用全景成像系统精准预测太阳辐照度，提前数十分钟预报云遮挡。
Computational Imaging for Long-Term Prediction of Solar Irradiance
- 采用特殊光学设计实现全天空均匀分辨率成像，提升近地平线云层检测能力。
- 结合风向风速估计，通过时空切片算法实现数十分钟的太阳辐照度预测。
- 实测与仿真验证，预测时效比以往提升一个数量级，适合光伏电网调度使用。
云层遮挡太阳是太阳能发电不确定性的主要来源，影响其作为主要能源的大规模应用。实时预测云移动及太阳辐照度，对并网光伏系统的能量调度至关重要。以往研究依赖广角天空影像监测云运动，但此类图像在地平线附近分辨率低，难以支持长期预测。为实现长时预测，需精确探测近地平线云层并估计其速度。为此，本文设计并部署了一套猫眼式（catadioptric）成像系统，提供全域均匀空间分辨率的天空影像。为进一步延长预测时间，提出一种算法，利用基于估算风向风速的时空切片数据进行预测。通过光线追踪仿真及户外实测平台验证，该系统可实现数十分钟后的太阳遮蔽与辐照度预测，较先前工作提升一个数量级。
原文摘要 · Abstract (English)
The occlusion of the sun by clouds is one of the primary sources of uncertainties in solar power generation, and is a factor that affects the wide-spread use of solar power as a primary energy source. Real-time forecasting of cloud movement and, as a result, solar irradiance is necessary to schedule and allocate energy across grid-connected photovoltaic systems. Previous works monitored cloud movement using wide-angle field of view imagery of the sky. However, such images have poor resolution for clouds that appear near the horizon, which reduces their effectiveness for long term prediction of solar occlusion. Specifically, to be able to predict occlusion of the sun over long time periods, clouds that are near the horizon need to be detected, and their velocities estimated precisely. To enable such a system, we design and deploy a catadioptric system that delivers wide-angle imagery with uniform spatial resolution of the sky over its field of view. To enable prediction over a longer time horizon, we design an algorithm that uses carefully selected spatio-temporal slices of the imagery using estimated wind direction and velocity as inputs. Using ray-tracing simulations as well as a real testbed deployed outdoors, we show that the system is capable of predicting solar occlusion as well as irradiance for tens of minutes in the future, which is an order of magnitude improvement over prior work.
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