Project Name: Advancing the WRF-Solar Model to Improve Solar Irradiance Forecast in Cloudy Environments
Funding Opportunity: Solar Forecasting 2
SETO Subprogram: Systems Integration
Location: Upton, NY
SETO Award Amount: $1,600,000
Awardee Cost Share: $214,195
Principal Investigator: Yangang Liu
The overall goal of this project is to improve the widely-used community Weather Forecasting and Research model’s (WRF-Solar) ability to determine the amount of sunlight that would have a negative impact on a solar array in cloudy environments. The improved model will take into account cloud properties such as the concentration and size of cloud droplets and their interactions with solar radiation. The improved forecasting model will enhance the ability of solar energy producers and users to better manage energy loads on grid systems.
This project will improve the WRF-Solar model using an iterative and interactive approach of model development and evaluation. The improved model will set parameters to include physical processes like the transfer of circumsolar radiation—light that originates from the region around the sun—through clouds, the entrainment of environmental dry air mixing into clouds, and the distribution of droplet sizes within a cloud. The project will explore the use of machine learning and analyze real-time data streams to advance the project.
The research resulting from this project is expected to improve the skill of forecasting solar radiation in cloudy conditions. The development of process parameters will help to fill crucial gaps in the current WRF-Solar model, and thus improve the ability to forecast solar energy in cloudy environments. The resulting analysis package will capitalize on advancements in big data science and machine learning and integrate physical modeling, helping to transform the overall development of solar forecasting models.