Computational Imaging for Long-Term Prediction of Solar Irradiance

Leron Julian, Haejoon Lee, Soummya Kar, Aswin C. Sankaranarayanan
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Abstract

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.
用于长期预测太阳辐照度的计算成像技术
云层遮挡太阳是太阳能发电不确定性的主要来源之一,也是影响将太阳能作为主要能源广泛使用的一个因素。对云层移动以及由此产生的太阳辐照度进行实时预测,对并网光伏系统的能源调度和分配十分必要。然而,这些图像对出现在地平线附近的云的分辨率较低,这降低了它们对长期预测太阳遮挡的有效性。具体来说,为了能够预测长时间的太阳遮挡,需要检测地平线附近的云层,并精确估计它们的速度。为了实现这样一个系统,我们设计并部署了一个视场内天空空间分辨率一致的广角成像系统。为了能够对更长的时间范围进行预测,我们设计了一种算法,该算法以估计的风向和风速为输入,使用精心选择的图像时空切片。通过射线追踪模拟以及在室外部署的真实测试平台,我们证明该系统能够预测未来数十分钟内的太阳闭塞度和辐照度,这比之前的工作有了数量级的提升。
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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