New Study Improves Solar Forecasting by Accounting for Variations in Cloud Types

Solar energy is a clean, renewable source of energy that can help us build a sustainable future. But predicting how much solar energy would be available at a given time is tricky - especially because clouds play such a significant role in blocking or letting sunlight through.

A study conducted by researchers at Brookhaven National Laboratory, in collaboration with researchers from Stony Brook University, Nanjing University of Information Science and Technology, and National Renewable Energy Laboratory, sheds light on how different cloud types can impact solar forecasting, advancing our ability to predict how much solar energy is available.

Yangang Liu, senior scientist at BNL and former adjunct professor in the School of Marine and Atmospheric Sciences and the Department of Applied Math and Statistics said, "Thanks to the decade-long, high-quality data collected by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program, our study offers a detailed evaluation of solar forecasting models across varied cloud conditions."

Using data from 2001 to 2014, the researchers systematically analyzed how eight distinct cloud types affect solar irradiance predictions. These included cumulus, stratiform clouds, congestus, deep convective clouds, altostratus, altocumulus, cirrostratus/anvil, and cirrus. The study built upon the team's earlier work on physics-informed data-driven models, which integrated cloud-radiation physics to improve solar forecasting accuracy. These models were tested against real-world measurements of solar radiance and cloud types from the ARM South Great Plain (SGP) Central Facility site.

The results showed a clear hierarchy in the models' accuracy based on cloud type. They performed best with weak convective clouds (like cirrus), followed by stratiform clouds, and worst with strong convective clouds, such as deep convective clouds.

Shinjae Yoo, adjunct assistant professor at Stony Brook University and Distinguished Scientist with Brookhaven Lab's Computing and Data Sciences directorate, said, "By categorizing clouds into stratiform, weak, and strong convective types, we were able to identify where our models performed best and where they needed improvement. The trends we saw highlighted the complexity of forecasting under certain cloud conditions. For example, in the case of deep convective clouds - which have more complex spatial structures with dynamic and unpredictable nature - we noticed a significant uncertainty in the results."

Read the full story by Ankita Nagpal at the AI Innovation Institute website.