Building the Sun4Cast System: Improvements in Solar Power Forecasting

Manajit Sengupta, Yu Xie, Sue Haupt, Branko Kosovic, Tara Jensen, Jeffrey Lazo, Jared Lee, Pedro Jimenez, James Cowie, Gerry Wiener, Tyler McCandless, Matthew Rogers, Steven Miller, Laura Hinkelman, Paul Kalb, John Heiser

Research output: Contribution to journalArticlepeer-review

58 Scopus Citations


The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities' customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.
Original languageAmerican English
Pages (from-to)121-135
Number of pages15
JournalBulletin of the American Meteorological Society
Issue number1
StatePublished - Jan 2018

Bibliographical note

Publisher Copyright:
© 2018 American Meteorological Society.

NREL Publication Number

  • NREL/JA-5D00-67622


  • forecasting
  • solar
  • Sun4Cast


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