EAGER: EPCN: Computational Imaging of the Sky for Precise Prediction of Solar Variability

太阳等可再生能源自然是间歇性的。这种不确定性增加了依赖太阳能的系统和工艺的运营成本，从而阻碍了太阳能光伏发电的广泛采用。这个NSF EAGER项目旨在开发一个系统，用于预测太阳能的分布，时间跨度从几分钟到一小时。该项目将为能源调度和调度系统带来变革，影响到广泛的住宅和商业太阳能生产商和消费者。这将通过对天空成像来监测云的空间分布及其移动来实现。该项目的智力价值包括设计一个天空成像系统，该系统可以获得高分辨率的云层图像，特别是在地平线附近，用于预测云层演变的基于学习的技术，以及根据太阳能光伏测试平台对预测进行评估。该项目的更广泛影响包括为K-12学生举办讲习班以及传播工作，使更广泛的公众能够可视化和利用太阳能预测。为了实现太阳能预测的目标，该项目开发了计算成像以及物理和数据感知建模方面的新工具，用于预测长期天空图像的演变。为此，该研究将建立一个可重新配置的天空成像仪，以减轻视野（或地平线）外围附近分辨率的严重损失。捕获的图像将使我们能够使用物理感知学习来模拟云的动态，该学习考虑了风的流动以及数据驱动的模型。我们将验证我们的系统所做的预测对实际测量与共定位光伏系统。将对涉及多个分布式成像和太阳能单元的设置进行广泛的分析和实验，以评估和增强拟议预测解决方案的可扩展性。该项目开发的核心技术的影响将通过推广和教育工作得到扩大。我们将通过卡内基梅隆大学的既定项目为初中和高中学生组织研讨会。 我们还将通过一个门户网站参与公民科学项目，该门户网站将允许访问我们系统所做的太阳能预测。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Renewable energy sources such as the sun are naturally intermittent. This uncertainty increases the operational cost of systems and processes that are reliant on solar power, thus hindering the widespread adoption of solar photovoltaics. This NSF EAGER project aims to develop a system for predicting the distribution of solar power for time horizons spanning minutes to an hour. The project will bring transformative change to energy dispatch and scheduling systems that affect a broad range of residential and commercial solar producers and consumers. This will be achieved by imaging the sky to monitor the spatial distribution of clouds as well as their movement. The intellectual merits of the project include the design of a sky imaging system that can acquire high-resolution images of clouds especially near the horizon, learning-based techniques for predicting the evolution of clouds, and the evaluation of the prediction against a solar photovoltaic testbed. The broader impacts of the project include workshops for K-12 students as well as dissemination efforts that will allow the broader public to visualize and utilize the solar energy forecasts.To achieve the goal of solar power forecasting, this project develops new tools in computational imaging as well as physics and data-aware modeling for predicting the evolution of the sky image over long-time horizons. To this end, the research will build a reconfigurable sky imager that mitigates the severe loss of resolution near the periphery of the field of view (or the horizon). The captured imagery will allow us to model the dynamics of clouds using physics-aware learning that considers wind flow as well as data-driven models. We will validate the forecasts made by our system against actual measurements made with a co-located photovoltaic system. Extensive analysis and experimentation will be performed with setups involving multiple distributed imaging and solar units to assess and enhance the scalability of the proposed prediction solutions. The impact of the core technology developed in this project will be amplified via outreach and education efforts. We will organize workshops for middle and high-school students through established programs at Carnegie Mellon University. We will also engage in citizen science projects using a web portal that will allow access to the solar energy forecasts made by our system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.