Precise Forecasting of Sky Images Using Spatial Warping

Leron Julian, Aswin C. Sankaranarayanan
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Abstract

The intermittency of solar power, due to occlusion from cloud cover, is one of the key factors inhibiting its widespread use in both commercial and residential settings. Hence, real-time forecasting of solar irradiance for grid-connected photovoltaic systems is necessary to schedule and allocate resources across the grid. Ground-based imagers that capture wide field-of-view images of the sky are commonly used to monitor cloud movement around a particular site in an effort to forecast solar irradiance. However, these wide FOV imagers capture a distorted image of sky image, where regions near the horizon are heavily compressed. This hinders the ability to precisely predict cloud motion near the horizon which especially affects prediction over longer time horizons. In this work, we combat the aforementioned constraint by introducing a deep learning method to predict a future sky image frame with higher resolution than previous methods. Our main contribution is to derive an optimal warping method to counter the adverse affects of clouds at the horizon, and learn a framework for future sky image prediction which better determines cloud evolution for longer time horizons.
利用空间扭曲技术精确预报天空图像
由于云层遮挡,太阳能发电具有间歇性,这是阻碍其在商业和住宅环境中广泛使用的关键因素之一。因此,有必要对并网光伏系统的太阳辐照度进行实时预测,以便在整个电网中安排和分配资源。捕捉天空宽视场图像的地基成像仪通常用于监测特定地点周围的云层移动,以预测太阳辐照度。然而,这些宽视场成像仪捕捉到的天空图像是扭曲的,地平线附近的区域被严重压缩。这就妨碍了精确预测地平线附近云层运动的能力,尤其影响了对长时间地平线的预测。在这项工作中,我们引入了一种深度学习方法来预测未来天空图像帧,其分辨率比以前的方法更高,从而克服了上述限制。我们的主要贡献是推导出一种最佳的扭曲方法来应对地平线上云层的不利影响,并学习了一种未来天空图像预测框架,该框架能更好地确定更长时间范围内的云层演变情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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