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Methods and Systems for Optical Flow Modeling Applications for Wind and Solar Irradiance Forecasting

机译:用于风和太阳辐照度预测的光流建模应用的方法和系统

摘要

A method of forecasting cloud motion: gathering a time-series of satellite imagery; transforming the time-series of satellite imagery into a cloudiness index image by establishing an upper and lower limit of visible pixel values for time t; calculating the cloudiness index at each pixel location for time t to provide a cloudiness index image; applying optical flow modeling to the cloudiness index image by assuming pixel value constancy across time; assuming motion to be small and approximating the motion with a Taylor series; assuming vector field is smooth locally; selecting all pixels within d distance of location n with the same prior vector field (m*m pixels); solving system of m*m equations in the least square sense; repeat at multiple resolutions; and calculating cloud motion vectors from multiple resolution vector fields; applying the cloud motion vectors to the cloudiness index image to predict future cloud position and intensity.
机译:一种预测云运动的方法:收集卫星图像的时间序列;通过建立时间t的可见像素值的上限和下限,将卫星图像的时间序列转换为浊度指数图像;计算时间t在每个像素位置处的浊度指数,以提供浊度指数图像;通过假设跨时间的像素值恒定,将光流模型应用于浊度指数图像;假设运动很小,并用泰勒级数近似运动;假设矢量场局部光滑选择具有相同先验矢量场(m * m像素)的位置n的d距离内的所有像素;最小二乘解m * m方程组以多种分辨率重复;从多个分辨率矢量场计算云运动矢量;将云运动矢量应用于浊度指数图像以预测未来的云位置和强度。

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