带有悬垂的南窗的预期每月阴影。对于扩散校正因子,与悬垂较大的窗口的解析解决方案相比,可获得高达11%的误差。与照相方法相比,带有多个障碍物的窗户的标准列表值是保守的。列表方法考虑了即使重叠发生各种障碍物阴影的影响,这可能解释了方法之间的差异。

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