用空间扭曲提升云图预测精度，改善长时光伏预报。
Precise Forecasting of Sky Images Using Spatial Warping
- 引入最优空间扭曲方法校正地面相机拍摄的云图畸变
- 实现比以往更高分辨率的未来天空图像预测
- 特别提升近地平线区域云运动预测能力，适合光伏调度场景
太阳能发电因云层遮挡导致间歇性，是其在商业和住宅领域广泛应用的主要障碍。因此，对并网光伏系统进行实时太阳辐照度预测，对电网资源调度与分配至关重要。地面广角相机常用于监测特定站点上空云层运动以预测太阳辐照度。然而，这些广角相机捕获的天空图像存在畸变，靠近地平线的区域被严重压缩，影响对近地平线云运动的精确追踪，尤其削弱了长期时间尺度下的预测能力。本文提出一种深度学习方法，通过推导最优空间扭曲策略，缓解地平线处云层带来的负面影响，并构建未来天空图像预测框架，更准确地建模云的演化过程，从而提升长时间序列的预测精度。
原文摘要 · Abstract (English)
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.
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