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

可再生能源，如太阳能，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的广泛采用。美国国家科学基金会的这个渴望项目旨在开发一种系统，用于在几分钟到一小时的时间范围内预测太阳能的分布。该项目将给能源调度和调度系统带来革命性的变化，影响到广泛的住宅和商业太阳能生产商和消费者。这将通过成像天空来实现，以监测云的空间分布以及它们的运动。该项目的智力优势包括设计一种天空成像系统，可以获取尤其是地平线附近的云的高分辨率图像，预测云的演变的基于学习的技术，以及在太阳能光伏试验台上对预测进行评估。该项目的更广泛影响包括为K-12学生举办研讨会以及传播工作，使更广泛的公众能够可视化和使用太阳能预测。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，用于预测天空图像在长期地平线上的演变。为此，这项研究将建造一种可重新配置的天空成像仪，以缓解视场(或地平线)边缘附近分辨率的严重丧失。捕获的图像将允许我们使用考虑风流和数据驱动模型的物理感知学习来对云的动态进行建模。我们将验证我们的系统所作的预测，并与一地两检的光伏系统的实际测量结果进行比较。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，以评估和提高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外联和教育努力得到扩大。我们将通过卡内基梅隆大学现有的项目，为初中生和高中生组织研讨会。我们还将使用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做出的太阳能预测。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.