I-Corps: Global solar irradiance forecasting with flexible prediction time horizons

The broader impact/commercial potential of this I-Corps project is the development of a generalizable approach for accurate and scalable prediction of solar light (irradiance) based on freely available satellite cloud images. High penetration of renewable energy is essential for decarbonizing the grid and decelerating anthropogenic climate change. The intermittency of renewable energy, however, is currently a hurdle in its cost-effective integration, which is only partially solved by improved energy storage.

Improving the accuracy of solar irradiance forecasts is a vital step towards effective utilization and integration of solar energy since it allows utilities and electricity market operators to make informed decisions for scheduling reserve capacity and designing efficient bidding strategies in the wholesale power markets. Moreover, it allows the strategic value of solar deployment to be assessed at various scales.

The proposed technology can benefit utilities, energy market operators, and policymakers by allowing them to make informed decisions in regulating, planning, and operating the grid.

This I-Corps project harnesses the power of big data and the latest developments in deep learning to develop a generalizable Convolutional Global Horizontal Irradiance prediction model. Specifically, the model leverages convolutional neural networks and cloud imagery to accurately predict solar irradiance and has been validated across several U.S. sites.

Rigorous posterior analyses have been conducted to study the model's variable skill as a function of prediction time horizon and cloud cover and to demonstrate the benefits of incorporating mesoscale atmospheric dynamics for enhanced prediction performance. The technology addresses key fundamental gaps in state-of-the-art solar irradiance forecasting technologies as it combines the benefits of physics-based solar irradiance prediction models (with no need for in-situ measurement) with the accuracy and scalability of statistical models.

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.