DOE Announces Winners of Solar Forecasting Prize

The DOE has announced the winners of the American-Made Solar Forecasting Prize. The five winning teams, who will each receive $50,000, had the best-performing forecasting models and strongest plans to accelerate the adoption of probabilistic forecasts. The two runners-up will receive $25,000 each.

The Solar Forecasting Prize is designed to incentivize the development of state-of-the-art solar forecasting capabilities to assist grid operators in predicting how much solar power will be produced while considering weather-related uncertainties such as cloud coverage. Winners from DOE EPSCoR jurisdictions include:

• Nimbus, AI (Honolulu, HAWAI‘I): Fast Solar Forecasting with Machine Learning This team developed a fast and inexpensive system for geographically flexible, hyper-local day-ahead probabilistic solar forecasting. The team combined historical ground- and satellite-based instrument data with physics-based numerical weather prediction (NWP) techniques to produce probabilistic forecasts.
• Northview Weather (Danville, VERMONT): Determining Who Wins the Cumulus vs. Stratus Battle This team developed a solar forecasting method that uses a mesoscale weather forecast model to produce a dynamically based spread of probabilistic solar power forecast information. Probabilistic information is also tuned blending statistical information and machine learning of historic sky cover observations.

Runners-up from DOE EPSCoR jurisdictions include:

• Matt Motoki (Aiea, HAWAI‘I): RadianceIQ This team developed an advanced machine learning technique for probabilistic forecasting that utilizes custom deep neural networks to directly minimize the Continuous Ranked Probability Score loss with no post-processing calibration needed. This method allowed for greater accuracy and its lightweight architecture allows it to run faster than NWP ensembles and other machine learning approaches.